Doctor Penguin

General

Application of Artificial Intelligence in COVID-19 drug repurposing.

In Diabetes & metabolic syndrome

BACKGROUND AND AIM : COVID-19 outbreak has created havoc and a quick cure for the disease will be a therapeutic medicine that has usage history in patients to resolve the current pandemic. With technological advancements in Artificial Intelligence (AI) coupled with increased computational power, the AI-empowered drug repurposing can prove beneficial in the COVID-19 scenario.

METHODS : The recent literature is studied and analyzed from various sources such as Scopus, Google Scholar, PubMed, and IEEE Xplore databases. The search terms used are 'COVID-19', ' AI ', and 'Drug Repurposing'.

RESULTS : AI is implemented in the field design through the generation of the learning-prediction model and performs a quick virtual screening to accurately display the output. With a drug-repositioning strategy, AI can quickly detect drugs that can fight against emerging diseases such as COVID-19. This technology has the potential to improve the drug discovery, planning, treatment, and reported outcomes of the COVID-19 patient, being an evidence-based medical tool.

CONCLUSIONS : Thus, there are chances that the application of the AI approach in drug discovery is feasible. With prior usage experiences in patients, few of the old drugs, if shown active against SARS-CoV-2, can be readily applied to treat the COVID-19 patients. With the collaboration of AI with pharmacology, the efficiency of drug repurposing can improve significantly.

Mohanty Sweta, Harun Ai Rashid Md, Mridul Mayank, Mohanty Chandana, Swayamsiddha Swati

2020-Jul-03

Artificial intelligence, COVID-19, Coronavirus, Deep learning, Drug repositioning, Drug repurposing, Machine learning

General

Update on therapeutic approaches and emerging therapies for SARS-CoV-2 virus.

In European journal of pharmacology ; h5-index 57.0
The global pandemic of coronavirus disease 2019 (COVID-19), caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in over 7,273,958 cases with almost over 413,372 deaths worldwide as per the WHO situational report 143 on COVID-19. There are no known treatment regimens with proven efficacy and vaccines thus far, posing an unprecedented challenge to identify effective drugs and vaccines for prevention and treatment. The urgency for its prevention and cure has resulted in an increased number of proposed treatment options. The high rate and volume of emerging clinical trials on therapies for COVID-19 need to be compared and evaluated to provide scientific evidence for effective medical options. Other emerging non-conventional drug discovery techniques such as bioinformatics and cheminformatics, structure-based drug design, network-based methods for prediction of drug-target interactions, artificial intelligence (AI) and machine learning (ML) and phage technique could provide alternative routes to discovering potent Anti-SARS-CoV2 drugs. While drugs are being repurposed and discovered for COVID-19, novel drug delivery systems will be paramount for efficient delivery and avoidance of possible drug resistance. This review describes the proposed drug targets for therapy, and outcomes of clinical trials that have been reported. It also identifies the adopted treatment modalities that are showing promise, and those that have failed as drug candidates. It further highlights various emerging therapies and future strategies for the treatment of COVID-19 and delivery of Anti-SARS-CoV2 drugs.
Omolo Calvin A, Soni Nikki, Fasiku Victoria Oluwaseun, Mackraj Irene, Govender Thirumala

2020-Jul-04

COVID-19, Clinical trials, Drug targets, Re-purposing, SARS-CoV2, Vaccines

General

Artificial intelligence and COVID-19: A multidisciplinary approach.

In Integrative medicine research ; h5-index 20.0
The COVID-19 pandemic is taking a colossal toll in human suffering and lives. A significant amount of new scientific research and data sharing is underway due to the pandemic which is still rapidly spreading. There is now a growing amount of coronavirus related datasets as well as published papers that must be leveraged along with artificial intelligence (AI) to fight this pandemic by driving news approaches to drug discovery, vaccine development, and public awareness. AI can be used to mine this avalanche of new data and papers to extract new insights by cross-referencing papers and searching for patterns that AI algorithms could help discover new possible treatments or help in vaccine development. Drug discovery is not a trivial task and AI technologies like deep learning can help accelerate this process by helping predict which existing drugs, or brand-new drug-like molecules could treat COVID-19. AI techniques can also help disseminate vital information across the globe and reduce the spread of false information about COVID-19. The positive power and potential of AI must be harnessed in the fight to slow the spread of COVID-19 in order to save lives and limit the economic havoc due to this horrific disease.
Ahuja Abhimanyu S, Reddy Vineet Pasam, Marques Oge

2020-Sep

Artificial intelligence, COVID-19, Drug Discovery, Integrative medicine, Vaccine development

Public Health

The Correlation of Comorbidities on the Mortality in Patients with COVID-19: an Observational Study Based on the Korean National Health Insurance Big Data.

In Journal of Korean medical science

BACKGROUND : Mortality of coronavirus disease 2019 (COVID-19) is a major concern for quarantine departments in all countries. This is because the mortality of infectious diseases determines the basic policy stance of measures to prevent infectious diseases. Early screening of high-risk groups and taking action are the basics of disease management. This study examined the correlation of comorbidities on the mortality of patients with COVID-19.

METHODS : We constructed epidemiologic characteristics and medical history database based on the Korean National Health Insurance Service Big Data and linked COVID-19 registry data of Korea Centers for Disease Control & Prevention (KCDC) for this emergent observational cohort study. A total of 9,148 patients with confirmed COVID-19 were included. Mortalities by sex, age, district, income level and all range of comorbidities classified by International Classification of Diseases-10 based 298 categories were estimated.

RESULTS : There were 3,556 male confirmed cases, 67 deaths, and crude death rate (CDR) of 1.88%. There were 5,592 females, 63 deaths, and CDR of 1.13%. The most confirmed cases were 1,352 patients between the ages of 20 to 24, followed by 25 to 29. As a result of multivariate logistic regression analysis that adjusted epidemiologic factors to view the risk of death, the odds ratio of death would be hemorrhagic conditions and other diseases of blood and blood-forming organs 3.88-fold (95% confidence interval [CI], 1.52-9.88), heart failure 3.17-fold (95% CI, 1.88-5.34), renal failure 3.07-fold (95% CI, 1.43-6.61), prostate malignant neoplasm 2.88-fold (95% CI, 1.01-8.22), acute myocardial infarction 2.38-fold (95% CI, 1.03-5.49), diabetes was 1.82-fold (95% CI, 1.25-2.67), and other ischemic heart disease 1.71-fold (95% CI, 1.09-2.66).

CONCLUSION : We hope that this study could provide information on high risk groups for preemptive interventions. In the future, if a vaccine for COVID-19 is developed, it is expected that this study will be the basic data for recommending immunization by selecting those with chronic disease that had high risk of death, as recommended target diseases for vaccination.

Kim Dong Wook, Byeon Kyeong Hyang, Kim Jaiyong, Cho Kyu Dong, Lee Nakyoung

2020-Jul-06

COVID-19, Chronic Diseases, Comorbidities, Mortality Risk

Public Health

Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia.

In International journal of medical sciences
Background: As 2019 ends coronavirus disease start expanding all over the world. It is highly transmissible disease that can affect respiratory tract and can leads to organ failure. In 2020 it is declared by world health organization as "Public health emergency of international concerns". The current situation of Covid-19 and chest related diseases have already gone through radical change with the advancements of image processing tools. There is no effective method which can accurately identify all chest related diseases and tackle the multiple class problems with reliable results. Method: There are many potentially impactful applications of Deep Learning to fighting the Covid-19 from Chest X-Ray/CT Images, however, most are still in their early stages due to lack of data sharing as it continues to inhibit overall progress in a variety of medical research problems. Based on COVID-19 radiographical changes in CT images, this work aims to detect the possibility of COVID-19 in the patient. This work provides a significant contribution in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results. Results: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides effective analysis of chest related diseases with respect to age and gender. Our model achieves 89% accuracy in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results. Conclusion: If the gap in identifying of all viral pneumonias is not filled with effective automation of chest disease detection the healthcare industry may have to bear unfavorable circumstances.
Albahli Saleh

2020

Chest diseases, Coronavirus, Deep learning, Inception-V3, ResNet-152, X-ray

Public Health

Shape-based Machine Learning Models for the potential Novel COVID-19 protease inhibitors assisted by Molecular Dynamics Simulation.

In Current topics in medicinal chemistry ; h5-index 40.0

BACKGROUND : The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases has overwhelmed health and public health services. AI and ML algorithms have extended its major role in tracking the disease patterns, and in identifying possible treatment of disease.

OBJECTIVE : To identify potential COVID-19 protease inhibitor through shape-based Machine Learning assisted by Molecular docking and Molecular Dynamics simulation.

METHODS : 31 repurposed compounds have been selected targeting coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from 3D shape to pharmacophoric features of its seed compound. Ligand-Receptor docking was performed with Optimized Potential for Liquid Simulations (OPLS3) algorithms to identify high-affinity compounds from the list of selected candidates for 6LU7. This compound was subjected to Molecular Dynamic Simulations followed by ADMET studies and other analysis.

RESULTS : Shape-based Machine learning reported Remdesivir, Valrubicin, Aprepitant, and Fulvestrant and a novel therapeutic compound as the best therapeutic agents with the highest affinity for its target protein. Among the best shape-based compounds, the novel theoretical compound was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorvEMBS'. Further, toxicity analysis showed nCorv-EMBS to be efficacious that can be qualified as a 6LU7 inhibitor in COVID-19.

CONCLUSION : An effective ACE-II, GAK, AAK1, and protease 3C blockers that can serve a novel therapeutic approach to block the binding and attachment of COVID-19 protein (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 Lung cells. The novel theoretical compound nCorv-EMBS herein proposed stands as a promising inhibitor that can be extended for entering phases of clinical trials for COVID-19 treatment.

Khandelwal Ravina, Nayarisseri Anuraj, Madhavi Maddala, Selvaraj Chandrabose, Panwar Umesh, Sharma Khushboo, Hussain Tajamul, Singh Sanjeev Kumar

2020-Jul-04

COVID-19, COVID-19 protease inhibitors, Machine Learning, Molecular\nDynamics Simulation, Molecular Docking, Remdesivir, Shape-based ML, nCorv-EMBS.

Pathology

Digital pathology and artificial intelligence will be key to supporting clinical and academic cellular pathology through COVID-19 and future crises: the PathLAKE consortium perspective.

In Journal of clinical pathology
The measures to control the COVID-19 outbreak will likely remain a feature of our working lives until a suitable vaccine or treatment is found. The pandemic has had a substantial impact on clinical services, including cancer pathways. Pathologists are working remotely in many circumstances to protect themselves, colleagues, family members and the delivery of clinical services. The effects of COVID-19 on research and clinical trials have also been significant with changes to protocols, suspensions of studies and redeployment of resources to COVID-19. In this article, we explore the specific impact of COVID-19 on clinical and academic pathology and explore how digital pathology and artificial intelligence can play a key role to safeguarding clinical services and pathology-based research in the current climate and in the future.
Browning Lisa, Colling Richard, Rakha Emad, Rajpoot Nasir, Rittscher Jens, James Jacqueline A, Salto-Tellez Manuel, Snead David R J, Verrill Clare

2020-Jul-03

computer systems, image processing, computer-assisted, pathology, surgical

General

Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning.

In Journal of biomolecular structure & dynamics
Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (-). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches.Communicated by Ramaswamy H. Sarma.
Jaiswal Aayush, Gianchandani Neha, Singh Dilbag, Kumar Vijay, Kaur Manjit

2020-Jul-03

COVID-19, classification, deep learning, deep transfer learning

Surgery

Using Machine Learning to Estimate Unobserved COVID-19 Infections in North America.

In The Journal of bone and joint surgery. American volume

BACKGROUND : The detection of coronavirus disease 2019 (COVID-19) cases remains a huge challenge. As of April 22, 2020, the COVID-19 pandemic continues to take its toll, with >2.6 million confirmed infections and >183,000 deaths. Dire projections are surfacing almost every day, and policymakers worldwide are using projections for critical decisions. Given this background, we modeled unobserved infections to examine the extent to which we might be grossly underestimating COVID-19 infections in North America.

METHODS : We developed a machine-learning model to uncover hidden patterns based on reported cases and to predict potential infections. First, our model relied on dimensionality reduction to identify parameters that were key to uncovering hidden patterns. Next, our predictive analysis used an unbiased hierarchical Bayesian estimator approach to infer past infections from current fatalities.

RESULTS : Our analysis indicates that, when we assumed a 13-day lag time from infection to death, the United States, as of April 22, 2020, likely had at least 1.3 million undetected infections. With a longer lag time-for example, 23 days-there could have been at least 1.7 million undetected infections. Given these assumptions, the number of undetected infections in Canada could have ranged from 60,000 to 80,000. Duarte's elegant unbiased estimator approach suggested that, as of April 22, 2020, the United States had up to >1.6 million undetected infections and Canada had at least 60,000 to 86,000 undetected infections. However, the Johns Hopkins University Center for Systems Science and Engineering data feed on April 22, 2020, reported only 840,476 and 41,650 confirmed cases for the United States and Canada, respectively.

CONCLUSIONS : We have identified 2 key findings: (1) as of April 22, 2020, the United States may have had 1.5 to 2.029 times the number of reported infections and Canada may have had 1.44 to 2.06 times the number of reported infections and (2) even if we assume that the fatality and growth rates in the unobservable population (undetected infections) are similar to those in the observable population (confirmed infections), the number of undetected infections may be within ranges similar to those described above. In summary, 2 different approaches indicated similar ranges of undetected infections in North America.

LEVEL OF EVIDENCE : Prognostic Level V. See Instructions for Authors for a complete description of levels of evidence.

Vaid Shashank, Cakan Caglar, Bhandari Mohit

2020-Jul-01

General

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.

In European radiology ; h5-index 62.0

OBJECTIVES : To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents.

METHODS : A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score.

RESULTS : Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm³. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance.

CONCLUSIONS : The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists.

KEY POINTS : • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.

Ni Qianqian, Sun Zhi Yuan, Qi Li, Chen Wen, Yang Yi, Wang Li, Zhang Xinyuan, Yang Liu, Fang Yi, Xing Zijian, Zhou Zhen, Yu Yizhou, Lu Guang Ming, Zhang Long Jiang

2020-Jul-02

COVID-19, Deep learning, Diagnosis, Multidetector computed tomography, Pneumonia

Public Health

COVID-19: A master stroke of Nature.

In AIMS public health
This article presents the status of countries affected by COVID-19 (as of mid-May 2020) and their preparedness to combat the after-effects of the pandemic. The report also provides an analysis of how human behavior may have triggered such a global pandemic and why humans need to consider living sustainably to make our future world livable for all. COVID-19 originated in the city of Wuhan, China in December 2019. As of mid-May, it has spread to 213 countries and territories worldwide. The World Health Organization has declared COVID-19 a global pandemic, with a death toll of over 300,000 to date. The U.S. is currently the most impacted country. Collaborative efforts of scientists and politicians across the world will be needed to better plan and utilize global health resources to combat this global pandemic. Machine learning-based prediction models could also help by identifying potential COVID-19-prone areas and individuals. The cause of the emergence of COVID-19 is still a matter of research; however, one consistent theme is humanity's unsustainable behavior. By sustainably interacting with nature, humans may have avoided this pandemic. If unsustainable human practices are not controlled through education, awareness, behavioral change, as well as sustainable policy creation and enforcement, there could be several such pandemics in our future.
Singh Sushant K

2020

COVID-19, Nature, coronavirus, pandemic, public health, sustainability

Public Health

Coronavirus disease 2019 (COVID-19): an evidence map of medical literature.

In BMC medical research methodology

BACKGROUND : Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of June 2020, gaps and longitudinal trends in the COVID-19 medical literature remain unidentified, despite potential benefits for research prioritisation and policy setting in both the COVID-19 pandemic and future large-scale public health crises.

METHODS : In this paper, we searched PubMed and Embase for medical literature on COVID-19 between 1 January and 24 March 2020. We characterised the growth of the early COVID-19 medical literature using evidence maps and bibliometric analyses to elicit cross-sectional and longitudinal trends and systematically identify gaps.

RESULTS : The early COVID-19 medical literature originated primarily from Asia and focused mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health, the use of novel technologies and artificial intelligence, pathophysiology of COVID-19 within different body systems, and indirect effects of COVID-19 on the care of non-COVID-19 patients. Few articles involved research collaboration at the international level (24.7%). The median submission-to-publication duration was 8 days (interquartile range: 4-16).

CONCLUSIONS : Although in its early phase, COVID-19 research has generated a large volume of publications. However, there are still knowledge gaps yet to be filled and areas for improvement for the global research community. Our analysis of early COVID-19 research may be valuable in informing research prioritisation and policy planning both in the current COVID-19 pandemic and similar global health crises.

Liu Nan, Chee Marcel Lucas, Niu Chenglin, Pek Pin Pin, Siddiqui Fahad Javaid, Ansah John Pastor, Matchar David Bruce, Lam Sean Shao Wei, Abdullah Hairil Rizal, Chan Angelique, Malhotra Rahul, Graves Nicholas, Koh Mariko Siyue, Yoon Sungwon, Ho Andrew Fu Wah, Ting Daniel Shu Wei, Low Jenny Guek Hong, Ong Marcus Eng Hock

2020-Jul-02

COVID-19, Coronavirus, Evidence gap map, Review, SARS-CoV-2

Radiology

Chest CT evaluation of 11 persistent asymptomatic patients with SARS-CoV-2 infection.

In Japanese journal of infectious diseases
Eleven asymptomatic carriers who received nasal or throat swab test for SARS-CoV-2 after close contacts with patients who developed symptomatic 2019 coronavirus disease (COVID-19) were enrolled in this study. The chest CT images of enrolled patients were analyzed qualitatively and quantitatively. There were 3 (27.3%) patients had normal first chest CT, two of which were under age of 15 years. Lesions in 2 (18.2%) patients involved one lobe with unifocal presence. Subpleural lesions were seen in 7 (63.6%) patients. Ground glass opacity (GGO) was the most common sign observed in 7 (63.6%) patients. Crazy-paving pattern and consolidation were detected in 2 (18.2%) and 4 (36.4%) cases, respectively. Based on deep learning quantitative analysis, volume of intrapulmonary lesions on first CT scans was 85.73±84.46 cm³. In patients with positive findings on CT images, average interval days between positive real-time reverse transcriptase polymerase chain reaction assay and peak volume on CT images were 5.1±3.1 days. In conclusion, typical CT findings can be detected in over 70% of asymptomatic SARS-CoV-2 carriers. It mainly starts as GGO along subpleural regions and bronchi, and absorbs in nearly 5 days.
Yan Shuo, Chen Hui, Xie Ru-Ming, Guan Chun-Shuang, Xue Ming, Lv Zhi-Bin, Wei Lian-Gui, Bai Yan, Chen Bu-Dong

2020-Jun-30

asymptomatic, coronavirus, multidetector computed tomography, pneumonia

Ophthalmology

COVID-19 pandemic from an ophthalmology point of view.

In The Indian journal of medical research
Coronavirus disease 2019 (COVID-19) is caused by a highly contagious RNA virus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Ophthalmologists are at high-risk due to their proximity and short working distance at the time of slit-lamp examination. Eye care professionals can be caught unaware because conjunctivitis may be one of the first signs of COVID-19 at presentation, even precluding the emergence of additional symptoms such as dry cough and anosmia. Breath and eye shields as well as N95 masks, should be worn while examining patients with fever, breathlessness, or any history of international travel or travel from any hotspot besides maintaining hand hygiene. All elective surgeries need to be deferred. Adults or children with sudden-onset painful or painless visual loss, or sudden-onset squint, or sudden-onset floaters or severe lid oedema need a referral for urgent care. Patients should be told to discontinue contact lens wear if they have any symptoms of COVID-19. Cornea retrieval should be avoided in confirmed cases and suspects, and long-term preservation medium for storage of corneas should be encouraged. Retinal screening is unnecessary for coronavirus patients taking chloroquine or hydroxychloroquine as the probability of toxic damage to the retina is less due to short-duration of drug therapy. Tele-ophthalmology and artificial intelligence should be preferred for increasing doctor-patient interaction.
Gupta Parul Chawla, Kumar M Praveen, Ram Jagat

2020-May

Chloroquine - contact lens - coronavirus - eye donation - eye shields - hydroxychloroquine - ophthalmologist

General

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study.

In Journal of medical systems ; h5-index 48.0
The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost 200,000 deaths. Amplification of viral RNA by (real time) reverse transcription polymerase chain reaction (rRT-PCR) is the current gold standard test for confirmation of infection, although it presents known shortcomings: long turnaround times (3-4 hours to generate results), potential shortage of reagents, false-negative rates as large as 15-20%, the need for certified laboratories, expensive equipment and trained personnel. Thus there is a need for alternative, faster, less expensive and more accessible tests. We developed two machine learning classification models using hematochemical values from routine blood exams (namely: white blood cells counts, and the platelets, CRP, AST, ALT, GGT, ALP, LDH plasma levels) drawn from 279 patients who, after being admitted to the San Raffaele Hospital (Milan, Italy) emergency-room with COVID-19 symptoms, were screened with the rRT-PCR test performed on respiratory tract specimens. Of these patients, 177 resulted positive, whereas 102 received a negative response. We have developed two machine learning models, to discriminate between patients who are either positive or negative to the SARS-CoV-2: their accuracy ranges between 82% and 86%, and sensitivity between 92% e 95%, so comparably well with respect to the gold standard. We also developed an interpretable Decision Tree model as a simple decision aid for clinician interpreting blood tests (even off-line) for COVID-19 suspect cases. This study demonstrated the feasibility and clinical soundness of using blood tests analysis and machine learning as an alternative to rRT-PCR for identifying COVID-19 positive patients. This is especially useful in those countries, like developing ones, suffering from shortages of rRT-PCR reagents and specialized laboratories. We made available a Web-based tool for clinical reference and evaluation (This tool is available at https://covid19-blood-ml.herokuapp.com/ ).
Brinati Davide, Campagner Andrea, Ferrari Davide, Locatelli Massimo, Banfi Giuseppe, Cabitza Federico

2020-Jul-01

Blood tests, COVID-19, Machine learning, RT-PCR test, Random forest, Three-way

General

Setting up an Easy-to-Use Machine Learning Pipeline for Medical Decision Support: A Case Study for COVID-19 Diagnosis Based on Deep Learning with CT Scans.

In Studies in health technology and informatics ; h5-index 23.0
Coronavirus disease (COVID-19) constitutes an ongoing global health problem with significant morbidity and mortality. It usually presents characteristic findings on a chest CT scan, which may lead to early detection of the disease. A timely and accurate diagnosis of COVID-19 is the cornerstone for the prompt management of the patients. The aim of the present study was to evaluate the performance of an automated machine learning algorithm in the diagnosis of Covid-19 pneumonia using chest CT scans. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, and positive predictive value. The method's average precision was 0.932. We suggest that auto-ML platforms help users with limited ML expertise train image recognition models by only uploading the examined dataset and performing some basic settings. Such methods could deliver significant potential benefits for patients in the future by allowing for earlier disease detection and care.
Sakagianni Aikaterini, Feretzakis Georgios, Kalles Dimitris, Koufopoulou Christina, Kaldis Vasileios

2020-Jun-26

Artificial intelligence, AutoML Vision, COVID-19, automated machine learning, chest CT scan, coronavirus disease, image classification

General

Unsupervised Machine Learning for the Discovery of Latent Clusters in COVID-19 Patients Using Electronic Health Records.

In Studies in health technology and informatics ; h5-index 23.0
The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent clusters in COVID-19 patients. Over 6,000 adult patients tested positive for the SARS-CoV-2 infection at the Mount Sinai Health System in New York, USA met the inclusion criteria for analysis. Patients' diagnoses were mapped onto chronicity and one of the 18 body systems, and the optimal number of clusters was determined using K-means algorithm and the elbow method. 4 clusters were identified; the most frequently associated comorbidities involved infectious, respiratory, cardiovascular, endocrine, and genitourinary disorders, as well as socioeconomic factors that influence health status and contact with health services. These results offer a strong direction for future research and more granular analysis.
Cui Wanting, Robins Daniel, Finkelstein Joseph

2020-Jun-26

Big Data Analytics, Unsupervised Machine Learning

Ophthalmology

Tele-Neuro-Ophthalmology: Vision for 20/20 and Beyond.

In Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society

BACKGROUND : Telehealth provides health care to a patient from a provider at a distant location. Prior to the COVID-19 pandemic adoption of telehealth modalities was increasing slowly but steadily. During the public health emergency rapid widespread telehealth implementation has been encouraged to promote patient and provider safety and preserve access to health care.

EVIDENCE ACQUISITION : Evidence was acquired from English language Internet-searches of medical and business literature and following breaking news on the COVID-19 pandemic and responses from health care stakeholders including policy makers, payers, physicians and health care organizations, and patients. We also had extensive discussions with colleagues who are developing telehealth techniques relevant to neuro-ophthalmology.

RESULTS : Regulatory, legal, reimbursement and cultural barriers impeded the widespread adoption of telehealth prior to the COVID-19 pandemic. With the increased use of telehealth in response to the public health emergency, we are rapidly accumulating experience and an evidence base identifying opportunities and challenges related to the widespread adoption of tele-neuro-ophthalmology. One of the major challenges is the current inability to adequately perform funduscopy remotely.

CONCLUSIONS : Telehealth is an increasingly recognized means of healthcare delivery. Tele-neuro-ophthalmology adoption is necessary for the sake of our patients, the survival of our subspecialty, and the education of our trainees and students. Telehealth does not supplant but supplements and complements in-person neuro-ophthalmologic care. Innovations in digital optical fundus photography, mobile vision testing applications, artificial intelligence and principles of channel management will facilitate further adoption of tele-neuro-ophthalmology and bring the specialty to the leading edge of healthcare delivery.

Ko Melissa, Busis Neil A

2020-Jun-26

General

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Coronavirus disease (COVID-19) is an infectious disease caused by a new virus never identified before in humans. This virus causes respiratory disease (for instance, flu) with symptoms such as cough, fever and, in severe cases, pneumonia. The test to detect the presence of this virus in humans is performed on sputum or blood samples and the outcome is generally available within a few hours or, at most, days. Analysing biomedical imaging the patient shows signs of pneumonia. In this paper, with the aim of providing a fully automatic and faster diagnosis, we propose the adoption of deep learning for COVID-19 detection from X-rays.

METHOD : In particular, we propose an approach composed by three phases: the first one to detect if in a chest X-ray there is the presence of a pneumonia. The second one to discern between COVID-19 and pneumonia. The last step is aimed to localise the areas in the X-ray symptomatic of the COVID-19 presence.

RESULTS AND CONCLUSION : Experimental analysis on 6,523 chest X-rays belonging to different institutions demonstrated the effectiveness of the proposed approach, with an average time for COVID-19 detection of approximately 2.5 seconds and an average accuracy equal to 0.97.

Brunese Luca, Mercaldo Francesco, Reginelli Alfonso, Santone Antonella

2020-Jun-20

Artificial intelligence, COVID-19, Chest, Coronavirus, Deep learning, Transfer learning

Public Health

[Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19].

In Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
The outbreak of pneumonia caused by novel coronavirus (COVID-19) at the end of 2019 was a major public health emergency in human history. In a short period of time, Chinese medical workers have experienced the gradual understanding, evidence accumulation and clinical practice of the unknown virus. So far, National Health Commission of the People's Republic of China has issued seven trial versions of the "Guidelines for the Diagnosis and Treatment of COVID-19". However, it is difficult for clinicians and laymen to quickly and accurately distinguish the similarities and differences among the different versions and locate the key points of the new version. This paper reports a computer-aided intelligent analysis method based on machine learning, which can automatically analyze the similarities and differences of different treatment plans, present the focus of the new version to doctors, reduce the difficulty in interpreting the "diagnosis and treatment plan" for the professional, and help the general public better understand the professional knowledge of medicine. Experimental results show that this method can achieve the topic prediction and matching of the new version of the program text through unsupervised learning of the previous versions of the program topic with an accuracy of 100%. It enables the computer interpretation of "diagnosis and treatment plan" automatically and intelligently.
Pu Xiaorong, Chen Kecheng, Liu Junchi, Wen Jin, Zhneng Shangwei, Li Honghao

2020-Jun-25

COVID-19, artificial intelligence, biomedical engineering, diagnosis and treatment plan, machine learning, novel coronavirus

General

Effect of Underlying Comorbidities on the Infection and Severity of COVID-19 in Korea: a Nationwide Case-Control Study.

In Journal of Korean medical science

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic is an emerging threat worldwide. It remains unclear how comorbidities affect the risk of infection and severity of COVID-19.

METHODS : This is a nationwide retrospective case-control study of 219,961 individuals, aged 18 years or older, whose medical costs for COVID-19 testing were claimed until May 15, 2020. COVID-19 diagnosis and infection severity were identified from reimbursement data using diagnosis codes and on the basis of respiratory support use, respectively. Odds ratios (ORs) were estimated using multiple logistic regression, after adjusting for age, sex, region, healthcare utilization, and insurance status.

RESULTS : The COVID-19 group (7,341 of 219,961) was young and had a high proportion of female. Overall, 13.0% (954 of 7,341) of the cases were severe. The severe COVID-19 group had older patients and a proportion of male ratio than did the non-severe group. Diabetes (odds ratio range [ORR], 1.206-1.254), osteoporosis (ORR, 1.128-1.157), rheumatoid arthritis (ORR, 1.207-1.244), substance use (ORR, 1.321-1.381), and schizophrenia (ORR, 1.614-1.721) showed significant association with COVID-19. In terms of severity, diabetes (OR, 1.247; 95% confidential interval, 1.009-1.543), hypertension (ORR, 1.245-1.317), chronic lower respiratory disease (ORR, 1.216-1.233), chronic renal failure, and end-stage renal disease (ORR, 2.052-2.178) were associated with severe COVID-19.

CONCLUSION : We identified several comorbidities associated with COVID-19. Health care workers should be more careful while diagnosing and treating COVID-19 when patients have the abovementioned comorbidities.

Ji Wonjun, Huh Kyungmin, Kang Minsun, Hong Jinwook, Bae Gi Hwan, Lee Rugyeom, Na Yewon, Choi Hyoseon, Gong Seon Yeong, Choi Yoon Hyeong, Ko Kwang Pil, Im Jeong Soo, Jung Jaehun

2020-Jun-29

COVID-19, Comorbidity, Risk Factor, SARS-CoV-2, Severity

Internal Medicine

Compliance of Antihypertensive Medication and Risk of Coronavirus Disease 2019: a Cohort Study Using Big Data from the Korean National Health Insurance Service.

In Journal of Korean medical science

BACKGROUND : There is a controversy whether it is safe to continue renin-angiotensin system blockers in patients with coronavirus disease 2019 (COVID-19). We analyzed big data to investigate whether angiotensin-converting enzyme inhibitors and/or angiotensin II receptor blockers have any significant effect on the risk of COVID-19. Population-based cohort study was conducted based on the prescription data from nationwide health insurance records.

METHODS : We investigated the 1,374,381 residents aged ≥ 40 years living in Daegu, the epicenter of the COVID-19 outbreak, between February and March 2020. Prescriptions of antihypertensive medication during the year before the outbreak were extracted from the National Health Insurance Service registry. Medications were categorized by types and stratified by the medication possession ratios (MPRs) of antihypertensive medications after controlling for the potential confounders. The risk of COVID-19 was estimated using a difference in difference analysis.

RESULTS : Females, older individuals, low-income earners, and recently hospitalized patients had a higher risk of infection. Patients with higher MPRs of antihypertensive medications had a consistently lower risk of COVID-19 than those with lower MPRs of antihypertensive medications and non-users. Among patients who showed complete compliance, there was a significantly lower risk of COVID-19 for those prescribed angiotensin II receptor blockers (relative risk [RR], 0.751; 95% confidence interval [CI], 0.587-0.960) or calcium channel blockers (RR, 0.768; 95% CI, 0.601-0.980).

CONCLUSION : Renin-angiotensin system blockers or other antihypertensive medications do not increase the risk of COVID-19. Patients should not stop antihypertensive medications, including renin-angiotensin system blockers, because of concerns of COVID-19.

Kim Jaiyong, Kim Dong Wook, Kim Kwang Il, Kim Hong Bin, Kim Jong Hun, Lee Yong Gab, Byeon Kyeong Hyang, Cheong Hae Kwan

2020-Jun-29

Antihypertensive Medication, Big Data, Cohort Study, Coronavirus Disease 2019, Risk Assessment

Public Health

AI Surveillance during Pandemics: Ethical Implementation Imperatives.

In The Hastings Center report
Artificial intelligence surveillance can be used to diagnose individual cases, track the spread of Covid-19, and help provide care. The use of AI for surveillance purposes (such as detecting new Covid-19 cases and gathering data from healthy and ill individuals) in a pandemic raises multiple concerns ranging from privacy to discrimination to access to care. Luckily, there exist several frameworks that can help guide stakeholders, especially physicians but also AI developers and public health officials, as they navigate these treacherous shoals. While these frameworks were not explicitly designed for AI surveillance during a pandemic, they can be adapted to help address concerns regarding privacy, human rights, and due process and equality. In a time where the rapid implementation of all tools available is critical to ending a pandemic, physicians, public health officials, and technology companies should understand the criteria for the ethical implementation of AI surveillance.
Shachar Carmel, Gerke Sara, Adashi Eli Y

2020-May

Covid-19, artificial intelligence, data privacy, equality, surveillance

General

Data stream dataset of SARS-CoV-2 genome.

In Data in brief
As of May 25, 2020, the novel coronavirus disease (called COVID-19) spread to more than 185 countries/regions with more than 348,000 deaths and more than 5,550,000 confirmed cases. In the bioinformatics area, one of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream techniques and algorithms. However, to make feasible this approach, it is necessary to transform the nucleotide sequences string to numerical stream representation. Thus, the dataset provides four kinds of data stream representation (DSR) of SARS-CoV-2 virus nucleotide sequences. The dataset provides the DSR of 1557 instances of SARS-CoV-2 virus, 11540 other instances of other viruses from the Virus-Host DB dataset, and three instances of Riboviria viruses from NCBI (Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21).
Barbosa Raquel de M, Fernandes Marcelo A C

2020-Aug

COVID-19, Data stream, SARS-CoV-2

Public Health

Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach.

In Wellcome open research
Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.
Carrillo-Larco Rodrigo M, Castillo-Cara Manuel

2020

COVID-19, clustering, k-mean, pandemic, unsupervised algorithms

General

Origin of Novel Coronavirus (COVID-19): A Computational Biology Study using Artificial Intelligence

bioRxiv Preprint
Origin of the COVID-19 virus has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic. Recent research results suggest that bats or pangolins might be the original hosts for the virus based on comparative studies using its genomic sequences. This paper investigates the COVID-19 virus origin by using artificial intelligence (AI) and raw genomic sequences of the virus. More than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods. The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes. This provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 virus. At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.
Nguyen, T. T.; Abdelrazek, M.; Nguyen, D. T.; Aryal, S.; Nguyen, D. T.; Khatami, A.

2020-07-01

General

Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods.

In Computer methods and programs in biomedicine

CONTEXT : People who have recently recovered from the threat of deteriorating coronavirus disease-2019 (COVID-19) have antibodies to the coronavirus circulating in their blood. Thus, the transfusion of these antibodies to deteriorating patients could theoretically help boost their immune system. Biologically, two challenges need to be surmounted to allow convalescent plasma (CP) transfusion to rescue the most severe COVID-19 patients. First, convalescent subjects must meet donor selection plasma criteria and comply with national health requirements and known standard routine procedures. Second, multi-criteria decision-making (MCDM) problems should be considered in the selection of the most suitable CP and the prioritisation of patients with COVID-19.

OBJECTIVE : This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.

METHOD : The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between 'serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria' and 'patient list based on novel MCDM method known as subjective and objective decision by opinion score method'. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix.

RESULT : An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments.

DISCUSSION : The proposed framework implies the benefits of providing effective care and prevention of the extremely rapidly spreading COVID-19 from affecting patients and the medical sector.

Albahri O S, Al-Obaidi Jameel R, Zaidan A A, Albahri A S, Zaidan B B, Salih Mahmood M, Qays Abdulhadi, Dawood K A, Mohammed R T, Abdulkareem Karrar Hameed, Aleesa A M, Alamoodi A H, Chyad M A, Zulkifli Che Zalina

2020-Jun-20

COVID-19, Convalescent plasma therapy, MCDM, Machine learning, Protein biomarker, SODOSM, Serological

Radiology

Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning

ArXiv Preprint
Effective and non-invasive radiological imaging based tumor/lesion characterization (e.g., subtype classification) has long been a major aim in the oncology diagnosis and treatment procedures, with the hope of reducing needs for invasive surgical biopsies. Prior work are generally very restricted to a limited patient sample size, especially using patient studies with confirmed pathological reports as ground truth. In this work, we curate a patient cohort of 1305 dynamic contrast CT studies (i.e., 5220 multi-phase 3D volumes) with pathology confirmed ground truth. A novel fully-automated and multi-stage liver tumor characterization framework is proposed, comprising four steps of tumor proposal detection, tumor harvesting, primary tumor site selection, and deep texture-based characterization. More specifically, (1) we propose a 3D non-isotropic anchor-free lesion detection method; (2) we present and validate the use of multi-phase deep texture learning for precise liver lesion tissue characterization, named spatially adaptive deep texture (SaDT); (3) we leverage small-sized public datasets to semi-automatically curate our large-scale clinical dataset of 1305 patients where four main liver tumor subtypes of primary, secondary, metastasized and benign are presented. Extensive evaluations demonstrate that our new data curation strategy, combined with the SaDT deep dynamic texture analysis, can effectively improve the mean F1 scores by >8.6% compared with baselines, in differentiating four major liver lesion types. This is a significant step towards the clinical goal.
Yuankai Huo, Jinzheng Cai, Chi-Tung Cheng, Ashwin Raju, Ke Yan, Bennett A. Landman, Jing Xiao, Le Lu, Chien-Hung Liao, Adam Harrison

2020-06-28

Radiology

Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation.

In European radiology ; h5-index 62.0

OBJECTIVE : Lombardy (Italy) was the epicentre of the COVID-19 pandemic in March 2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support devices. In our Institution, most patients received chest CT at admission, only interpreted visually. Given the proven value of quantitative CT analysis (QCT) in the setting of ARDS, we tested QCT as an outcome predictor for COVID-19.

METHODS : We performed a single-centre retrospective study on COVID-19 patients hospitalised from January 25, 2020, to April 28, 2020, who received CT at admission prompted by respiratory symptoms such as dyspnea or desaturation. QCT was performed using a semi-automated method (3D Slicer). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient's clinical data including oxygenation support throughout hospitalisation.

RESULTS : Two hundred twenty-two patients (163 males, median age 66, IQR 54-6) were included; 75% received oxygenation support (20% intubation rate). Compromised lung volume was the most accurate outcome predictor (logistic regression, p < 0.001). %CL values in the 6-23% range increased risk of oxygenation support; values above 23% were at risk for intubation. %CL showed a negative correlation with PaO₂/FiO₂ ratio (p < 0.001) and was a risk factor for in-hospital mortality (p < 0.001).

CONCLUSIONS : QCT provides new metrics of COVID-19. The compromised lung volume is accurate in predicting the need for oxygenation support and intubation and is a significant risk factor for in-hospital death. QCT may serve as a tool for the triaging process of COVID-19.

KEY POINTS : • Quantitative computer-aided analysis of chest CT (QCT) provides new metrics of COVID-19. • The compromised lung volume measured in the - 500, 100 HU interval predicts oxygenation support and intubation and is a risk factor for in-hospital death. • Compromised lung values in the 6-23% range prompt oxygenation therapy; values above 23% increase the need for intubation.

Lanza Ezio, Muglia Riccardo, Bolengo Isabella, Santonocito Orazio Giuseppe, Lisi Costanza, Angelotti Giovanni, Morandini Pierandrea, Savevski Victor, Politi Letterio Salvatore, Balzarini Luca

2020-Jun-26

COVID-19, Intubation, Pulmonary ventilation, Tomography, spiral computed

General

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19

ArXiv Preprint
Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis.
Abdelkader Nasreddine Belkacem, Sofia Ouhbi, Abderrahmane Lakas, Elhadj Benkhelifa, Chao Chen

2020-06-28

General

New machine learning method for image-based diagnosis of COVID-19.

In PloS one ; h5-index 176.0
COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.
Elaziz Mohamed Abd, Hosny Khalid M, Salah Ahmad, Darwish Mohamed M, Lu Songfeng, Sahlol Ahmed T

2020

General

Truncated inception net: COVID-19 outbreak screening using chest X-rays.

In Physical and engineering sciences in medicine
Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in a short period of time, and the infection, caused by SARS-CoV-2, is spreading rapidly. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID-19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis, and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using the acquired CXRs, and proves the viability of using the proposed Truncated Inception Net as a screening tool.
Das Dipayan, Santosh K C, Pal Umapada

2020-Jun-25

CNN, COVID-19, Chest X-rays, Deep learning, Inception net, Pneumonia, Tuberculosis

General

A Digital Protein Microarray for COVID-19 Cytokine Storm Monitoring.

In medRxiv : the preprint server for health sciences
Despite widespread concern for cytokine storms leading to severe morbidity in COVID-19, rapid cytokine assays are not routinely available for monitoring critically ill patients. We report the clinical application of a machine learning-based digital protein microarray platform for rapid multiplex quantification of cytokines from critically ill COVID-19 patients admitted to the intensive care unit (ICU) at the University of Michigan Hospital. The platform comprises two low-cost modules: (i) a semi-automated fluidic dispensing/mixing module that can be operated inside a biosafety cabinet to minimize the exposure of technician to the virus infection and (ii) a 12-12-15 inch compact fluorescence optical scanner for the potential near-bedside readout. The platform enabled daily cytokine analysis in clinical practice with high sensitivity (<0.4pg/mL), inter-assay repeatability (~10% CV), and near-real-time operation with a 10 min assay incubation. A cytokine profiling test with the platform allowed us to observe clear interleukin-6 (IL-6) elevations after receiving tocilizumab (IL-6 inhibitor) while significant cytokine profile variability exists across all critically ill COVID-19 patients and to discover a weak correlation between IL-6 to clinical biomarkers, such as Ferritin and CRP. Our data revealed large subject-to-subject variability in a patient's response to anti-inflammatory treatment for COVID-19, reaffirming the need for a personalized strategy guided by rapid cytokine assays.
Song Yujing, Ye Yuxuan, Su Shiuan-Haur, Stephens Andrew, Cai Tao, Chung Meng-Ting, Han Meilan, Newstead Michael W, Frame David, Singer Benjamin H, Kurabayashi Katsuo

2020-Jun-17

Public Health

Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach.

In Wellcome open research
Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.
Carrillo-Larco Rodrigo M, Castillo-Cara Manuel

2020

COVID-19, clustering, k-mean, pandemic, unsupervised algorithms

General

Advanced bioinformatics rapidly identifies existing therapeutics for patients with coronavirus disease-2019 (COVID-19).

In Journal of translational medicine

BACKGROUND : The recent global pandemic has placed a high priority on identifying drugs to prevent or lessen clinical infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused by Coronavirus disease-2019 (COVID-19).

METHODS : We applied two computational approaches to identify potential therapeutics. First, we sought to identify existing FDA approved drugs that could block coronaviruses from entering cells by binding to ACE2 or TMPRSS2 using a high-throughput AI-based binding affinity prediction platform. Second, we sought to identify FDA approved drugs that could attenuate the gene expression patterns induced by coronaviruses, using our Disease Cancelling Technology (DCT) platform.

RESULTS : Top results for ACE2 binding iincluded several ACE inhibitors, a beta-lactam antibiotic, two antiviral agents (Fosamprenavir and Emricasan) and glutathione. The platform also assessed specificity for ACE2 over ACE1, important for avoiding counterregulatory effects. Further studies are needed to weigh the benefit of blocking virus entry against potential counterregulatory effects and possible protective effects of ACE2. However, the data herein suggest readily available drugs that warrant experimental evaluation to assess potential benefit. DCT was run on an animal model of SARS-CoV, and ranked compounds by their ability to induce gene expression signals that counteract disease-associated signals. Top hits included Vitamin E, ruxolitinib, and glutamine. Glutathione and its precursor glutamine were highly ranked by two independent methods, suggesting both warrant further investigation for potential benefit against SARS-CoV-2.

CONCLUSIONS : While these findings are not yet ready for clinical translation, this report highlights the potential use of two bioinformatics technologies to rapidly discover existing therapeutic agents that warrant further investigation for established and emerging disease processes.

Kim Jason, Zhang Jenny, Cha Yoonjeong, Kolitz Sarah, Funt Jason, Escalante Chong Renan, Barrett Scott, Kusko Rebecca, Zeskind Ben, Kaufman Howard

2020-Jun-25

Artificial intelligence, Bioinformatics, Computational Biology, Coronavirus, Drug therapy

General

A prospect on the use of antiviral drugs to control local outbreaks of COVID-19.

In BMC medicine ; h5-index 89.0

BACKGROUND : Current outbreaks of COVID-19 are threatening the health care systems of several countries around the world. Control measures, based on isolation, contact tracing, and quarantine, can decrease and delay the burden of the ongoing epidemic. With respect to the ongoing COVID-19 epidemic, recent modeling work shows that these interventions may be inadequate to control local outbreaks, even when perfect isolation is assumed. The effect of infectiousness prior to symptom onset combined with asymptomatic infectees further complicates the use of contact tracing. We aim to study whether antivirals, which decrease the viral load and reduce infectiousness, could be integrated into control measures in order to augment the feasibility of controlling the epidemic.

METHODS : Using a simulation-based model of viral transmission, we tested the efficacy of different intervention measures to control local COVID-19 outbreaks. For individuals that were identified through contact tracing, we evaluate two procedures: monitoring individuals for symptoms onset and testing of individuals. Additionally, we investigate the implementation of an antiviral compound combined with the contact tracing process.

RESULTS : For an infectious disease in which asymptomatic and presymptomatic infections are plausible, an intervention measure based on contact tracing performs better when combined with testing instead of monitoring, provided that the test is able to detect infections during the incubation period. Antiviral drugs, in combination with contact tracing, quarantine, and isolation, result in a significant decrease of the final size and the peak incidence, and increase the probability that the outbreak will fade out.

CONCLUSION : In all tested scenarios, the model highlights the benefits of control measures based on the testing of traced individuals. In addition, the administration of an antiviral drug, together with quarantine, isolation, and contact tracing, is shown to decrease the spread of the epidemic. This control measure could be an effective strategy to control local and re-emerging outbreaks of COVID-19.

Torneri Andrea, Libin Pieter, Vanderlocht Joris, Vandamme Anne-Mieke, Neyts Johan, Hens Niel

2020-Jun-25

General

Can Artificial Intelligence and Internet be the Solution to Prevent the Exponential Spread of COVID-19?

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Artificial intelligence (AI) and internet can have a promising role in maximizing safety and preventing the rapid terrifying spread of COVID-19. With the exponential increase in the number of COVID-19 patients, there is a high possibility that the physicians and health care workers will not be able to cover all of these cases. Thus, computer scientists should contribute in this struggle against COVID-19 by introducing more intelligent solutions to achieve a rapid control of the SARS-CoV-2 virus.

OBJECTIVE : Our objective was to provide analysis for the current literature along with discussing the possible applicability for the available ideas that utilize AI to prevent and control COVID-19 showing how the current systems may be of use in a particular area to build a comprehensive view. This can be of great help to many healthcare administrators, computer scientists, and policy makers around the world.

METHODS : We conducted an electronic search of articles that utilize MEDLINE, Google Scholar, Embase and Web of Knowledge databases to formulate a comprehensive view that summarize different ideas about the most recent approaches to prevent and control the spread of COVID-19 using AI.

RESULTS : Our search identified the 10 most recent artificial intelligence approaches, which were suggested to be able to give the best solutions for maximizing safety and preventing the spread of COVID-19. The approaches included detection of suspected cases, large scale screening, monitoring, interactions with experimental therapies, screening pneumonia, internet of intelligent things for data and information, resources allocation, predictions, modeling and simulation, and robotics for medical quarantine.

CONCLUSIONS : It was found that the most approaches which almost have none or very few studies are the usage of AI for COVID-19 interactions with experimental therapies, usage of AI for resources allocation to COVID-19 patients, and usage of AI and internet of intelligent things for COVID-19 data and information. However, other approaches including usage of AI for COVID-19 predictions, usage of AI for COVID-19 modeling and simulation, and usage of AI robotics for medical quarantine should be furtherly emphasized to be adopted by researchers since these important approaches lack sufficient number of studies. Therefore, our recommendation for computer scientists is to focus on these approaches that are still not being adequately covered.

CLINICALTRIAL :

Adly Aya Sedky, Adly Afnan Sedky, Adly Mahmoud Sedky

2020-Jun-25

Public Health

An Aberration Detection-Based Approach for Sentinel Syndromic Surveillance of COVID-19 and Other Novel Influenza-Like Illnesses.

In medRxiv : the preprint server for health sciences
Coronavirus Disease 2019 has emerged as a significant global concern, triggering harsh public health restrictions in a successful bid to curb its exponential growth. As discussion shifts towards relaxation of these restrictions, there is significant concern of second-wave resurgence. The key to managing these outbreaks is early detection and intervention, and yet there is significant lag time associated with usage of laboratory confirmed cases for surveillance purposes. To address this, syndromic surveillance can be considered to provide a timelier alternative for first-line screening. Existing syndromic surveillance solutions are however typically focused around a known disease and have limited capability to distinguish between outbreaks of individual diseases sharing similar syndromes. This poses a challenge for surveillance of COVID-19 as its active periods are tend to overlap temporally with other influenza-like illnesses. In this study we explore performing sentinel syndromic surveillance for COVID-19 and other influenza-like illnesses using a deep learning-based approach. Our methods are based on aberration detection utilizing autoencoders that leverages symptom prevalence distributions to distinguish outbreaks of two ongoing diseases that share similar syndromes, even if they occur concurrently. We first demonstrate that this approach works for detection of outbreaks of influenza, which has known temporal boundaries. We then demonstrate that the autoencoder can be trained to not alert on known and well-managed influenza-like illnesses such as the common cold and influenza. Finally, we applied our approach to 2019-2020 data in the context of a COVID-19 syndromic surveillance task to demonstrate how implementation of such a system could have provided early warning of an outbreak of a novel influenza-like illness that did not match the symptom prevalence profile of influenza and other known influenza-like illnesses.
Wen Andrew, Wang Liwei, He Huan, Liu Sijia, Fu Sunyang, Sohn Sunghwan, Kugel Jacob A, Kaggal Vinod C, Huang Ming, Wang Yanshan, Shen Feichen, Fan Jungwei, Liu Hongfang

2020-Jun-09

General

Development and Prospective Validation of a Transparent Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.

In medRxiv : the preprint server for health sciences

IMPORTANCE : Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation is of great importance and may aid in delivering timely treatment.

OBJECTIVE : To develop, externally validate and prospectively test a transparent deep learning algorithm for predicting 24 hours in advance the need for mechanical ventilation in hospitalized patients and those with COVID-19.

DESIGN : Observational cohort study SETTING: Two academic medical centers from January 01, 2016 to December 31, 2019 (Retrospective cohorts) and February 10, 2020 to May 4, 2020 (Prospective cohorts).

PARTICIPANTS : Over 31,000 admissions to the intensive care units (ICUs) at two hospitals. Additionally, 777 patients with COVID-19 patients were used for prospective validation. Patients who were placed on mechanical ventilation within four hours of their admission were excluded. MAIN OUTCOME(S) and MEASURE(S): Electronic health record (EHR) data were extracted on an hourly basis, and a set of 40 features were calculated and passed to an interpretable deep-learning algorithm to predict the future need for mechanical ventilation 24 hours in advance. Additionally, commonly used clinical criteria (based on heart rate, oxygen saturation, respiratory rate, FiO2 and pH) was used to assess future need for mechanical ventilation. Performance of the algorithms were evaluated using the area under receiver-operating characteristic curve (AUC), sensitivity, specificity and positive predictive value.

RESULTS : After applying exclusion criteria, the external validation cohort included 3,888 general ICU and 402 COVID-19 patients. The performance of the model (AUC) with a 24-hour prediction horizon at the validation site was 0.882 for the general ICU population and 0.918 for patients with COVID-19. In comparison, commonly used clinical criteria and the ROX score achieved AUCs in the range of 0.773 - 0.782 and 0.768 - 0.810 for the general ICU population and patients with COVID-19, respectively.

CONCLUSIONS AND RELEVANCE : A generalizable and transparent deep-learning algorithm improves on traditional clinical criteria to predict the need for mechanical ventilation in hospitalized patients, including those with COVID-19. Such an algorithm may help clinicians with optimizing timing of tracheal intubation, better allocation of mechanical ventilation resources and staff, and improve patient care.

Shashikumar Supreeth P, Wardi Gabriel, Paul Paulina, Carlile Morgan, Brenner Laura N, Hibbert Kathryn A, North Crystal M, Mukerji Shibani, Robbins Gregory, Shao Yu-Ping, Malhotra Atul, Westover Brandon, Nemati Shamim

2020-Jun-03

General

Sarbecovirus comparative genomics elucidates gene content of SARS-CoV-2 and functional impact of COVID-19 pandemic mutations.

In bioRxiv : the preprint server for biology
Despite its overwhelming clinical importance for understanding and mitigating the COVID-19 pandemic, the protein-coding gene content of the SARS-CoV-2 genome remains unresolved, with the function and even protein-coding status of many hypothetical proteins unknown and often conflicting among different annotations, thus hindering efforts for systematic dissection of its biology and the impact of recent mutations. Comparative genomics is a powerful approach for distinguishing protein-coding versus non-coding functional elements, based on their characteristic patterns of change, which we previously used to annotate protein-coding genes in human, fly, and other species. Here, we use comparative genomics to provide a high-confidence set of SARS-CoV-2 protein-coding genes, to characterize their protein-level and nucleotide-level evolutionary constraint, and to interpret the functional implications for SARS-CoV-2 mutations acquired during the current pandemic. We select 44 complete Sarbecovirus genomes at evolutionary distances well-suited for protein-coding and non-coding element identification, create whole-genome alignments spanning all named and putative genes, and quantify their protein-coding evolutionary signatures using PhyloCSF and their overlapping constraint using FRESCo. We find strong protein-coding signatures for all named genes and for hypothetical ORFs 3a, 6, 7a, 7b, and 8, indicating protein-coding roles, and provide strong evidence of protein-coding status for a recently-proposed alternate-frame novel ORF within 3a. By contrast, ORF10 shows no protein-coding signatures but shows unusually-high nucleotide-level constraint, indicating it has important but non-coding functions, and ORF14 and SARS-CoV-1 ORF3b, which overlap other genes, lack evolutionary signatures expected for dual-coding regions, indicating they do not produce functional proteins. ORF9b has ambiguous protein-coding signatures, preventing us from resolving its protein-coding status. ORF8 shows extremely fast nucleotide-level evolution, lacks a known function, and was deactivated in SARS-CoV-1, but shows clear signatures indicating protein-coding function worthy of further investigation given its rapid evolution and potential role in replication. SARS-CoV-2 mutations are preferentially excluded from evolutionarily-constrained amino acid residues and synonymously-constrained nucleotides, indicating purifying constraint acting at both coding and non-coding levels. In contrast, we find a conserved region in the nucleocapsid that is enriched for recent mutations, which could indicate a selective signal, and find that several spike-protein mutations previously identified as candidates for increased transmission and several mutations in isolates found to generate higher viral load in-vitro disrupt otherwise-perfectly-conserved amino-acids, consistent with adaptations for human-to-human transmission.
Jungreis Irwin, Sealfon Rachel, Kellis Manolis

2020-Jun-03

General

Spatial modelling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between 19 February to 14 June 2020).

In International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

OBJECTIVES : Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries and 2 international conveyance with more than 432,902 recorded deaths and 7,898,442 confirmed global worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19.

METHODS : This is the first comprehensive study of COVID-19 in Iran and it undertakes spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends; prediction of mortality trends using regression modelling; spatial modelling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT); and validation of the modelled risk map.

RESULTS : The results show that from February 19 to June 14, 2020 the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on World Health Organisation (WHO) data, Iran's fatality (deaths/0.1 M pop) is 10.53. Other countries' fatality rates were, for comparison, Belgium - 83.32, UK - 61.39, Spain - 58.04, Italy - 56.73, Sweden - 48.28, France - 45.04, USA - 35.52, Canada - 21.49, Brazil - 20.10, Peru - 19.70, Chile - 16.20, Mexico- 12.80, and Germany - 10.58. This fatality rate for China is 0.32 (deaths/0.1 M pop). The heatmap of the infected areas over time identified two critical time intervals for the COVID-19 outbreak in Iran. The provinces were classified in terms of disease and death rates into a large primary group and three provinces that had critical outbreaks that were separate from others. The heatmap of countries of the world show that China and Italy were distinguished from other countries in terms of nine viral infection-related parameters. The regression models for death cases showed an increasing trend but with some evidences of turning. A polynomial relationship was identified between coronavirus infection rate and province population density. In addition, a third-degree polynomial regression model for deaths showed an increasing trend recently, indicating that subsequent measures taken to cope with the outbreak have been insufficient and ineffective. The general trend of deaths in Iran is similar to the worlds, but it shows lower volatility. Change detection of COVID-19 risk maps with a random forest model for the period from March 11^th to March 18^th showed an increasing trend in COVID-19 in Iran's provinces. It is worth noting that using the LASSO MLT to evaluate variables' importance indicated that the most important variables were distance from bus stations, bakeries, hospitals, mosques, ATMs (automated teller machines), banks, and the minimum temperature of the coldest month.

CONCLUSIONS : We believe that the risk maps provided by this study is the primary, fundamental step for managing and controlling COVID-19 in Iran and its provinces.

Pourghasemi Hamid Reza, Pouyan Soheila, Heidari Bahram, Farajzadeh Zakariya, Shamsi Seyed Rashid Fallah, Babaei Sedigheh, Khosravi Rasoul, Etemadi Mohammad, Ghanbarian Gholamabbas, Farhadi Ahmad, Safaeian Roja, Heidari Zahra, Tarazkar Mohammad Hassan, Tiefenbacher John P, Azmi Amir, Sadeghian Faezeh

2020-Jun-20

Heatmap, Iran, Outbreak trend, Regression model, Risk map, Spatial modelling

General

Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India.

In Chaos, solitons, and fractals
In this paper, Deep Learning-based models are used for predicting the number of novel coronavirus (COVID-19) positive reported cases for 32 states and union territories of India. Recurrent neural network (RNN) based long-short term memory (LSTM) variants such as Deep LSTM, Convolutional LSTM and Bi-directional LSTM are applied on Indian dataset to predict the number of positive cases. LSTM model with minimum error is chosen for predicting daily and weekly cases. It is observed that the proposed method yields high accuracy for short term prediction with error less than 3% for daily predictions and less than 8% for weekly predictions. Indian states are categorised into different zones based on the spread of positive cases and daily growth rate for easy identification of novel coronavirus hot-spots. Preventive measures to reduce the spread in respective zones are also suggested. A website is created where the state-wise predictions are updated using the proposed model for authorities,researchers and planners. This study can be applied by other countries for predicting COVID-19 cases at the state or national level.
Arora Parul, Kumar Himanshu, Panigrahi Bijaya Ketan

2020-Oct

COVID-19, Deep learning, LSTM, Prediction, RNN

Radiology

Chest CT in COVID-19 pneumonia: A review of current knowledge.

In Diagnostic and interventional imaging
The current COVID-19 pandemic has highlighted the essential role of chest computed tomography (CT) examination in patient triage in the emergency departments, allowing them to be referred to "COVID" or "non-COVID" wards. Initial chest CT examination must be performed without intravenous administration of iodinated contrast material, but contrast material administration is required when pulmonary embolism is suspected, which seems to be frequent in severe forms of the disease. Typical CT features consist of bilateral ground-glass opacities with peripheral, posterior and basal predominance. Lung disease extent on CT correlates with clinical severity. Artificial intelligence could assist radiologists for diagnosis and prognosis evaluation.
Jalaber C, Lapotre T, Morcet-Delattre T, Ribet F, Jouneau S, Lederlin M

2020-Jun-11

COVID-19, Pulmonary embolism, Severe acute respiratory syndrome coronavirus 2, Tomography, X-ray computed

Public Health

Global Comparison of Changes in the Number of Test-Positive Cases and Deaths by Coronavirus Infection (COVID-19) in the World.

In Journal of clinical medicine
Global differences in changes in the numbers of population-adjusted daily test-positive cases (NPDP) and deaths (NPDD) by COVID-19 were analyzed for 49 countries, including developed and developing countries. The changes as a proportion of national population were compared, adjusting by the beginning of test-positive cases increase (BPI) or deaths increase (BDI). Remarkable regional differences of more than 100-fold in NPDP and NPDD were observed. The trajectories of NPDD after BDI increased exponentially within 20 days in most countries. Machine learning analysis suggested that NPDD on 30 days after BDI was the highest in developed Western countries (1180 persons per hundred million), followed by countries in the Middle East (128), Latin America (97), and Asia (7). Furthermore, in Western countries with positive rates of the PCR test of less than 7.0%, the increase in NPDP was slowing-down two weeks after BPI, and subsequent NPDD was only 15% compared with those with higher positive rates, which suggested that the situation of testing might have affected the velocity of COVID-19 spread. The causes behind remarkable differences between regions possibly include genetic factors of inhabitants because distributions of the race and of the observed infection increasing rates were in good agreement globally.
Hisaka Akihiro, Yoshioka Hideki, Hatakeyama Hiroto, Sato Hiromi, Onouchi Yoshihiro, Anzai Naohiko

2020-Jun-18

COVID-19, PCR test, coronavirus, infection management, infectious disease, kinetic analysis, mortality

General

Using Deep Learning and Explainable Artificial Intelligence in Patients' Choices of Hospital Levels

ArXiv Preprint
In countries that enabled patients to choose their own providers, a common problem is that the patients did not make rational decisions, and hence, fail to use healthcare resources efficiently. This might cause problems such as overwhelming tertiary facilities with mild condition patients, thus limiting their capacity of treating acute and critical patients. To address such maldistributed patient volume, it is essential to oversee patients choices before further evaluation of a policy or resource allocation. This study used nationwide insurance data, accumulated possible features discussed in existing literature, and used a deep neural network to predict the patients choices of hospital levels. This study also used explainable artificial intelligence methods to interpret the contribution of features for the general public and individuals. In addition, we explored the effectiveness of changing data representations. The results showed that the model was able to predict with high area under the receiver operating characteristics curve (AUC) (0.90), accuracy (0.90), sensitivity (0.94), and specificity (0.97) with highly imbalanced label. Generally, social approval of the provider by the general public (positive or negative) and the number of practicing physicians serving per ten thousand people of the located area are listed as the top effecting features. The changing data representation had a positive effect on the prediction improvement. Deep learning methods can process highly imbalanced data and achieve high accuracy. The effecting features affect the general public and individuals differently. Addressing the sparsity and discrete nature of insurance data leads to better prediction. Applications using deep learning technology are promising in health policy making. More work is required to interpret models and practice implementation.
Lichin Chen, Yu Tsao, Ji-Tian Sheu

2020-06-24

General

Can artificial intelligence identify effective COVID-19 therapies?

In EMBO molecular medicine
In this issue of EMBO Molecular Medicine, Stebbing et al validate an artificial intelligence-assisted prediction that a drug used to treat rheumatoid arthritis could be a potent weapon against COVID-19. Using liver organoids infected with SARS-CoV-2, they confirm dual antiviral and anti-inflammatory activities, and show that its administration in four COVID-19 patients is correlated with disease improvement, paving the way for more rigorous placebo-controlled trials.
Schultz Michael B, Vera Daniel, Sinclair David A

2020-Jun-22

Artificial intelligence, COVID-19, Numb-associated kinase, antiviral, baricitinib

Radiology

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians.

OBJECTIVE : We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT.

METHODS : : A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers.

RESULTS : Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively).

CONCLUSIONS : The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

CLINICALTRIAL :

Ko Hoon, Chung Heewon, Kim Kyung Won, Shin Youngbin Shin, Kang Seung Ji, Lee Jae Hoon, Kim Young Jun, Kim Nan Yeol, Jung Hyun Seok, Kang Wu Seong, Lee Jinseok

2020-Jun-21

Public Health

The Role of Health Technology and Informatics in Global Public Health Emergency: Practices and Implications from the COVID-19 Pandemic.

In JMIR medical informatics ; h5-index 23.0
At present, COVID-19 is spreading around the world. It is a critical and important task to take all-out efforts to prevent and control the pandemic. Compared with Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome, COVID-19 spreads more rapidly owing to increased globalization, longer incubation period, and unobvious symptoms. As the coronavirus has the characteristics of strong transmission and weak lethality, and since the large-scale increase of infected people may drag down the healthcare systems, efforts are needed to treat critical patients, track and manage the health status of residents, and isolate suspected patients. The application of emerging health technologies and digital practices in healthcare such as artificial intelligence, telemedicine/telehealth, mobile health, big data, 5G, and the internet of things have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control. Applications and evaluations of all of these technologies or health delivery services are highlighted.
Ye Jiancheng

2020-Jun-21

General

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches.

In Computers in biology and medicine
Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.
Toğaçar Mesut, Ergen Burhan, Cömert Zafer

2020-Jun

2019-nCoV, COVID-19, Deep learning, Fuzzy color technique, Social mimic, Stacking technique

Radiology

Automated detection of COVID-19 cases using deep neural networks with X-ray images.

In Computers in biology and medicine
The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
Ozturk Tulin, Talo Muhammed, Yildirim Eylul Azra, Baloglu Ulas Baran, Yildirim Ozal, Rajendra Acharya U

2020-Jun

Chest X-ray images, Coronavirus (COVID-19), Deep learning, Radiology images

General

Targeting infectious Coronavirus Disease 2019 (COVID-19) with Artificial Intelligence (AI) applications: Evidence based opinion.

In Infectious disorders drug targets
The current COVID-19 pandemic has opened the new doors for AI based technology adoption in various health care sectors. This will be bringing opportunities as well as risks. These technologies, upon proper human interventions and directions, will be useful for making faster decisions in current crisis. However, this should not mean that AI could bypass the various regulatory processes. Besides multiple potential applications, we should know that these technologies will be only benefited with proper human interventions and un-biased data. With proper AI based approaches, we will be definitely benefited with rapid diagnosis of disease, early warnings, fastening drug development processes and proper social control measures during this pandemic.
Mali Suraj N, Pratap Amit P

2020-Jun-22

AI Applications, Artificial Intelligence (AI), COVID-19, Coronavirus Pandemic

Radiology

End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

PURPOSE : In the absence of a virus nucleic acid real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test and experienced radiologists, clinical diagnosis is challenging for viral pneumonia with clinical symptoms and CT signs similar to that of coronavirus disease 2019 (COVID-19). We developed an end-to-end automatic differentiation method based on CT images to identify COVID-19 pneumonia patients in real time.

METHODS : From January 18 to February 23, 2020, we conducted a retrospective study and enrolled 201 patients from two hospitals in China who underwent chest CT and RT-PCR tests, of which 98 patients tested positive for COVID-19 (118 males and 83 females, with an average age of 42 years). Patient CT images from one hospital were divided among training, validation and test datasets with an 80%:10%:10% ratio. An end-to-end representation learning method using a large-scale bi-directional generative adversarial network (BigBiGAN) architecture was designed to extract semantic features from the CT images. The semantic feature matrix was input for linear classifier construction. Patients from the other hospital were used for external validation. Differentiation accuracy was evaluated using a receiver operating characteristic curve.

RESULTS : Based on the 120-dimensional semantic features extracted by BigBiGAN from each image, the linear classifier results indicated that the area under the curve (AUC) in the training, validation and test datasets were 0.979, 0.968 and 0.972, respectively, with an average sensitivity of 92% and specificity of 91%. The AUC for external validation was 0.850, with a sensitivity of 80% and specificity of 75%. Publicly available architecture and computing resources were used throughout the study to ensure reproducibility.

CONCLUSION : This study provides an efficient recognition method for coronavirus disease 2019 pneumonia, using an end-to-end design to implement targeted and effective isolation for the containment of this communicable disease.

Song Jiangdian, Wang Hongmei, Liu Yuchan, Wu Wenqing, Dai Gang, Wu Zongshan, Zhu Puhe, Zhang Wei, Yeom Kristen W, Deng Kexue

2020-Jun-22

Artificial intelligence, BigBiGAN, Coronavirus disease 2019 pneumonia, Differentiation, Semantic features

Radiology

Quantitative analysis of chest CT imaging findings with the risk of ARDS in COVID-19 patients: a preliminary study.

In Annals of translational medicine

Background : The coronavirus disease 2019 (COVID-19) has rapidly become a pandemic worldwide. The value of chest computed tomography (CT) is debatable during the treatment of COVID-19 patients. Compared with traditional chest X-ray radiography, quantitative CT may supply more information, but its value on COVID-19 patients was still not proven.

Methods : An automatic quantitative analysis model based on a deep network called VB-Net for infection region segmentation was developed. A quantitative analysis was performed for patients diagnosed as severe COVID 19. The quantitative assessment included volume and density among the infectious area. The primary clinical outcome was the existence of acute respiratory distress syndrome (ARDS). A univariable and multivariable logistic analysis was done to explore the relationship between the quantitative results and ARDS existence.

Results : The VB-Ne model was sensitive and stable for pulmonary lesion segmentation, and quantitative analysis indicated that the total volume and average density of the lung lesions were not related to ARDS. However, lesions with specific density changes showed some influence on the risk of ARDS. The proportion of lesion density from -549 to -450 Hounsfield unit (HU) was associated with increased risk of ARDS, while the density was ranging from -149 to -50 HU was related to a lowered risk of ARDS.

Conclusions : The automatic quantitative model based on VB-Ne can supply useful information for ARDS risk stratification in COVID-19 patients during treatment.

Wang Yi, Chen Yuntian, Wei Yi, Li Man, Zhang Yuwei, Zhang Na, Zhao Shuang, Zeng Hanjiang, Deng Wen, Huang Zixing, Ye Zheng, Wan Shang, Song Bin

2020-May

Coronavirus disease 2019 (COVID-19), acute respiratory distress syndrome (ARDS), artificial intelligence (AI), quantitative computed tomography assessment (quantitative CT assessment)

General

Examination of community sentiment dynamics due to covid-19 pandemic: a case study from Australia

ArXiv Preprint
The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has caused unprecedented impacts to people's daily life around the world. Various measures and policies such as lock-down and social-distancing are implemented by governments to combat the disease during the pandemic period. These measures and policies as well as virus itself may cause different mental health issues to people such as depression, anxiety, sadness, etc. In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Australia during the pandemic period. Different from the existing work that mostly focuses the country-level and static sentiment analysis, we analyse the sentiment dynamics at the fine-grained local government areas (LGAs). Based on the analysis of around 94 million tweets that posted by around 183 thousand users located at different LGAs in NSW in five months, we found that people in NSW showed an overall positive sentimental polarity and the COVID-19 pandemic decreased the overall positive sentimental polarity during the pandemic period. The fine-grained analysis of sentiment in LGAs found that despite the dominant positive sentiment most of days during the study period, some LGAs experienced significant sentiment changes from positive to negative. This study also analysed the sentimental dynamics delivered by the hot topics in Twitter such as government policies (e.g. the Australia's JobKeeper program, lock-down, social-distancing) as well as the focused social events (e.g. the Ruby Princess Cruise). The results showed that the policies and events did affect people's overall sentiment, and they affected people's overall sentiment differently at different stages.
Jianlong Zhou, Shuiqiao Yang, Chun Xiao, Fang Chen

2020-06-22

General

The Neurobiology of Social Distance.

In Trends in cognitive sciences ; h5-index 93.0
Never before have we experienced social isolation on such a massive scale as we have in response to coronavirus disease 2019 (COVID-19). However, we know that the social environment has a dramatic impact on our sense of life satisfaction and well-being. In times of distress, crisis, or disaster, human resilience depends on the richness and strength of social connections, as well as on active engagement in groups and communities. Over recent years, evidence emerging from various disciplines has made it abundantly clear: perceived social isolation (i.e., loneliness) may be the most potent threat to survival and longevity. We highlight the benefits of social bonds, the choreographies of bond creation and maintenance, as well as the neurocognitive basis of social isolation and its deep consequences for mental and physical health.
Bzdok Danilo, Dunbar Robin I M

2020-Jun-03

friendship, loneliness, mass isolation, social boycott, social brain

General

COVID-19 Diagnostics, Tools, and Prevention.

In Diagnostics (Basel, Switzerland)
The Coronavirus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), outbreak from Wuhan City, Hubei province, China in 2019 has become an ongoing global health emergency. The emerging virus, SARS-CoV-2, causes coughing, fever, muscle ache, and shortness of breath or dyspnea in symptomatic patients. The pathogenic particles that are generated by coughing and sneezing remain suspended in the air or attach to a surface to facilitate transmission in an aerosol form. This review focuses on the recent trends in pandemic biology, diagnostics methods, prevention tools, and policies for COVID-19 management. To meet the growing demand for medical supplies during the COVID-19 era, a variety of personal protective equipment (PPE) and ventilators have been developed using do-it-yourself (DIY) manufacturing. COVID-19 diagnosis and the prediction of virus transmission are analyzed by machine learning algorithms, simulations, and digital monitoring. Until the discovery of a clinically approved vaccine for COVID-19, pandemics remain a public concern. Therefore, technological developments, biomedical research, and policy development are needed to decipher the coronavirus mechanism and epidemiological characteristics, prevent transmission, and develop therapeutic drugs.
Allam Mayar, Cai Shuangyi, Ganesh Shambavi, Venkatesan Mythreye, Doodhwala Saurabh, Song Zexing, Hu Thomas, Kumar Aditi, Heit Jeremy, Study Group Covid-, Coskun Ahmet F

2020-Jun-16

3D printing, COVID-19, SARS-CoV-2, digital tracking, do-it-yourself, immunity, machine learning, pandemic policy, rapid testing, vaccines

General

Artificial Intelligence and the Future of Psychiatry.

In IEEE pulse
An estimated 792 million people live with mental health disorders worldwide-more than one in ten people-and this number is expected to grow in the shadow of the Coronavirus disease 2019 (COVID-19) pandemic. Unfortunately, there aren't enough mental health professionals to treat all these people. Can artificial intelligence (AI) help? While many psychiatrists have different views on this question, recent developments suggest AI may change the practice of psychiatry for both clinicians and patients.
Allen Summer

Radiology

A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection: A Retrospective Cohort Study.

In Journal of thoracic imaging

OBJECTIVE : This study aimed to use the radiomics signatures of a machine learning-based tool to evaluate the prognosis of patients with coronavirus disease 2019 (COVID-19) infection.

METHODS : The clinical and imaging data of 64 patients with confirmed diagnoses of COVID-19 were retrospectively selected and divided into a stable group and a progressive group according to the data obtained from the ongoing treatment process. Imaging features from whole-lung images from baseline computed tomography (CT) scans were extracted and dimensionality reduction was performed. Support vector machines were used to construct radiomics signatures and to compare differences between the 2 groups. We also compared the differences of signature scores in the clinical, laboratory, and CT image feature subgroups and finally analyzed the correlation between the radiomics features of the constructed signature and the other features including clinical, laboratory, and CT imaging features.

RESULTS : The signature has a good classification effect for the stable group and the progressive group, with area under curve, sensitivity, and specificity of 0.833, 80.95%, and 74.42%, respectively. Signature score differences in laboratory and CT imaging features between subgroups were not statistically significant (P>0.05); cough was negatively correlated with GLCM Entropy_angle 90_offset4 (r=-0.578), but was positively correlated with ShortRunEmphhasis_AllDirect_offset4_SD (r=0.454); C-reactive protein was positively correlated with Cluster Prominence_ AllDirect_offset 4_ SD (r=0.47).

CONCLUSION : The radiomics signature of the whole lung based on machine learning may reveal the changes of lung microstructure in the early stage and help to indicate the progression of the disease.

Fu Liping, Li Yongchou, Cheng Aiping, Pang PeiPei, Shu Zhenyu

2020-Jun-16

General

Discovery of Aptamers Targeting Receptor-Binding Domain of the SARS-CoV-2 Spike Glycoprotein.

In Analytical chemistry
The World Health Organization has declared the outbreak of a novel coronavirus (SARS-CoV-2 or 2019-nCoV) as a global pandemic. However, the mechanisms behind the coronavirus infection are not yet fully understand, nor are there any targeted treatments or vaccines. In this study, we identified high-binding-affinity aptamers targeting SARS-CoV-2 RBD, using an ACE2 competition-based aptamer selection strategy and a machine learning screening algorithm. The Kd values of the optimized CoV2-RBD-1C and CoV2-RBD-4C aptamers against RBD were 5.8 nM and 19.9 nM, respectively. Simulated interaction modeling, along with competitive with experiments, suggests that two aptamers may have partially identical binding sites at ACE2 on SARS-CoV-2 RBD. These aptamers present an opportunity for generating new probes for recognition of SARS-CoV-2, and could provide assistance in the diagnosis and treatment of SARS-CoV-2 while providing a new tool for in-depth study of the mechanisms behind the coronavirus infection.
Song Yanling, Song Jia, Wei Xinyu, Huang Mengjiao, Sun Miao, Zhu Lin, Lin Bingqian, Shen Haicong, Zhu Zhi, Yang Chaoyong

2020-Jun-17

General

Innovative use of artificial intelligence and digital communication in acute stroke pathway in response to COVID-19.

In Future healthcare journal
Acute stroke care demands real-time, specialist-led treatment decisions, including thrombolysis and referral for mechanical thrombectomy. Pathways designed to deliver time-critical interventions for stroke patients are under intense pressure due to the impact of COVID-19 pandemic. In response to this unprecedented burden on acute care services, stroke clinicians are having to reconfigure existing clinical pathways both within and between hospitals. Incorporating artificial intelligence and digital communication support into clinical pathways offers an opportunity to mitigate the disruption to acute stroke care. In this case study we describe how Royal Berkshire Hospital, working collaboratively with Brainomix, a UK-based artificial intelligence software company, adopted technological innovation and integrated it into the hyperacute stroke pathway. A case is presented to demonstrate how this innovation can support patient care and deliver successful patient outcomes. We believe this model can be adopted in other hospitals and networks to deliver safe and efficient hyperacute stroke care.
Nagaratnam Kiruba, Harston George, Flossmann Enrico, Canavan Clara, Geraldes Rui Carmelo, Edwards Chani

2020-Jun

AI, Acute stroke, COVID-19, artificial intelligence

General

Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers

ArXiv Preprint
An adversarial attack paradigm explores various scenarios for the vulnerability of deep learning models: minor changes of the input can force a model failure. Most of the state of the art frameworks focus on adversarial attacks for images and other structured model inputs, but not for categorical sequences models. Successful attacks on classifiers of categorical sequences are challenging because the model input is tokens from finite sets, so a classifier score is non-differentiable with respect to inputs, and gradient-based attacks are not applicable. Common approaches deal with this problem working at a token level, while the discrete optimization problem at hand requires a lot of resources to solve. We instead use a fine-tuning of a language model for adversarial attacks as a generator of adversarial examples. To optimize the model, we define a differentiable loss function that depends on a surrogate classifier score and on a deep learning model that evaluates approximate edit distance. So, we control both the adversability of a generated sequence and its similarity to the initial sequence. As a result, we obtain semantically better samples. Moreover, they are resistant to adversarial training and adversarial detectors. Our model works for diverse datasets on bank transactions, electronic health records, and NLP datasets.
I. Fursov, A. Zaytsev, N. Kluchnikov, A. Kravchenko, E. Burnaev

2020-06-19

Radiology

Chest lesion CT radiological features and quantitative analysis in RT-PCR turned negative and clinical symptoms resolved COVID-19 patients.

In Quantitative imaging in medicine and surgery

Background : Many studies have described lung lesion computed tomography (CT) features of coronavirus disease 2019 (COVID-19) patients at the early and progressive stages. In this study, we aim to evaluate lung lesion CT radiological features along with quantitative analysis for the COVID-19 patients ready for discharge.

Methods : From February 10 to March 10, 2020, 125 COVID-19 patients (age: 16-67 years, 63 males) ready for discharge, with two consecutive negative reverse transcription-polymerase chain reaction (RT-PCR) and no clinical symptoms for more than 3 days, were included. The pre-discharge CT was performed on all patients 1-3 days after the second negative RT-PCR test, and the follow-up CTs were performed on 44 patients 2-13 days later. The imaging features and quantitative analysis were evaluated on both the pre-discharge and the follow-up CTs, by both radiologists and an artificial intelligence (AI) software.

Results : On the pre-discharge CT, the most common CT findings included ground-glass opacity (GGO) (99/125, 79.2%) with bilateral mixed distribution, and fibrosis (56/125, 44.8%) with bilateral subpleural distribution. Enlarged mediastinal lymph nodes were also commonly observed (45/125, 36.0%). AI enabled quantitative analysis showed the right lower lobe was mostly involved, and lesions most commonly had CT value of -570 to -470 HU consistent with GGO. Follow-up CT showed GGO decrease in size and density (40/40, 100%) and fibrosis reduction (17/26, 65.4%). Compared with the pre-discharge CT results, quantitative analysis shows the lung lesion volume regressed significantly at follow-up.

Conclusions : For COVID-19 patients ready for discharge, GGO and fibrosis are the main CT features and they further regress at follow-up.

Du Siyao, Gao Si, Huang Guoliang, Li Shu, Chong Wei, Jia Ziyi, Hou Gang, Wáng Yì Xiáng J, Zhang Lina

2020-Jun

Coronavirus disease 2019 (COVID-19), computed tomography (CT), follow-up, lung, quantitative analysis

General

Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targetted Data Augmentation

ArXiv Preprint
According to the World Health Organization, distracted driving is one of the leading cause of motor accidents and deaths in the world. In our study, we tackle the problem of distracted driving by aiming to build a robust multi-class classifier to detect and identify different forms of driver inattention using the State Farm Distracted Driving Dataset. We utilize combinations of pretrained image classification models, classical data augmentation, OpenCV based image preprocessing and skin segmentation augmentation approaches. Our best performing model combines several augmentation techniques, including skin segmentation, facial blurring, and classical augmentation techniques. This model achieves an approximately 15% increase in F1 score over the baseline, thus showing the promise in these techniques in enhancing the power of neural networks for the task of distracted driver detection.
Nikka Mofid, Jasmine Bayrooti, Shreya Ravi

2020-06-19

General

Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection.

In PeerJ
MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression found in more than 200 diverse organisms. Although it is still not fully established if RNA viruses could generate miRNAs, there are examples of miRNA like sequences from RNA viruses with regulatory functions. In the case of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there are several mechanisms that would make miRNAs impact the virus, like interfering with viral replication, translation and even modulating the host expression. In this study, we performed a machine learning based miRNA prediction analysis for the SARS-CoV-2 genome to identify miRNA-like hairpins and searched for potential miRNA-based interactions between the viral miRNAs and human genes and human miRNAs and viral genes. Overall, 950 hairpin structured sequences were extracted from the virus genome and based on the prediction results, 29 of them could be precursor miRNAs. Targeting analysis showed that 30 viral mature miRNA-like sequences could target 1,367 different human genes. PANTHER gene function analysis results indicated that viral derived miRNA candidates could target various human genes involved in crucial cellular processes including transcription, metabolism, defense system and several signaling pathways such as Wnt and EGFR signalings. Protein class-based grouping of targeted human genes showed that host transcription might be one of the main targets of the virus since 96 genes involved in transcriptional processes were potential targets of predicted viral miRNAs. For instance, basal transcription machinery elements including several components of human mediator complex (MED1, MED9, MED12L, MED19), basal transcription factors such as TAF4, TAF5, TAF7L and site-specific transcription factors such as STAT1 were found to be targeted. In addition, many known human miRNAs appeared to be able to target viral genes involved in viral life cycle such as S, M, N, E proteins and ORF1ab, ORF3a, ORF8, ORF7a and ORF10. Considering the fact that miRNA-based therapies have been paid attention, based on the findings of this study, comprehending mode of actions of miRNAs and their possible roles during SARS-CoV-2 infections could create new opportunities for the development and improvement of new therapeutics.
Saçar Demirci Müşerref Duygu, Adan Aysun

2020

COVID19, Host–virus interaction, MicroRNA, SARS-CoV-2

Public Health

Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions.

In Journal of infection and public health
The substantial increase in the number of daily new cases infected with coronavirus around the world is alarming, and several researchers are currently using various mathematical and machine learning-based prediction models to estimate the future trend of this pandemic. In this work, we employed the Autoregressive Integrated Moving Average (ARIMA) model to forecast the expected daily number of COVID-19 cases in Saudi Arabia in the next four weeks. We first performed four different prediction models; Autoregressive Model, Moving Average, a combination of both (ARMA), and integrated ARMA (ARIMA), to determine the best model fit, and we found out that the ARIMA model outperformed the other models. The forecasting results showed that the trend in Saudi Arabia will continue growing and may reach up to 7668 new cases per day and over 127,129 cumulative daily cases in a matter of four weeks if stringent precautionary and control measures are not implemented to limit the spread of COVID-19. This indicates that the Umrah and Hajj Pilgrimages to the two holy cities of Mecca and Medina in Saudi Arabia that are supposedly scheduled to be performed by nearly 2 million Muslims in mid-July may be suspended. A set of extreme preventive and control measures are proposed in an effort to avoid such a situation.
Alzahrani Saleh I, Aljamaan Ibrahim A, Al-Fakih Ebrahim A

2020-Jun-08

COVID-19, Pandemic, SARS-Cov-2, Saudi Arabia, Time Series models, mARIMA Prediction Model

Public Health

Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States.

In International journal of environmental research and public health ; h5-index 73.0
Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country. Few studies have examined nationwide modeling of COVID-19 incidence in the United States particularly using machine-learning algorithms. Thus, we collected and prepared a database of 57 candidate explanatory variables to examine the performance of multilayer perceptron (MLP) neural network in predicting the cumulative COVID-19 incidence rates across the continental United States. Our results indicated that a single-hidden-layer MLP could explain almost 65% of the correlation with ground truth for the holdout samples. Sensitivity analysis conducted on this model showed that the age-adjusted mortality rates of ischemic heart disease, pancreatic cancer, and leukemia, together with two socioeconomic and environmental factors (median household income and total precipitation), are among the most substantial factors for predicting COVID-19 incidence rates. Moreover, results of the logistic regression model indicated that these variables could explain the presence/absence of the hotspots of disease incidence that were identified by Getis-Ord Gi* (p < 0.05) in a geographic information system environment. The findings may provide useful insights for public health decision makers regarding the influence of potential risk factors associated with the COVID-19 incidence at the county level.
Mollalo Abolfazl, Rivera Kiara M, Vahedi Behzad

2020-Jun-12

COVID-19 (Coronavirus), GIS, United States, artificial neural networks, multilayer perceptron

Cardiology

A predictive tool for identification of SARS-CoV-2 PCR-negative emergency department patients using routine test results.

In Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology

BACKGROUND : Testing for COVID-19 remains limited in the United States and across the world. Poor allocation of limited testing resources leads to misutilization of health system resources, which complementary rapid testing tools could ameliorate.

OBJECTIVE : To predict SARS-CoV-2 PCR positivity based on complete blood count components and patient sex.

STUDY DESIGN : A retrospective case-control design for collection of data and a logistic regression prediction model was used. Participants were emergency department patients > 18 years old who had concurrent complete blood counts and SARS-CoV-2 PCR testing. 33 confirmed SARS-CoV-2 PCR positive and 357 negative patients at Stanford Health Care were used for model training. Validation cohorts consisted of emergency department patients > 18 years old who had concurrent complete blood counts and SARS-CoV-2 PCR testing in Northern California (41 PCR positive, 495 PCR negative), Seattle, Washington (40 PCR positive, 306 PCR negative), Chicago, Illinois (245 PCR positive, 1015 PCR negative), and South Korea (9 PCR positive, 236 PCR negative).

RESULTS : A decision support tool that utilizes components of complete blood count and patient sex for prediction of SARS-CoV-2 PCR positivity demonstrated a C-statistic of 78 %, an optimized sensitivity of 93 %, and generalizability to other emergency department populations. By restricting PCR testing to predicted positive patients in a hypothetical scenario of 1000 patients requiring testing but testing resources limited to 60 % of patients, this tool would allow a 33 % increase in properly allocated resources.

CONCLUSIONS : A prediction tool based on complete blood count results can better allocate SARS-CoV-2 testing and other health care resources such as personal protective equipment during a pandemic surge.

Joshi Rohan P, Pejaver Vikas, Hammarlund Noah E, Sung Heungsup, Lee Seong Kyu, Furmanchuk Al’ona, Lee Hye-Young, Scott Gregory, Gombar Saurabh, Shah Nigam, Shen Sam, Nassiri Anna, Schneider Daniel, Ahmad Faraz S, Liebovitz David, Kho Abel, Mooney Sean, Pinsky Benjamin A, Banaei Niaz

2020-Jun-10

COVID-19, Machine learning, Prediction tool, Rapid testing, SARS-CoV-2

General

Repositioning of 8565 Existing Drugs for COVID-19.

In The journal of physical chemistry letters ; h5-index 129.0
The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over 5 million people and led to over 0.3 million deaths. Currently, there is no specific anti-SARS-CoV-2 medication. New drug discovery typically takes more than ten years. Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental dataset for SARS-CoV-2 or SARS-CoV main protease inhibitors. Based on this dataset, we develop validated machine learning models with relatively low root mean square error to screen 1553 FDA-approved drugs as well as other 7012 investigational or off-market drugs in DrugBank. We found that many existing drugs might be potentially potent to SARS-CoV-2. The druggability of many potent SARS-CoV-2 main protease inhibitors is analyzed. This work offers a foundation for further experimental studies of COVID-19 drug repositioning.
Gao Kaifu, Nguyen Duc D, Chen Jiahui, Wang Rui, Wei Guowei

2020-Jun-16

Public Health

Health Belief Model-based Deep Learning Classifiers for Classifying COVID-19 Social Media Content to Examine Public Behaviors towards Physical Distancing.

In JMIR public health and surveillance

BACKGROUND : Public health authorities (PHAs) have been recommending interventions such as physical distancing and face masks, to curtail the transmission of coronavirus disease (COVID-19) within the community. Public perceptions towards such interventions are to be identified so that PHAs can effectively address valid concerns. The Health Belief Model (HBM) has been used to characterize user-generated content from social media during previous outbreaks, to understand health behaviors of people.

OBJECTIVE : This study is aimed at developing and evaluating deep learning-based text classification models for classifying social media content posted during the COVID-19 outbreak, using the key four constructs of HBM. We specifically focus on content related to the physical distancing interventions put forth by PHAs. We intend to test the model with a real-world case study.

METHODS : The dataset for this study was prepared by analyzing Facebook comments which were posted by the public in response to the COVID-19 posts of three PHAs: Ministry of Health of Singapore (MOH), Centers for Disease Control and Prevention (CDC) and Public Health England (PHE). The comments made in the context of physical distancing were manually classified with a Yes/No flag for each of the four HBM constructs: perceived severity, perceived susceptibility, perceived barriers, and perceived benefits. Using a curated dataset of 16,752 comments, gated recurrent unit (GRU) based recurrent neural network (RNN) models were trained and validated for text classification. Accuracy and binary cross-entropy loss were used for evaluating the model while specificity, sensitivity and balanced accuracy were the test metrics used for evaluating the classification results in the MOH case study.

RESULTS : The HBM text classification models achieved mean accuracy rates of 0.92, 0.95, 0.91 and 0.94 for the constructs perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, respectively. In the testing case study with MOH FB comments, specificity was above 96% for all HBM constructs. Sensitivity was 94.3% and 90.9% for perceived severity and perceived benefits while for perceived susceptibility and perceived barriers, it was 79.6% and 81.5%. The classification models were able to accurately predict the trends in the prevalence of the constructs for the examined days in the case study.

CONCLUSIONS : The deep learning-based text classifiers developed in this study help in getting an understanding of the public perceptions towards physical distancing, using the four key constructs of HBM. Health officials can make use of the classification model to characterize health behaviors of public through the lens of social media. In future studies, we intend to extend the model for studying public perceptions on other important interventions of PHAs.

CLINICALTRIAL :

Sesagiri Raamkumar Aravind, Tan Soon Guan, Wee Hwee Lin

2020-Jun-13

General

[Recommendations for treatment of severe coronavirus disease 2019 based on critical care ultrasound].

In Zhonghua nei ke za zhi
Severe patients with COVID-19 are characterized by persistent lung damage, causing respiratory failure, secondary circulatory changes and multiple organ dysfunction after virus invasion. Because of its dynamic, real-time, non-invasive, repeatable and other advantages, critical ultrasonography can be widely used in the diagnosis, assessment and guidance of treatment for severe patients. Based on the recommendations of critical care experts from all over the country who fight against the epidemic in Wuhan, this article summarizes the guidelines for the treatment of COVID-19 based on critical ultrasonography, hoping to provide help for the treatment of severe patients. The recommendations mainly cover the following aspects: (1) lung ultrasound in patients with COVID-19 is mainly manifested by thickened and irregular pleural lines, different types of B-lines, shred signs, and other consolidation like dynamic air bronchogram; (2) Echocardiography may show right heart dysfunction, diffuse cardiac function enhancement, stress cardiomyopathy, diffuse cardiac depression and other multiple abnormalities; (3) Critical ultrasonography helps with initiating early treatment in the suspect patient, screening confirmed patients after intensive care unit admission, early assessment of sudden critical events, rapid grading assessment and treatment based on it; (4) Critical ultrasonography helps to quickly screen for the etiology of respiratory failure in patients with COVID-19, make oxygen therapeutic strategy, guide the implementation of lung protective ventilation, graded management and precise off-ventilator; (5) Critical ultrasonography is helpful for assessing the circulatory status of patients with COVID-19, finding chronic cardiopulmonary diseases and guiding extracorporeal membrane oxygenation management; (6) Critical ultrasonography Contributes to the management of organs besides based on cardiopulmonary oxygen transport; (7) Critical ultrasonography can help to improve the success of operation; (8) Critical ultrasonography can help to improve the safety and quality of nursing; (9) When performing critical ultrasonography for patients with COVID-19, it needs to implement three-level protection standard, pay attention to disinfect the machine and strictly obey the rules from nosocomial infection. (10) Telemedicine and artificial intelligence centered on critical ultrasonography may help to improve the efficiency of treatment for the patients with COVID-19.In the face of the global spread of the epidemic, all we can do is to share experience, build a defense line, We hope this recommendations can help COVID-19patients therapy.
Zhang L N, Yin M G, He W, Zhang H M, Liu L X, Zhu R, Wu J, Cai S H, Chao Y G, Wang X T

2020-Jun-15

Acute Respiratory Distress Syndrome, Coronavirus disease 2019, Critical ultrasonography, Protocol, Therapy

Radiology

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.

In Chaos, solitons, and fractals
Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.
Panwar Harsh, Gupta P K, Siddiqui Mohammad Khubeb, Morales-Menendez Ruben, Singh Vaishnavi

2020-Sep

COVID-19, Convolutional neural network (CNN), Deep learning, Detection, X-Rays, nCOVnet

Radiology

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114,698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testing kits and resources. The limited quantity of testing kits and increasing number of daily cases encouraged us to come up with a Deep Learning model that can aid radiologists and clinicians in detecting COVID-19 cases using chest X-rays.

METHODS : In this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model is based on Xception architecture pre-trained on ImageNet dataset and trained end-to-end on a dataset prepared by collecting COVID-19 and other chest pneumonia X-ray images from two different publically available databases.

RESULTS : CoroNet has been trained and tested on the prepared dataset and the experimental results show that our proposed model achieved an overall accuracy of 89.6%, and more importantly the precision and recall rate for COVID-19 cases are 93% and 98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal). For 3-class classification (COVID vs Pneumonia vs normal), the proposed model produced a classification accuracy of 95%. The preliminary results of this study look promising which can be further improved as more training data becomes available.

CONCLUSION : CoroNet achieved promising results on a small prepared dataset which indicates that given more data, the proposed model can achieve better results with minimum pre-processing of data. Overall, the proposed model substantially advances the current radiology based methodology and during COVID-19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of COVID-19 cases.

Khan Asif Iqbal, Shah Junaid Latief, Bhat Mohammad Mudasir

2020-Jun-05

COVID-19, Pneumonia viral, Convolutional Neural Network, Coronavirus, Deep learning, Pneumonia bacterial

General

COVID 19 diagnostic multiplicity and its role in community surveillance and control.

In Le infezioni in medicina
Diagnosis of persons exposed to/infected with severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) is central to controlling the global pandemic of COVID-19. Currently, several diagnostic modalities are available for COVID-19, each with its own pros and cons. Although there is a global consensus to increase the testing capacity, it is also essential to prudently utilize these tests to control the pandemic. In this paper, we have reviewed the current array of diagnostics for SARS-CoV-2, highlighted the gaps in current diagnostic modalities, and their role in community surveillance and control of the pandemic. The different modalities of COVID-19 diagnosis discussed are: clinical and radiological, molecular based (laboratory based and point-of-care), Immunoassay based (ELISA, rapid antigen and antibody detection tests) and digital diagnostics (artificial intelligence based algorithms). The role of rapid antigen/antibody detection tests in community surveillance has also been described here. These tests can be used to identify asymptomatic persons exposed to the virus and in community based seroprevalence surveys to assess the epidemiology of spread of the virus. However, there are few concerns about the accuracy of these tests which needs to evaluated beforehand.
Tripathi Satyendra C, Deshmukh Vishwajit, Patil Ashlesh, Tripathy Jaya Prasad

2020-Jun-01

Public Health

Examining the effect of social distancing on the compound growth rate of COVID-19 at the county level (United States) using statistical analyses and a random forest machine learning model.

In Public health

OBJECTIVES : The goal of the present work is to investigate trends among US counties and coronavirus disease 2019 (COVID-19) growth rates in relation to the existence of shelter-in-place (SIP) orders in that county.

STUDY DESIGN : This is a prospective cohort study.

METHODS : Compound growth rates were calculated using cumulative confirmed COVID-19 cases from January 21, 2020, to March 31, 2020, in all 3139 US counties. Compound growth was chosen as it gives a single number that can be used in machine learning to represent the speed of virus spread during defined time intervals. Statistical analyses and a random forest machine learning model were used to analyze the data for differences in counties with and without SIP orders.

RESULTS : Statistical analyses revealed that the March 16 presidential recommendation (limiting gatherings to ≤10 people) lowered the compound growth rate of COVID-19 for all counties in the US by 6.6%, and the counties that implemented SIP after March 16 had a further reduction of 7.8% compared with the counties that did not implement SIP after March 16. A random forest machine learning model was built to predict compound growth rate after a SIP order and was found to have an accuracy of 92.3%. The random forest found that population, longitude, and population per square mile were the most important features when predicting the effect of SIP.

CONCLUSIONS : SIP orders were found to be effective at reducing the growth rate of COVID-19 cases in the US. Counties with a large population or a high population density were found to benefit the most from a SIP order.

Cobb J S, Seale M A

2020-Apr-28

COVID-19, Machine learning, SARS-CoV-2, Shelter-in-place, Social distancing, Statistics

Radiology

Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China.

In European radiology ; h5-index 62.0

OBJECTIVES : To determine the patterns of chest computed tomography (CT) evolution according to disease severity in a large coronavirus disease 2019 (COVID-19) cohort in Jiangsu Province, China.

METHODS : This retrospective cohort study was conducted from January 10, 2020, to February 18, 2020. All patients diagnosed with COVID-19 in Jiangsu Province were included, retrospectively. Quantitative CT measurements of pulmonary opacities including volume, density, and location were extracted by deep learning algorithm. Dynamic evolution of these measurements was investigated from symptom onset (day 1) to beyond day 15. Comparison was made between severity groups.

RESULTS : A total of 484 patients (median age of 47 years, interquartile range 33-57) with 954 CT examinations were included, and each was assigned to one of the three groups: asymptomatic/mild (n = 63), moderate (n = 378), severe/critically ill (n = 43). Time series showed different evolution patterns of CT measurements in the groups. Following disease onset, posteroinferior subpleural area of the lung was the most common location for pulmonary opacities. Opacity volume continued to increase beyond 15 days in the severe/critically ill group, compared with peaking on days 13-15 in the moderate group. Asymptomatic/mild group had the lowest opacity volume which almost resolved after 15 days. The opacity density began to drop from day 10 to day 12 for moderately ill patients.

CONCLUSIONS : Volume, density, and location of the pulmonary opacity and their evolution on CT varied with disease severity in COVID-19. These findings are valuable in understanding the nature of the disease and monitoring the patient's condition during the course of illness.

KEY POINTS : • Volume, density, and location of the pulmonary opacity on CT change over time in COVID-19. • The evolution of CT appearance follows specific pattern, varying with disease severity.

Wang Yuan-Cheng, Luo Huanyuan, Liu Songqiao, Huang Shan, Zhou Zhen, Yu Qian, Zhang Shijun, Zhao Zhen, Yu Yizhou, Yang Yi, Wang Duolao, Ju Shenghong

2020-Jun-10

Coronavirus, Multidetector computed tomography, Viral pneumonia

Public Health

Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses.

In Proceedings of the National Academy of Sciences of the United States of America
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses an immediate, major threat to public health across the globe. Here we report an in-depth molecular analysis to reconstruct the evolutionary origins of the enhanced pathogenicity of SARS-CoV-2 and other coronaviruses that are severe human pathogens. Using integrated comparative genomics and machine learning techniques, we identify key genomic features that differentiate SARS-CoV-2 and the viruses behind the two previous deadly coronavirus outbreaks, SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), from less pathogenic coronaviruses. These features include enhancement of the nuclear localization signals in the nucleocapsid protein and distinct inserts in the spike glycoprotein that appear to be associated with high case fatality rate of these coronaviruses as well as the host switch from animals to humans. The identified features could be crucial contributors to coronavirus pathogenicity and possible targets for diagnostics, prognostication, and interventions.
Gussow Ayal B, Auslander Noam, Faure Guilhem, Wolf Yuri I, Zhang Feng, Koonin Eugene V

2020-Jun-10

COVID-19, coronaviruses, nucleocapsid, pathogenicity, spike protein

General

Navigating Chemical Space By Interfacing Generative Artificial Intelligence and Molecular Docking

bioRxiv Preprint
Here we report the testing and application of a simple, structure-aware framework to design target-specific screening libraries for drug development. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to rapidly explore chemical space conditioned on the unique physiochemical properties of the active site of a biomolecular target. As a proof-of-concept, we used our framework to construct a focused library for cyclin-dependent kinase type-2 (CDK2). We then used it to rapidly generate a library specific to the active site of the main protease (Mpro) of the SARS-CoV-2 virus, which causes COVID-19. By comparing approved and experimental drugs to compounds in our library, we also identified six drugs, namely, Naratriptan, Etryptamine, Panobinostat, Procainamide, Sertraline, and Lidamidine, as possible SARS-CoV-2 Mpro targeting compounds and, as such, potential drug repurposing candidates. To complement the open-science COVID-19 drug discovery initiatives, we make our SARS-CoV-2 Mpro library fully accessible to the research community (https://github.com/atfrank/SARS-CoV-2).
Xu, Z.; Wauchope, O. R.; Frank, A. T.

2020-06-11

General

COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature

ArXiv Preprint
The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. We also designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared clinical symptoms and clinical findings of COVID-19 vs. those of influenza to demonstrate the disease differences in the scientific publications. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR.
Yifan Peng, Yu-Xing Tang, Sungwon Lee, Yingying Zhu, Ronald M. Summers, Zhiyong Lu

2020-06-11

Public Health

Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: a stochastic simulation and machine learning study.

In American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Clinical decision-making in kidney transplantation (KT) during the COVID-19 pandemic is understandably a conundrum: both candidates and recipients may face increased acquisition risks and case fatality rates (CFRs). Given our poor understanding of these risks, many centers have paused or reduced KT activity, yet data to inform such decisions are lacking. To quantify the benefit/harm of KT in this context, we conducted a simulation study of immediate-KT vs delay-until-after-pandemic for different patient phenotypes under a variety of potential COVID-19 scenarios. A calculator was implemented (http://www.transplantmodels.com/covid_sim), and machine learning approaches were used to evaluate the important aspects of our modeling. Characteristics of the pandemic (acquisition risk, CFR) and length of delay (length of pandemic, waitlist priority when modeling deceased donor KT) had greatest influence on benefit/harm. In most scenarios of COVID-19 dynamics and patient characteristics, immediate-KT provided survival benefit; KT only began showing evidence of harm in scenarios where CFRs were substantially higher for KT recipients (e.g. ≥50% fatality) than for waitlist registrants. Our simulations suggest that KT could be beneficial in many centers if local resources allow, and our calculator can help identify patients who would benefit most. Furthermore, as the pandemic evolves, our calculator can update these predictions.
Massie Allan B, Boyarsky Brian J, Werbel William A, Bae Sunjae, Chow Eric Kh, Avery Robin K, Durand Christine M, Desai Niraj, Brennan Daniel, Garonzik-Wang Jacqueline M, Segev Dorry L

2020-Jun-09

Radiology

Radiology of COVID-19 - Imaging the pulmonary damage.

In JPMA. The Journal of the Pakistan Medical Association
A large part of the world is presently in the grip of the coronavirus disease (COVID-19) by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus), declared a pandemic in March 2020. This document is a brief commentary of the imaging modalities used in the screening, diagnosis and management of COVID-19 pneumonia. Chest x-rays, especially portable, still form a part of majority of official guidelines, with reports of the suggestive radiologic features. The potential of CT scan and ultrasound is also realised, with earlier detection rate. Typical radiologic findings of bilateral, asymmetrical, crazy-paved ground glass opacification, consolidation, reverse halo sign, opacities, progressing to fibrosis are well described for both the X-ray and CT scan. Atypical findings include airway changes, pleural effusion, pulmonary nodules and acute pulmonary embolism. Absence of lymphadenopathy, pleural effusion and pneumothorax is notable. The role of portable lung ultrasound, reported to be useful in emergency, is yet to be established in the guidelines. Disinfection of the equipment is a major concern. Governmental guidelines still advocate X-ray despite professional societies increasingly recommending CT scan.
Sohail Saba

2020-May

COVID-19, Radiology, Chest X-ray, CT scan, Ultrasound, Artificial intelligence, Sub-pleural consolidation, Ground glass opacification.\n

General

Morphological Cell Profiling of SARS-CoV-2 Infection Identifies Drug Repurposing Candidates for COVID-19

bioRxiv Preprint
The global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated disease COVID-19, requires therapeutic interventions that can be rapidly translated to clinical care. Unfortunately, traditional drug discovery methods have a >90% failure rate and can take 10-15 years from target identification to clinical use. In contrast, drug repurposing can significantly accelerate translation. We developed a quantitative high-throughput screen to identify efficacious single agents and combination therapies against SARS-CoV-2. Quantitative high-content morphological profiling was coupled with an AI-based machine learning strategy to classify features of cells for infection and stress. This assay detected multiple antiviral mechanisms of action (MOA), including inhibition of viral entry, propagation, and modulation of host cellular responses. From a library of 1,425 FDA-approved compounds and clinical candidates, we identified 16 dose-responsive compounds with antiviral effects. In particular, we discovered that lactoferrin is an effective inhibitor of SARS-CoV-2 infection with an IC50 of 308 nM and that it potentiates the efficacy of both remdesivir and hydroxychloroquine. Lactoferrin also stimulates an antiviral host cell response and retains inhibitory activity in iPSC-derived alveolar epithelial cells, a model for the primary site of infection. Given its safety profile in humans, these data suggest that lactoferrin is a readily translatable therapeutic adjunct for COVID-19. Additionally, several commonly prescribed drugs were found to exacerbate viral infection and warrant clinical investigation. We conclude that morphological profiling for drug repurposing is an effective strategy for the selection and optimization of drugs and drug combinations as viable therapeutic options for COVID-19 pandemic and other emerging infectious diseases.
Mirabelli, C.; Wotring, J. W.; Zhang, C. J.; McCarty, S. M.; Fursmidt, R.; Frum, T.; Kadambi, N. S.; Amin, A. T.; O’Meara, T. R.; Pretto-Kernahan, C. D.; Spence, J. R.; Huang, J.; Alysandratos, K. D.; Kotton, D. N.; Handelman, S. K.; Wobus, C. E.; Weatherwax, K. J.; Mashour, G. A.; O’Meara, M. J.; Sexton, J. Z.

2020-06-10

General

Global Data Science Project for COVID-19 Summary Report

ArXiv Preprint
This paper aims at providing the summary of the Global Data Science Project (GDSC) for COVID-19. as on May 31 2020. COVID-19 has largely impacted on our societies through both direct and indirect effects transmitted by the policy measures to counter the spread of viruses. We quantitatively analysed the multifaceted impacts of the COVID-19 pandemic on our societies including people's mobility, health, and social behaviour changes. People's mobility has changed significantly due to the implementation of travel restriction and quarantine measurements. Indeed, the physical distance has widened at international (cross-border), national and regional level. At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March. In particular, the number of flights connecting Europe dropped drastically in mid of March after the United States announced travel restrictions to Europe and the EU and participating countries agreed to close borders, at 84 percent decline compared to March 10th. Similarly, we examined the impacts of quarantine measures in the major city: Tokyo (Japan), New York City (the United States), and Barcelona (Spain). Within all three cities, we found the significant decline in traffic volume. We also identified the increased concern for mental health through the analysis of posts on social networking services such as Twitter and Instagram. Notably, in the beginning of April 2020, the number of post with #depression on Instagram doubled, which might reflect the rise in mental health awareness among Instagram users. Besides, we identified the changes in a wide range of people's social behaviors, as well as economic impacts through the analysis of Instagram data and primary survey data.
Dario Garcia-Gasulla, Sergio Alvarez Napagao, Irene Li, Hiroshi Maruyama, Hiroki Kanezashi, Raquel P’erez-Arnal, Kunihiko Miyoshi, Euma Ishii, Keita Suzuki, Sayaka Shiba, Mariko Kurokawa, Yuta Kanzawa, Naomi Nakagawa, Masatoshi Hanai, Yixin Li, Tianxiao Li

2020-06-10

General

Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2.

In bioRxiv : the preprint server for biology
We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations, and we find for SARS-CoV-2 that it provides superior predicted display of viral epitopes by MHC class I and MHC class II molecules over populations when compared to other candidate vaccines. Our method is robust to idiosyncratic errors in the prediction of MHC peptide display and considers target population HLA haplotype frequencies during optimization. To minimize clinical development time our methods validate vaccines with multiple peptide presentation algorithms to increase the probability that a vaccine will be effective. We optimize an objective function that is based on the presentation likelihood of a diverse set of vaccine peptides conditioned on a target population HLA haplotype distribution and expected epitope drift. We produce separate peptide formulations for MHC class I loci (HLA-A, HLA-B, and HLA-C) and class II loci (HLA-DP, HLA-DQ, and HLA-DR) to permit signal sequence based cell compartment targeting using nucleic acid based vaccine platforms. Our SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA hits on average in an individual (≥ 1 peptide 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our MHC class II vaccine formulations provide 90.17% predicted coverage with at least five vaccine peptide-HLA hits on average in an individual with all peptides having observed mutation probability ≤ 0.001. We evaluate 29 previously published peptide vaccine designs with our evaluation tool with the requirement of having at least five vaccine peptide-HLA hits per individual, and they have a predicted maximum of 58.51% MHC class I coverage and 71.65% MHC class II coverage given haplotype based analysis. We provide an open source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts.
Liu Ge, Carter Brandon, Bricken Trenton, Jain Siddhartha, Viard Mathias, Carrington Mary, Gifford David K

2020-May-17

General

Potentially highly potent drugs for 2019-nCoV.

In bioRxiv : the preprint server for biology
The World Health Organization (WHO) has declared the 2019 novel coronavirus (2019-nCoV) infection outbreak a global health emergency. Currently, there is no effective anti-2019-nCoV medication. The sequence identity of the 3CL proteases of 2019-nCoV and SARS is 96%, which provides a sound foundation for structural-based drug repositioning (SBDR). Based on a SARS 3CL protease X-ray crystal structure, we construct a 3D homology structure of 2019-nCoV 3CL protease. Based on this structure and existing experimental datasets for SARS 3CL protease inhibitors, we develop an SBDR model based on machine learning and mathematics to screen 1465 drugs in the DrugBank that have been approved by the U.S. Food and Drug Administration (FDA). We found that many FDA approved drugs are potentially highly potent to 2019-nCoV.
Nguyen Duc Duy, Gao Kaifu, Chen Jiahui, Wang Rui, Wei Guo-Wei

2020-Feb-13

General

COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning.

In bioRxiv : the preprint server for biology
To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane protein, have been tested for vaccine development against SARS and MERS. We further used the Vaxign reverse vaccinology tool and the newly developed Vaxign-ML machine learning tool to predict COVID-19 vaccine candidates. The N protein was found to be conserved in the more pathogenic strains (SARS/MERS/COVID-19), but not in the other human coronaviruses that mostly cause mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10) were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and linear B-cell epitopes localized in specific locations and functional domains of the protein. Our predicted vaccine targets provide new strategies for effective and safe COVID-19 vaccine development.
Ong Edison, Wong Mei U, Huffman Anthony, He Yongqun

2020-Mar-21

General

Machine intelligence design of 2019-nCoV drugs.

In bioRxiv : the preprint server for biology
Wuhan coronavirus, called 2019-nCoV, is a newly emerged virus that infected more than 9692 people and leads to more than 213 fatalities by January 30, 2020. Currently, there is no effective treatment for this epidemic. However, the viral protease of a coronavirus is well-known to be essential for its replication and thus is an effective drug target. Fortunately, the sequence identity of the 2019-nCoV protease and that of severe-acute respiratory syndrome virus (SARS-CoV) is as high as 96.1%. We show that the protease inhibitor binding sites of 2019-nCoV and SARS-CoV are almost identical, which means all potential anti-SARS-CoV chemotherapies are also potential 2019-nCoV drugs. Here, we report a family of potential 2019-nCoV drugs generated by a machine intelligence-based generative network complex (GNC). The potential effectiveness of treating 2019-nCoV by using some existing HIV drugs is also analyzed.
Gao Kaifu, Nguyen Duc Duy, Wang Rui, Wei Guo-Wei

2020-Feb-04

2019-nCoV, SARS-CoV, deep learning

Public Health

Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses.

In bioRxiv : the preprint server for biology
SARS-CoV-2 poses an immediate, major threat to public health across the globe. Here we report an in-depth molecular analysis to reconstruct the evolutionary origins of the enhanced pathogenicity of SARS-CoV-2 and other coronaviruses that are severe human pathogens. Using integrated comparative genomics and machine learning techniques, we identify key genomic features that differentiate SARS-CoV-2 and the viruses behind the two previous deadly coronavirus outbreaks, SARS-CoV and MERS-CoV, from less pathogenic coronaviruses. These features include enhancement of the nuclear localization signals in the nucleocapsid protein and distinct inserts in the spike glycoprotein that appear to be associated with high case fatality rate of these coronaviruses as well as the host switch from animals to humans. The identified features could be crucial elements of coronavirus pathogenicity and possible targets for diagnostics, prognostication and interventions.
Gussow Ayal B, Auslander Noam, Faure Guilhem, Wolf Yuri I, Zhang Feng, Koonin Eugene V

2020-Apr-09

General

COVID-19 Digital Health Innovation Policy: A Portal to Alternative Futures in the Making.

In Omics : a journal of integrative biology
"The pandemic is a portal." In the words of the novelist scholar Arundhati Roy, the COVID-19 pandemic is not merely an epic calamity. It has opened up a new space, a portal, to rethink everything, for example, in how we live, work, produce scientific knowledge, provide health care, and relate to others, be they humans or nonhuman animals in planetary ecosystems. Meanwhile, as the intensity of the pandemic escalates, digital health tools such as the Internet of Things (IoT), biosensors, and artificial intelligence (AI) are being deployed to address the twin goals of social distancing and health care in a "no touch" emergency state. Permanent integration of digital technologies into every aspect of post-pandemic civic life-health care, disease tracking, education, work, and beyond-is considered by governments and technology actors around the world. Although digital transformation of health care and industry are in the works, we ought to ensure that digital transformation does not degenerate into "digitalism," which we define here as an unchecked and misguided belief on extreme digital connectivity without considering the attendant adverse repercussions on science, human rights, and everyday practices of democracy. Indeed, the current shrinking of the critically informed public policy space amid a devastating pandemic raises principled questions on the broader and long-term impacts that digital technologies will have on democratic governance of planetary health and society. To this end, a wide range of uncertainties-technical, biological, temporal, spatial, and political-is on the COVID-19 pandemic horizon. This calls for astute and anticipatory innovation policies to steer the health sciences and services toward democratic ends. In this article, we describe new and critically informed approaches to democratize COVID-19 digital health innovation policy, especially when the facts are uncertain, the stakes are high, and decisions are urgent, as they often are in the course of a pandemic. In addition, we introduce a potential remedy to democratize pandemic innovation policy, the concept of "epistemic competence," so as to check the frames and framings of the pandemic innovation policy juggernaut and the attendant power asymmetries. We suggest that if epistemic competence, and attention to not only scientific knowledge but also its framing are broadly appreciated, they can help reduce the disparity between the enormous technical progress and investments made in digital health versus our currently inadequate understanding of the societal dimensions of emerging technologies such as AI, IoT, and extreme digital connectivity on the planet.
Bayram Mustafa, Springer Simon, Garvey Colin K, Özdemir Vural

2020-Jun-08

COVID-19, critical policy studies, digital health, digital transformation, digitalism, futures, innovation policy, risk and uncertainty

General

Diagnostic methods and potential portable biosensors for coronavirus disease 2019.

In Biosensors & bioelectronics
Timely detection and diagnosis are urgently needed to guide epidemiological measures, infection control, antiviral treatment, and vaccine research. In this review, biomarkers/indicators for diagnosis of coronavirus disease 2019 (COVID-19) or detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the environment are summarized and discussed. It is concluded that the detection methods targeting antibodies are not suitable for screening of early and asymptomatic cases since most patients had an antibody response at about 10 days after onset of symptoms. However, antibody detection methods can be combined with quantitative real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) to significantly improve the sensitivity and specificity of diagnosis, and boost vaccine research. Fast, sensitive and accurate detection methods targeting antigens need to be developed urgently. Various specimens for diagnosis or detection are compared and analyzed. Among them, deep throat saliva and induced sputum are desired for RT-qPCR test or other early detection technologies. Chest computerized tomography (CT) scan, RT-qPCR, lateral flow immunochromatographic strip (LFICS) for diagnosis of COVID-19 are summarized and compared. Specially, potential electrochemical (EC) biosensor, surface enhanced Raman scattering (SERS)-based biosensor, field-effect transistor (FET)-based biosensor, surface plasmon resonance (SPR)-based biosensor and artificial intelligence (AI) assisted diagnosis of COVID-19 are emphasized. Finally, some commercialized portable detection device, current challenges and future directions are discussed.
Cui Feiyun, Zhou H Susan

2020-Jun-02

AI assisted diagnosis, Biosensors for virus detection, COVID-19, Lateral flow immunochromatographic strip, SARS-CoV-2

General

Significance of clinical phenomes of patients with COVID-19 infection: A learning from 3795 patients in 80 reports.

In Clinical and translational medicine
A new coronavirus SARS-CoV-2 has caused outbreaks in multiple countries and the number of cases is rapidly increasing through human-to-human transmission. Clinical phenomes of patients with SARS-CoV-2 infection are critical in distinguishing it from other respiratory infections. The extent and characteristics of those phenomes varied depending on the severities of the infection, for example, beginning with fever or a mild cough, progressed with signs of pneumonia, and worsened with severe or even fatal respiratory difficulty in acute respiratory distress syndrome. We summarized clinical phenomes of 3795 patients with COVID-19 based on 80 published reports from the onset of outbreak to March 2020 to emphasize the importance and specificity of those phenomes in diagnosis and treatment of infection, and evaluate the impact on medical services. The data show that the incidence of male patients was higher than that of females and the level of C-reaction protein was increased as well as most patients' imaging included ground-glass opacity. Clinical phenomes of SARS-CoV-2 infection were compared with those of SARS-CoV and MERS-CoV infections. There is an urgent need to develop an artificial intelligence-based machine learning capacity to analyze and integrate radiomics- or imaging-based, patient-based, clinician-based, and molecular measurements-based data to fight the outbreak of COVID-19 and enable more efficient responses to unknown infections in future.
Zhang Linlin, Wang Diane C, Huang Qihong, Wang Xiangdong

2020-Jan

COVID-19, acute lung injury, clinical phenome, lung

General

Contextualizing covid-19 spread: a county level analysis, urban versus rural, and implications for preparing for the next wave.

In medRxiv : the preprint server for health sciences
Paraphrasing [Morano and Holt, 2017], contextual determinants of health including social, environmental, healthcare and others, are a so-called deck of cards one is dealt. The ability to modify health outcomes varies then based upon how one's hand is played. It is thus of great interest to understand how these determinants associate with the emerging pandemic covid-19. To this end, we conducted a deep-dive analysis into this problem using a recently curated public dataset on covid-19 that connects infection spread over time to a rich collection of contextual determinants for all counties of the U.S and Washington, D.C. Using random forest machine learning methodology, we identified a relevant constellation of contextual factors of disease spread which manifest differently for urban and rural counties. The findings also have clear implications for better preparing for the next wave of disease.
Rao J Sunil, Zhang Hang, Mantero Alejandro

2020-Apr-29

General

A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter.

In medRxiv : the preprint server for health sciences
The rapidly evolving outbreak of COVID-19 presents challenges for actively monitoring its spread. In this study, we assessed a social media mining approach for automatically analyzing the chronological and geographical distribution of users in the United States reporting personal information related to COVID-19 on Twitter. The results suggest that our natural language processing and machine learning framework could help provide an early indication of the spread of COVID-19.
Klein Ari, Magge Arjun, O’Connor Karen, Cai Haitao, Weissenbacher Davy, Gonzalez-Hernandez Graciela

2020-Apr-22

General

Acute Kidney Injury in Hospitalized Patients with COVID-19.

In medRxiv : the preprint server for health sciences

IMPORTANCE : Preliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described.

OBJECTIVE : To provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients.

DESIGN : Observational, retrospective study.

SETTING : Admitted to hospital between February 27 and April 15, 2020.

PARTICIPANTS : Patients aged ≥18 years with laboratory confirmed COVID-19 Exposures: AKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline). Main Outcomes and Measures: Frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation.

RESULTS : A total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test.

CONCLUSIONS AND RELEVANCE : AKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation.

Chan Lili, Chaudhary Kumardeep, Saha Aparna, Chauhan Kinsuk, Vaid Akhil, Baweja Mukta, Campbell Kirk, Chun Nicholas, Chung Miriam, Deshpande Priya, Farouk Samira S, Kaufman Lewis, Kim Tonia, Koncicki Holly, Lapsia Vijay, Leisman Staci, Lu Emily, Meliambro Kristin, Menon Madhav C, Rein Joshua L, Sharma Shuchita, Tokita Joji, Uribarri Jaime, Vassalotti Joseph A, Winston Jonathan, Mathews Kusum S, Zhao Shan, Paranjpe Ishan, Somani Sulaiman, Richter Felix, Do Ron, Miotto Riccardo, Lala Anuradha, Kia Arash, Timsina Prem, Li Li, Danieletto Matteo, Golden Eddye, Glowe Patricia, Zweig Micol, Singh Manbir, Freeman Robert, Chen Rong, Nestler Eric, Narula Jagat, Just Allan C, Horowitz Carol, Aberg Judith, Loos Ruth J F, Cho Judy, Fayad Zahi, Cordon-Cardo Carlos, Schadt Eric, Levin Matthew A, Reich David L, Fuster Valentin, Murphy Barbara, He John Cijiang, Charney Alexander W, Bottinger Erwin P, Glicksberg Benjamin S, Coca Steven G, Nadkarni Girish N

2020-May-08

General

Artificial intelligence-enabled rapid diagnosis of COVID-19 patients.

In medRxiv : the preprint server for health sciences
For diagnosis of COVID-19, a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to two days to complete, serial testing may be required to rule out the possibility of false negative results, and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of COVID-19 patients. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiologic findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history, and laboratory testing to rapidly diagnose COVID-19 positive patients. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an AUC of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of RT-PCR positive COVID-19 patients who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.
Mei Xueyan, Lee Hao-Chih, Diao Kaiyue, Huang Mingqian, Lin Bin, Liu Chenyu, Xie Zongyu, Ma Yixuan, Robson Philip M, Chung Michael, Bernheim Adam, Mani Venkatesh, Calcagno Claudia, Li Kunwei, Li Shaolin, Shan Hong, Lv Jian, Zhao Tongtong, Xia Junli, Long Qihua, Steinberger Sharon, Jacobi Adam, Deyer Timothy, Luksza Marta, Liu Fang, Little Brent P, Fayad Zahi A, Yang Yang

2020-Apr-17

General

Acute lung injury in patients with COVID-19 infection.

In Clinical and translational medicine
During the 2020 Spring Festival in China, the outbreak of a novel coronavirus, named COVID-19 by WHO, brought on a worldwide panic. According to the clinical data of infected patients, radiologic evidence of lung edema is common and deserves clinical attention. Lung edema is a manifestation of acute lung injury (ALI) and may progress to hypoxemia and potentially acute respiratory distress syndrome (ARDS). Patients diagnosed with ARDS have poorer prognosis and potentially higher mortality. Although no effective treatment is formally approved for COVID-19 infection, support of ventilation with oxygen therapy and sometimes mechanical ventilation is often required. Treatment with systemic and/or local glucocorticoids might be helpful to alleviate the pulmonary inflammation and edema, which may decrease the development and/or consequences of ARDS. In this article, we focus on the lung edema and ALI of patients with this widely transmitted COVID-19 infection in order to provide clinical indications and potential therapeutic targets for clinicians and researchers.
Li Liyang, Huang Qihong, Wang Diane C, Ingbar David H, Wang Xiangdong

2020-Jan

ARDS, COVID-19, lung edema

General

Interpretable Artificial Intelligence for COVID-19 Diagnosis from Chest CT Reveals Specificity of Ground-Glass Opacities.

In medRxiv : the preprint server for health sciences
Background The use of CT imaging enhanced by artificial intelligence to effectively diagnose COVID-19, instead of or in addition to reverse transcription-polymerase chain reaction (RT-PCR), can improve widespread COVID-19 detection and resource allocation. Methods 904 axial lung window CT slices from 338 patients in 17 countries were collected and labeled. The data included 606 images from COVID-19 positive patients (confirmed via RT-PCR), 224 images of a variety of other pulmonary diseases including viral pneumonias, and 74 images of normal patients. We developed, trained, validated, and tested an object detection model which detects features in three categories: ground-glass opacities (GGOs) for COVID-19, GGOs for non-COVID-19 diseases, and features that are inconsistent with a COVID-19 diagnosis. These collected features are passed into an interpretable decision tree model to make a suggested diagnosis. Results On an independent test of 219 images from COVID-19 positive, a variety of pneumonia, and healthy patients, the model predicted COVID-19 diagnoses with an accuracy of 96.80 % (95% confidence interval [CI], 96.75 to 96.86) , AUC-ROC of 0.9664 (95% CI, 0.9659 to 0.9671) , sensitivity of 98.33% (95% CI, 98.29 to 98.40) , precision of 95.93% (95% CI, 95.83 to 95.99), and specificity of 94.95% (95% CI, 94.84 to 95.05). On an independent test of 34 images from asymptomatic COVID-19 positive patients, our model achieved an accuracy of 97.06% (95% CI, 96.81 to 97.06) and a sensitivity of 96.97% (95% CI, 96.71 to 96.97). Similarly high performance was also obtained for out-of-sample countries, and no significant performance difference was obtained between genders. Conclusion We present an interpretable artificial intelligence CT analysis tool to diagnose COVID-19 in both symptomatic and asymptomatic patients. Further, our model is able to differentiate COVID-19 GGOs from similar pathologies suggesting that GGOs can be disease-specific.
Warman Anmol, Warman Pranav, Sharma Ayushman, Parikh Puja, Warman Roshan, Viswanadhan Narayan, Chen Lu, Mohapatra Subhra, Mohapatra Shyam, Sapiro Guillermo

2020-May-18

General

Clinical predictors of COVID-19 mortality.

In medRxiv : the preprint server for health sciences

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic has affected over millions of individuals and caused hundreds of thousands of deaths worldwide. It can be difficult to accurately predict mortality among COVID-19 patients presenting with a spectrum of complications, hindering the prognostication and management of the disease.

METHODS : We applied machine learning techniques to clinical data from a large cohort of 5,051 COVID-19 patients treated at the Mount Sinai Health System in New York City, the global COVID-19 epicenter, to predict mortality. Predictors were designed to classify patients into Deceased or Alive mortality classes and were evaluated in terms of the area under the receiver operating characteristic (ROC) curve (AUC score).

FINDINGS : Using a development cohort (n=3,841) and a systematic machine learning framework, we identified a COVID-19 mortality predictor that demonstrated high accuracy (AUC=0.91) when applied to test sets of retrospective (n= 961) and prospective (n=249) patients. This mortality predictor was based on five clinical features: age, minimum O2 saturation during encounter, type of patient encounter (inpatient vs. various types of outpatient and telehealth encounters), hydroxychloroquine use, and maximum body temperature.

INTERPRETATION : An accurate and parsimonious COVID-19 mortality predictor based on five features may have utility in clinical settings to guide the management and prognostication of patients affected by this disease.

Yadaw Arjun S, Li Yan-Chak, Bose Sonali, Iyengar Ravi, Bunyavanich Supinda, Pandey Gaurav

2020-May-22

General

COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images.

In medRxiv : the preprint server for health sciences
Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections make the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we were able to successfully implement our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.
Khuzani Abolfazl Zargari, Heidari Morteza, Shariati S Ali

2020-May-18

General

Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK.

In medRxiv : the preprint server for health sciences
The rapidly evolving COVID-19 pandemic presents challenges for actively monitoring its transmission. In this study, we extend a social media mining approach used in the US to automatically identify personal reports of COVID-19 on Twitter in England, UK. The findings indicate that natural language processing and machine learning framework could help provide an early indication of the chronological and geographical distribution of COVID-19 in England.
Golder Su, Klein Ari, Magge Arjun, O’Connor Karen, Cai Haitao, Weissenbacher Davy

2020-May-08

Radiology

A collaborative online AI engine for CT-based COVID-19 diagnosis.

In medRxiv : the preprint server for health sciences
Artificial intelligence can potentially provide a substantial role in streamlining chest computed tomography (CT) diagnosis of COVID-19 patients. However, several critical hurdles have impeded the development of robust AI model, which include deficiency, isolation, and heterogeneity of CT data generated from diverse institutions. These bring about lack of generalization of AI model and therefore prevent it from applications in clinical practices. To overcome this, we proposed a federated learning-based Unified CT-COVID AI Diagnostic Initiative (UCADI, http://www.ai-ct-covid.team/), a decentralized architecture where the AI model is distributed to and executed at each host institution with the data sources or client ends for training and inferencing without sharing individual patient data. Specifically, we firstly developed an initial AI CT model based on data collected from three Tongji hospitals in Wuhan. After model evaluation, we found that the initial model can identify COVID from Tongji CT test data at near radiologist-level (97.5% sensitivity) but performed worse when it was tested on COVID cases from Wuhan Union Hospital (72% sensitivity), indicating a lack of model generalization. Next, we used the publicly available UCADI framework to build a federated model which integrated COVID CT cases from the Tongji hospitals and Wuhan Union hospital (WU) without transferring the WU data. The federated model not only performed similarly on Tongji test data but improved the detection sensitivity (98%) on WU test cases. The UCADI framework will allow participants worldwide to use and contribute to the model, to deliver a real-world, globally built and validated clinic CT-COVID AI tool. This effort directly supports the United Nations Sustainable Development Goals' number 3, Good Health and Well-Being, and allows sharing and transferring of knowledge to fight this devastating disease around the world.
Xu Yongchao, Ma Liya, Yang Fan, Chen Yanyan, Ma Ke, Yang Jiehua, Yang Xian, Chen Yaobing, Shu Chang, Fan Ziwei, Gan Jiefeng, Zou Xinyu, Huang Renhao, Zhang Changzheng, Liu Xiaowu, Tu Dandan, Xu Chuou, Zhang Wenqing, Yang Dehua, Wang Ming-Wei, Wang Xi, Xie Xiaoliang, Leng Hongxiang, Holalkere Nagaraj, Halin Neil J, Kamel Ihab Roushdy, Wu Jia, Peng Xuehua, Wang Xiang, Shao Jianbo, Mongkolwat Pattanasak, Zhang Jianjun, Rubin Daniel L, Wang Guoping, Zheng Chuangsheng, Li Zhen, Bai Xiang, Xia Tian

2020-May-19

General

Upper airway gene expression differentiates COVID-19 from other acute respiratory illnesses and reveals suppression of innate immune responses by SARS-CoV-2.

In medRxiv : the preprint server for health sciences
We studied the host transcriptional response to SARS-CoV-2 by performing metagenomic sequencing of upper airway samples in 238 patients with COVID-19, other viral or non-viral acute respiratory illnesses (ARIs). Compared to other viral ARIs, COVID-19 was characterized by a diminished innate immune response, with reduced expression of genes involved in toll-like receptor and interleukin signaling, chemokine binding, neutrophil degranulation and interactions with lymphoid cells. Patients with COVID-19 also exhibited significantly reduced proportions of neutrophils and macrophages, and increased proportions of goblet, dendritic and B-cells, compared to other viral ARIs. Using machine learning, we built 26-, 10- and 3-gene classifiers that differentiated COVID-19 from other acute respiratory illnesses with AUCs of 0.980, 0.950 and 0.871, respectively. Classifier performance was stable at low viral loads, suggesting utility in settings where direct detection of viral nucleic acid may be unsuccessful. Taken together, our results illuminate unique aspects of the host transcriptional response to SARS-CoV-2 in comparison to other respiratory viruses and demonstrate the feasibility of COVID-19 diagnostics based on patient gene expression.
Mick Eran, Kamm Jack, Pisco Angela Oliveira, Ratnasiri Kalani, Babik Jennifer M, Calfee Carolyn S, Castaneda Gloria, DeRisi Joseph L, Detweiler Angela M, Hao Samantha, Kangelaris Kirsten N, Kumar G Renuka, Li Lucy M, Mann Sabrina A, Neff Norma, Prasad Priya A, Serpa Paula Hayakawa, Shah Sachin J, Spottiswoode Natasha, Tan Michelle, Christenson Stephanie A, Kistler Amy, Langelier Charles

2020-May-19

Radiology

Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection.

In medRxiv : the preprint server for health sciences
The novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused a pandemic resulting in over 2.7 million infected individuals and over 190,000 deaths and growing. Respiratory disorders in COVID-19 caused by the virus commonly present as viral pneumonia-like opacities in chest X-ray images which are used as an adjunct to the reverse transcription-polymerase chain reaction test for confirmation and evaluating disease progression. The surge places high demand on medical services including radiology expertise. However, there is a dearth of sufficient training data for developing image-based automated decision support tools to alleviate radiological burden. We address this insufficiency by expanding training data distribution through use of weakly-labeled images pooled from publicly available CXR collections showing pneumonia-related opacities. We use the images in a stage-wise, strategic approach and train convolutional neural network-based algorithms to detect COVID-19 infections in CXRs. It is observed that weakly-labeled data augmentation improves performance with the baseline test data compared to non-augmented training by expanding the learned feature space to encompass variability in the unseen test distribution to enhance inter-class discrimination, reduce intra-class similarity and generalization error. Augmentation with COVID-19 CXRs from individual collections significantly improves performance compared to baseline non-augmented training and weakly-labeled augmentation toward detecting COVID-19 like viral pneumonia in the publicly available COVID-19 CXR collections. This underscores the fact that COVID-19 CXRs have a distinct pattern and hence distribution, unlike non-COVID-19 viral pneumonia and other infectious agents.
Rajaraman Sivaramakrishnan, Antani Sameer

2020-May-08

COVID-19, augmentation, chest-X-rays, convolutional neural network, deep learning, localization, pneumonia

Public Health

Impacts of Social and Economic Factors on the Transmission of Coronavirus Disease 2019 (COVID-19) in China.

In medRxiv : the preprint server for health sciences
This paper examines the role of various socioeconomic factors in mediating the local and cross-city transmissions of the novel coronavirus 2019 (COVID-19) in China. We implement a machine learning approach to select instrumental variables that strongly predict virus transmission among the rich exogenous weather characteristics. Our 2SLS estimates show that the stringent quarantine, massive lockdown and other public health measures imposed in late January significantly reduced the transmission rate of COVID-19. By early February, the virus spread had been contained. While many socioeconomic factors mediate the virus spread, a robust government response since late January played a determinant role in the containment of the virus. We also demonstrate that the actual population flow from the outbreak source poses a higher risk to the destination than other factors such as geographic proximity and similarity in economic conditions. The results have rich implications for ongoing global efforts in containment of COVID-19.
Qiu Yun, Chen Xi, Shi Wei

2020-Mar-17

2019 novel coronavirus, C23, I12, I18, transmission

Pathology

A single-cell RNA expression map of human coronavirus entry factors.

In bioRxiv : the preprint server for biology
To predict the tropism of human coronaviruses, we profile 28 SARS-CoV-2 and coronavirus-associated receptors and factors (SCARFs) using single-cell RNA-sequencing data from a wide range of healthy human tissues. SCARFs include cellular factors both facilitating and restricting viral entry. Among adult organs, enterocytes and goblet cells of the small intestine and colon, kidney proximal tubule cells, and gallbladder basal cells appear most permissive to SARS-CoV-2, consistent with clinical data. Our analysis also suggests alternate entry paths for SARS-CoV-2 infection of the lung, central nervous system, and heart. We predict spermatogonial cells and prostate endocrine cells, but not ovarian cells, to be highly permissive to SARS-CoV-2, suggesting male-specific vulnerabilities. Early stages of embryonic and placental development show a moderate risk of infection. The nasal epithelium looks like another battleground, characterized by high expression of both promoting and restricting factors and a potential age-dependent shift in SCARF expression. Lastly, SCARF expression appears broadly conserved across human, chimpanzee and macaque organs examined. Our study establishes an important resource for investigations of coronavirus biology and pathology.
Singh Manvendra, Bansal Vikas, Feschotte Cédric

2020-May-17

COVID-19, Coronaviruses, Restriction factors, SARS-CoV-2, Viral receptors, scRNA-seq

General

RNA-GPS Predicts SARS-CoV-2 RNA Localization to Host Mitochondria and Nucleolus.

In bioRxiv : the preprint server for biology
The SARS-CoV-2 coronavirus is driving a global pandemic, but its biological mechanisms are less well understood. SARS-CoV-2 is an RNA virus whose multiple genomic and subgenomic RNA (sgRNA) transcripts hijack the host cell's machinery, located across distinct cytotopic locations. Subcellular localization of its viral RNA could play important roles in viral replication and host antiviral immune response. Here we perform computational modeling of SARS-CoV-2 viral RNA localization across eight subcellular neighborhoods. We compare hundreds of SARS-CoV-2 genomes to the human transcriptome and other coronaviruses and perform systematic sub-sequence analyses to identify the responsible signals. Using state-of-the-art machine learning models, we predict that the SARS-CoV-2 RNA genome and all sgRNAs are enriched in the host mitochondrial matrix and nucleolus. The 5' and 3' viral untranslated regions possess the strongest and most distinct localization signals. We discuss the mitochondrial localization signal in relation to the formation of double-membrane vesicles, a critical stage in the coronavirus life cycle. Our computational analysis serves as a hypothesis generation tool to suggest models for SARS-CoV-2 biology and inform experimental efforts to combat the virus.
Wu Kevin, Zou James, Chang Howard Y

2020-Apr-28

Surgery

Risk of a second wave of Covid-19 infections: using artificial intelligence to investigate stringency of physical distancing policies in North America.

In International orthopaedics ; h5-index 43.0

PURPOSE : Accurately forecasting the occurrence of future covid-19-related cases across relaxed (Sweden) and stringent (USA and Canada) policy contexts has a renewed sense of urgency. Moreover, there is a need for a multidimensional county-level approach to monitor the second wave of covid-19 in the USA.

METHOD : We use an artificial intelligence framework based on timeline of policy interventions that triangulated results based on the three approaches-Bayesian susceptible-infected-recovered (SIR), Kalman filter, and machine learning.

RESULTS : Our findings suggest three important insights. First, the effective growth rate of covid-19 infections dropped in response to the approximate dates of key policy interventions. We find that the change points for spreading rates approximately coincide with the timelines of policy interventions across respective countries. Second, forecasted trend until mid-June in the USA was downward trending, stable, and linear. Sweden is likely to be heading in the other direction. That is, Sweden's forecasted trend until mid-June appears to be non-linear and upward trending. Canada appears to fall somewhere in the middle-the trend for the same period is flat. Third, a Kalman filter based robustness check indicates that by mid-June the USA will likely have close to two million virus cases, while Sweden will likely have over 44,000 covid-19 cases.

CONCLUSION : We show that drop in effective growth rate of covid-19 infections was sharper in the case of stringent policies (USA and Canada) but was more gradual in the case of relaxed policy (Sweden). Our study exhorts policy makers to take these results into account as they consider the implications of relaxing lockdown measures.

Vaid Shashank, McAdie Aaron, Kremer Ran, Khanduja Vikas, Bhandari Mohit

2020-Jun-05

Artificial intelligence, Bayesian (SIR), Covid-19, Kalman filter, Machine learning

General

Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis.

In In vivo (Athens, Greece)

BACKGROUND/AIM : To evaluate the research trends in coronavirus disease (COVID-19).

MATERIALS AND METHODS : A bibliometric analysis was performed using a machine learning bibliometric methodology. Information regarding publication outputs, countries, institutions, journals, keywords, funding and citation counts was retrieved from Scopus database.

RESULTS : A total of 1883 eligible papers were returned. An exponential increase in the COVID-19 publications occurred in the last months. As expected, China produced the majority of articles, followed by the United States of America, the United Kingdom and Italy. There is greater collaboration between highly contributing authors and institutions. The "BMJ" published the highest number of papers (n=129) and "The Lancet" had the most citations (n=1439). The most ubiquitous topic was COVID-19 clinical features.

CONCLUSION : This bibliometric analysis presents the most influential references related to COVID-19 during this time and could be useful to improve understanding and management of COVID-19.

DE Felice Francesca, Polimeni Antonella

2020-Jun

COVID-19, bibliometric analysis, coronavirus, machine learning, management

General

COVID-19: Closing the Psychological Treatment Gap during the Pandemic, a Protocol for Implementation and Evaluation of Text4Hope (a Supportive Text Message Program).

In JMIR research protocols ; h5-index 26.0

BACKGROUND : Coronavirus disease 2019 (COVID-19) has spread globally with far-reaching, significant and unprecedented impacts on health and way of life. Threats to mental health, psychological safety and well being are now emerging, increasing the impact of this virus on world health. Providing support for these challenges is difficult because of very high numbers of people requiring support in the context of a need to maintain physical distancing. This protocol describes use of text messaging (Text4Hope) as a convenient, cost-effective, and accessible population-level mental health intervention. This program is evidence-based, with prior research supporting good outcomes and high user satisfaction.

OBJECTIVE : The project goal is to implement a program of daily supportive text messaging (Text4Hope) to reduce distress related to the COVID-19 crisis initially amongst Canadians. Prevalence of stress, anxiety and depressive symptoms, demographic correlates of the same, and the outcomes of the Text4Hope intervention in mitigating distress will be evaluated.

METHODS : Self-administered, anonymous, online questionnaires will be used to assess stress (Perceived Stress Scale), anxiety (GAD-7), and depressive symptoms (PHQ-9). Data will be collected at baseline (onset of text messaging), at program midpoint (6-weeks), and end (12-weeks).

RESULTS : Data analysis will include parametric and non-parametric techniques, focusing on primary outcomes (i.e., stress, anxiety, depressive symptoms) and metrics of use, including number of subscribers and user satisfaction. Given the large size of the data set, machine learning and data-mining methods will also be used.

CONCLUSIONS : This COVID-19 project will provide key information regarding prevalence rates of stress, anxiety, and depressive symptoms during the pandemic, demographic correlates of distress, and outcome data related to this scalable population-level intervention. Information from this study will be valuable for practitioners, as useful for informing policy and decision-making regarding psychological interventions during the pandemic.

CLINICALTRIAL :

Agyapong Vincent Israel Opoku, Hrabok Marianne, Vuong Wesley, Gusnowski April, Shalaby Reham, Mrklas Kelly, Li Daniel, Urichuck Liana, Snaterse Mark, Surood Shireen, Cao Bo, Li Xin-Min, Greiner Russ, Greenshaw Andrew James

2020-Jun-04

Public Health

COVID-19 Case-initiated contact tracing using anonymous notification techniques.

In JMIR mHealth and uHealth
We propose a concept of contact notification tool for assisting tracing contacts who are exposure to confirmed COVID-19 cases, which is simple and affordable for countries with limited health manpower and high-tech means.
Cheng Weibin, Hao Chun

2020-Jun-04

General

A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2.

In Informatics in medicine unlocked
In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains 180 X-ray images that belong to persons infected with COVID-19, and we attempted to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have an unbalanced dataset (fewer cases of COVID-19 along with more cases from other classes). We also propose a neural network that is a concatenation of the Xception and ResNet50V2 networks. This network achieved the best accuracy by utilizing multiple features extracted by two robust networks. For evaluating our network, we have tested it on 11302 images to report the actual accuracy achievable in real circumstances. The average accuracy of the proposed network for detecting COVID-19 cases is 99.50%, and the overall average accuracy for all classes is 91.4%.
Rahimzadeh Mohammad, Attar Abolfazl

2020

COVID-19, Chest X-ray images, Convolutional neural networks, Coronavirus, Deep feature extraction, Deep learning, Transfer learning

Public Health

Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil.

In Chaos, solitons, and fractals
The new Coronavirus (COVID-19) is an emerging disease responsible for infecting millions of people since the first notification until nowadays. Developing efficient short-term forecasting models allow forecasting the number of future cases. In this context, it is possible to develop strategic planning in the public health system to avoid deaths. In this paper, autoregressive integrated moving average (ARIMA), cubist regression (CUBIST), random forest (RF), ridge regression (RIDGE), support vector regression (SVR), and stacking-ensemble learning are evaluated in the task of time series forecasting with one, three, and six-days ahead the COVID-19 cumulative confirmed cases in ten Brazilian states with a high daily incidence. In the stacking-ensemble learning approach, the CUBIST regression, RF, RIDGE, and SVR models are adopted as base-learners and Gaussian process (GP) as meta-learner. The models' effectiveness is evaluated based on the improvement index, mean absolute error, and symmetric mean absolute percentage error criteria. In most of the cases, the SVR and stacking-ensemble learning reach a better performance regarding adopted criteria than compared models. In general, the developed models can generate accurate forecasting, achieving errors in a range of 0.87%-3.51%, 1.02%-5.63%, and 0.95%-6.90% in one, three, and six-days-ahead, respectively. The ranking of models, from the best to the worst regarding accuracy, in all scenarios is SVR, stacking-ensemble learning, ARIMA, CUBIST, RIDGE, and RF models. The use of evaluated models is recommended to forecasting and monitor the ongoing growth of COVID-19 cases, once these models can assist the managers in the decision-making support systems.
Ribeiro Matheus Henrique Dal Molin, da Silva Ramon Gomes, Mariani Viviana Cocco, Coelho Leandro Dos Santos

2020-Jun

ARIMA, COVID-19, Decision-making, Forecasting, Machine learning, Time-series

General

Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks.

In Process safety and environmental protection : transactions of the Institution of Chemical Engineers, Part B
SARS-CoV-2 (COVID-19) is a new Coronavirus, with first reported human infections in late 2019. COVID-19 has been officially declared as a universal pandemic by the World Health Organization (WHO). The epidemiological characteristics of COVID-2019 have not been completely understood yet. More than 200,000 persons were killed during this epidemic (till 1 May 2020). Therefore, developing forecasting models to predict the spread of that epidemic is a critical issue. In this study, statistical and artificial intelligence based approaches have been proposed to model and forecast the prevalence of this epidemic in Egypt. These approaches are autoregressive integrated moving average (ARIMA) and nonlinear autoregressive artificial neural networks (NARANN). The official data reported by The Egyptian Ministry of Health and Population of COVID-19 cases in the period between 1 March and 10 May 2020 was used to train the models. The forecasted cases showed a good agreement with officially reported cases. The obtained results of this study may help the Egyptian decision-makers to put short-term future plans to face this epidemic.
Saba Amal I, Elsheikh Ammar H

2020-Sep

COVID-19, Egypt, Forecasting, Neural networks

General

A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases using X-ray Images.

In Current medical imaging

BACKGROUND : Scanning patient's lungs to detect a Coronavirus 2019 (COVID-19) may lead to similar imaging with other chest diseases that strongly requires a multidisciplinary approach to confirm the diagnosis. There are only few works targeted pathological x-ray images. Most of the works targeted only single disease detection which is not good enough. Some works have provided for all classes however the results suffer due to lack of data for rare classes and data unbalancing problem.

METHODS : Due to arise of COVID-19 virus medical facilities of many countries are overwhelmed and there is a need of intelligent system to detect it. There have been few works regarding detection of the coronavirus but there are many cases where it can be misclassified as some techniques do not provide any goodness if it can only identify type of diseases and ignore the rest. This work is a deep learning-based model to distinguish between cases of COVID-19 from other chest diseases which is need of today.

RESULTS : A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provide effective analysis of chest related diseases with respect to age and gender. Our model achieves 87% accuracy in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results.

CONCLUSION : If the gap in identifying of all viral pneumonias is not filled with effective automation of chest disease detection the healthcare industry may have to bear unfavorable circumstances.

Albahli Saleh

2020-Jun-04

Chest diseases, Coronavirus, Deep learning, Inception-V3.\n, ResNet-152, X-ray

General

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

ArXiv Preprint
Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that policy. However, in many real-world applications such as health, education, dialogue agents, and robotics, the cost or potential risk of deploying a new data-collection policy is high, to the point that it can become prohibitive to update the data-collection policy more than a few times during learning. With this view, we propose a novel concept of deployment efficiency, measuring the number of distinct data-collection policies that are used during policy learning. We observe that na\"ively applying existing model-free offline RL algorithms recursively does not lead to a practical deployment-efficient and sample-efficient algorithm. We propose a novel model-based algorithm, Behavior-Regularized Model-ENsemble (BREMEN) that can effectively optimize a policy offline using 10-20 times fewer data than prior works. Furthermore, the recursive application of BREMEN is able to achieve impressive deployment efficiency while maintaining the same or better sample efficiency, learning successful policies from scratch on simulated robotic environments with only 5-10 deployments, compared to typical values of hundreds to millions in standard RL baselines.
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu

2020-06-05

General

Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients.

In Journal of clinical medicine

OBJECTIVES : Approximately 20-30% of patients with COVID-19 require hospitalization, and 5-12% may require critical care in an intensive care unit (ICU). A rapid surge in cases of severe COVID-19 will lead to a corresponding surge in demand for ICU care. Because of constraints on resources, frontline healthcare workers may be unable to provide the frequent monitoring and assessment required for all patients at high risk of clinical deterioration. We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers' efforts and help hospitals plan their flow of operations.

METHODS : A retrospective cohort was comprised of non-ICU COVID-19 admissions at a large acute care health system between 26 February and 18 April 2020. Time series data, including vital signs, nursing assessments, laboratory data, and electrocardiograms, were used as input variables for training a random forest (RF) model. The cohort was randomly split (70:30) into training and test sets. The RF model was trained using 10-fold cross-validation on the training set, and its predictive performance on the test set was then evaluated.

RESULTS : The cohort consisted of 1987 unique patients diagnosed with COVID-19 and admitted to non-ICU units of the hospital. The median time to ICU transfer was 2.45 days from the time of admission. Compared to actual admissions, the tool had 72.8% (95% CI: 63.2-81.1%) sensitivity, 76.3% (95% CI: 74.7-77.9%) specificity, 76.2% (95% CI: 74.6-77.7%) accuracy, and 79.9% (95% CI: 75.2-84.6%) area under the receiver operating characteristics curve.

CONCLUSIONS : A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. This tool could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19.

Cheng Fu-Yuan, Joshi Himanshu, Tandon Pranai, Freeman Robert, Reich David L, Mazumdar Madhu, Kohli-Seth Roopa, Levin Matthew, Timsina Prem, Kia Arash

2020-Jun-01

COVID-19, critical care, intensive care units, random forest, supervised machine learning

General

Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning

ArXiv Preprint
Monitoring bridge health using the vibrations of drive-by vehicles has various benefits, such as low cost and no need for direct installation or on-site maintenance of equipment on the bridge. However, many such approaches require labeled data from every bridge, which is expensive and time-consuming, if not impossible, to obtain. This is further exacerbated by having multiple diagnostic tasks, such as damage quantification and localization. One way to address this issue is to directly apply the supervised model trained for one bridge to other bridges, although this may significantly reduce the accuracy because of distribution mismatch between different bridges'data. To alleviate these problems, we introduce a transfer learning framework using domain-adversarial training and multi-task learning to detect, localize and quantify damage. Specifically, we train a deep network in an adversarial way to learn features that are 1) sensitive to damage and 2) invariant to different bridges. In addition, to improve the error propagation from one task to the next, our framework learns shared features for all the tasks using multi-task learning. We evaluate our framework using lab-scale experiments with two different bridges. On average, our framework achieves 94%, 97% and 84% accuracy for damage detection, localization and quantification, respectively. within one damage severity level.
Jingxiao Liu, Mario Bergés, Jacobo Bielak, Hae Young Noh

2020-06-05

General

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera.

In Cell ; h5-index 250.0
Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.
Shen Bo, Yi Xiao, Sun Yaoting, Bi Xiaojie, Du Juping, Zhang Chao, Quan Sheng, Zhang Fangfei, Sun Rui, Qian Liujia, Ge Weigang, Liu Wei, Liang Shuang, Chen Hao, Zhang Ying, Li Jun, Xu Jiaqin, He Zebao, Chen Baofu, Wang Jing, Yan Haixi, Zheng Yufen, Wang Donglian, Zhu Jiansheng, Kong Ziqing, Kang Zhouyang, Liang Xiao, Ding Xuan, Ruan Guan, Xiang Nan, Cai Xue, Gao Huanhuan, Li Lu, Li Sainan, Xiao Qi, Lu Tian, Zhu Yi, Liu Huafen, Chen Haixiao, Guo Tiannan

2020-May-28

COVID-19, metabolomics, proteomics, serum, severity

Public Health

Machine Learning to Detect Self-Reporting of Symptoms, Testing Access and Recovery Associated with COVID-19 on Twitter: A Retrospective Big-Data Infoveillance Study.

In JMIR public health and surveillance

BACKGROUND : The coronavirus (COVID-19) pandemic is a global health emergency with over 6 million cases worldwide as of the beginning of June 2020. Importantly, the pandemic is historic in scope and precedent given its emergence in an increasing digital era. Importantly, there have been concerns about the accuracy of COVID-19 case counts due to issues such as lack of access to testing and difficulty in measuring recoveries.

OBJECTIVE : The aims of this study were to detect and characterize user-generated conversations that could be associated with COVID-19-related symptoms, experiences with access to testing, and mentions of disease recovery using an unsupervised machine learning approach.

METHODS : Tweets were collected from the Twitter public streaming API from March 3-20, 2020, filtered for general COVID-19-related keywords and then further filtered for terms that could be related to COVID-19 symptoms as self-reported by users. Tweets were analyzed using an unsupervised machine learning approach called the biterm topic model (BTM), where groups of tweets containing the same word-related themes were separated into topic clusters that included conversations about symptoms, testing, and recovery. Tweets in these clusters were then extracted and manually annotated for content analysis and also assessed for their statistical and geographic characteristics.

RESULTS : A total of 4,492,954 tweets were collected that contained terms that could be related to COVID-19 symptoms. After using BTM to identify relevant topic clusters and removing duplicate tweets, we identified a total of 3,465 (<1%) tweets that included user generated conversations about experiences that users associated with possible COVID-19 symptoms and other disease experiences. These tweets were grouped into five main categories including first and second-hand reports of symptoms, symptom reporting concurrent with lack of testing, discussion of recovery, confirmation of negative COVID-19 diagnosis after receiving testing, and users recalling symptoms and questioning whether they might have been previously infected with COVID-19. Co-occurrence of tweets for these themes were statistically significant for users reporting symptoms with lack of testing and with discussion of recovery. Sixty-three percent (n=1112) of tweets were located in the United States.

CONCLUSIONS : This study used unsupervised machine learning for the purposes of characterizing self-reporting of symptoms, experiences with testing, and mentions of recovery related to COVID-19. Many users reported symptoms they thought were related to COVID-19, but also were not able to get tested to confirm their concerns. In the absence of testing availability and confirmation, accurate case estimations for this period of the outbreak may never be known. Future studies should continue to explore the utility of infoveillance approaches to estimate COVID-19 disease severity.

CLINICALTRIAL :

Mackey Tim, Purushothaman Vidya, Li Jiawei, Shah Neal, Nali Matthew, Bardier Cortni, Liang Bryan, Cai Mingxiang, Cuomo Raphael

2020-Jun-03

General

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov.

In Interdisciplinary sciences, computational life sciences
A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein-ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, D-Sorbitol, D-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time.
Zhang Haiping, Saravanan Konda Mani, Yang Yang, Hossain Md Tofazzal, Li Junxin, Ren Xiaohu, Pan Yi, Wei Yanjie

2020-Jun-01

3C-like protease, Coronavirus, Deep learning, Drug screening, Homology modeling

General

Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds.

In Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Telemedicine could be a key to control the world-wide disruptive and spreading novel coronavirus disease (COVID-19) pandemic. The COVID-19 virus directly targets the lungs, leading to pneumonia-like symptoms and shortness of breath with life-threatening consequences. Despite the fact that self-quarantine and social distancing are indispensable during the pandemic, the procedure for testing COVID-19 contraction is conventionally available through nasal swabs, saliva test kits, and blood work at healthcare settings. Therefore, devising personalized self-testing kits for COVID-19 virus and other similar viruses is heavily admired. Many e-health initiatives have been made possible by the advent of smartphones with embedded software, hardware, high-performance computing, and connectivity capabilities. A careful review of breathing sounds and their implications in identifying breathing complications suggests that the breathing sounds of COVID-19 contracted users may reveal certain acoustic signal patterns, which is worth investigating. To this end, acquiring respiratory data solely from breathing sounds fed to the smartphone's microphone strikes as a very appealing resolution. The acquired breathing sounds can be analyzed using advanced signal processing and analysis in tandem with new deep/machine learning and pattern recognition techniques to separate the breathing phases, estimate the lung volume, oxygenation, and to further classify the breathing data input into healthy or unhealthy cases. The ideas presented have the potential to be deployed as self-test breathing monitoring apps for the ongoing global COVID-19 pandemic, where users can check their breathing sound pattern frequently through the app.
Faezipour Miad, Abuzneid Abdelshakour

2020-Jun-02

e-health, home health monitoring, sensor technology, technology, telemedicine

Radiology

Weakly Labeled Data Augmentation for Deep Learning: A Study on COVID-19 Detection in Chest X-Rays.

In Diagnostics (Basel, Switzerland)
The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic resulting in over 2.7 million infected individuals and over 190,000 deaths and growing. Assertions in the literature suggest that respiratory disorders due to COVID-19 commonly present with pneumonia-like symptoms which are radiologically confirmed as opacities. Radiology serves as an adjunct to the reverse transcription-polymerase chain reaction test for confirmation and evaluating disease progression. While computed tomography (CT) imaging is more specific than chest X-rays (CXR), its use is limited due to cross-contamination concerns. CXR imaging is commonly used in high-demand situations, placing a significant burden on radiology services. The use of artificial intelligence (AI) has been suggested to alleviate this burden. However, there is a dearth of sufficient training data for developing image-based AI tools. We propose increasing training data for recognizing COVID-19 pneumonia opacities using weakly labeled data augmentation. This follows from a hypothesis that the COVID-19 manifestation would be similar to that caused by other viral pathogens affecting the lungs. We expand the training data distribution for supervised learning through the use of weakly labeled CXR images, automatically pooled from publicly available pneumonia datasets, to classify them into those with bacterial or viral pneumonia opacities. Next, we use these selected images in a stage-wise, strategic approach to train convolutional neural network-based algorithms and compare against those trained with non-augmented data. Weakly labeled data augmentation expands the learned feature space in an attempt to encompass variability in unseen test distributions, enhance inter-class discrimination, and reduce the generalization error. Empirical evaluations demonstrate that simple weakly labeled data augmentation (Acc: 0.5555 and Acc: 0.6536) is better than baseline non-augmented training (Acc: 0.2885 and Acc: 0.5028) in identifying COVID-19 manifestations as viral pneumonia. Interestingly, adding COVID-19 CXRs to simple weakly labeled augmented training data significantly improves the performance (Acc: 0.7095 and Acc: 0.8889), suggesting that COVID-19, though viral in origin, creates a uniquely different presentation in CXRs compared with other viral pneumonia manifestations.
Rajaraman Sivaramakrishnan, Antani Sameer

2020-May-30

COVID-19, augmentation, chest X-rays, convolutional neural network, deep learning, localization, pneumonia

General

Analysis of RNA sequences of 3636 SARS-CoV-2 collected from 55 countries reveals selective sweep of one virus type.

In The Indian journal of medical research

Background & objectives : SARS-CoV-2 (Severe acute respiratory syndrome coronavirus-2) is evolving with the progression of the pandemic. This study was aimed to investigate the diversity and evolution of the coronavirus SARS-CoV-2 with progression of the pandemic over time and to identify similarities and differences of viral diversity and evolution across geographical regions (countries).

Methods : Publicly available data on type definitions based on whole-genome sequences of the SARS-CoV-2 sampled during December and March 2020 from 3636 infected patients spread over 55 countries were collected. Phylodynamic analyses were performed and the temporal and spatial evolution of the virus was examined.

Results : It was found that (i) temporal variation in frequencies of types of the coronavirus was significant; ancestral viruses of type O were replaced by evolved viruses belonging to type A2a; (ii) spatial variation was not significant; with the spread of SARS-CoV-2, the dominant virus was the A2a type virus in every geographical region; (iii) within a geographical region, there was significant micro-level variation in the frequencies of the different viral types, and (iv) the evolved coronavirus of type A2a swept rapidly across all continents.

Interpretation & conclusions : SARS-CoV-2 belonging to the A2a type possesses a non-synomymous variant (D614G) that possibly eases the entry of the virus into the lung cells of the host. This may be the reason why the A2a type has an advantage to infect and survive and as a result has rapidly swept all geographical regions. Therefore, large-scale sequencing of coronavirus genomes and, as required, of host genomes should be undertaken in India to identify regional and ethnic variation in viral composition and its interaction with host genomes. Further, careful collection of clinical and immunological data of the host can provide deep learning in relation to infection and transmission of the types of coronavirus genomes.

Biswas Nidhan K, Majumder Partha P

2020-May-30

Host genome interaction - phylogeny - RNA sequence - SARS-CoV-2 - viral type coronavirus