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Surgery Surgery

Endothelial Injury and Glycocalyx Degradation in Critically Ill Coronavirus Disease 2019 Patients: Implications for Microvascular Platelet Aggregation.

In Critical care explorations

Objectives : Coronavirus disease 2019 is caused by the novel severe acute respiratory syndrome coronavirus 2 virus. Patients admitted to the ICU suffer from microvascular thrombosis, which may contribute to mortality. Our aim was to profile plasma thrombotic factors and endothelial injury markers in critically ill coronavirus disease 2019 ICU patients to help understand their thrombotic mechanisms.

Design : Daily blood coagulation and thrombotic factor profiling with immunoassays and in vitro experiments on human pulmonary microvascular endothelial cells.

Setting : Tertiary care ICU and academic laboratory.

Subjects : All patients admitted to the ICU suspected of being infected with severe acute respiratory syndrome coronavirus 2, using standardized hospital screening methodologies, had daily blood samples collected until testing was confirmed coronavirus disease 2019 negative on either ICU day 3 or ICU day 7 if the patient was coronavirus disease 2019 positive.

Interventions : None.

Measurement and Main Results : Age- and sex-matched healthy control subjects and ICU patients that were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well balanced with the exception that coronavirus disease 2019 positive patients were more likely than coronavirus disease 2019 negative patients to suffer bilateral pneumonia. Mortality rate for coronavirus disease 2019 positive ICU patients was 40%. Compared with healthy control subjects, coronavirus disease 2019 positive patients had higher plasma von Willebrand factor (p < 0.001) and glycocalyx-degradation products (chondroitin sulfate and syndecan-1; p < 0.01). When compared with coronavirus disease 2019 negative patients, coronavirus disease 2019 positive patients had persistently higher soluble P-selectin, hyaluronic acid, and syndecan-1 (p < 0.05), particularly on ICU day 3 and thereafter. Thrombosis profiling on ICU days 1-3 predicted coronavirus disease 2019 status with 85% accuracy and patient mortality with 86% accuracy. Surface hyaluronic acid removal from human pulmonary microvascular endothelial cells with hyaluronidase treatment resulted in depressed nitric oxide, an instigating mechanism for platelet adhesion to the microvascular endothelium.

Conclusions : Thrombosis profiling identified endothelial activation and glycocalyx degradation in coronavirus disease 2019 positive patients. Our data suggest that medications to protect and/or restore the endothelial glycocalyx, as well as platelet inhibitors, should be considered for further study.

Fraser Douglas D, Patterson Eric K, Slessarev Marat, Gill Sean E, Martin Claudio, Daley Mark, Miller Michael R, Patel Maitray A, Dos Santos Claudia C, Bosma Karen J, O’Gorman David B, Cepinskas Gediminas

2020-Sep

coronavirus disease 2019, endothelial injury, glycocalyx degradation, platelet adhesion, thrombosis

General General

Genetic Characterization of the Local Pirenaica Cattle for Parentage and Traceability Purposes.

In Animals : an open access journal from MDPI

Pirenaica is the most important autochthonous cattle breed within the Protected Geographic Indication (PGI) beef quality label in the Basque region, in northern Spain. The short tandem repeats (STRs) are powerful markers to elucidate forensic cases and traceability across the agri-food sector. The main objective of the present work was to study the phylogenetic relationships of Pirenaica cattle and other breeds typically raised in the region and provide the minimum number of STR markers for parentage and traceability purposes. The 30-STR panel recommended by the International Society of Animal Genetics-Food and Agriculture Organization of the United Nations (ISAG-FAO) was compared against other commercial STR panels. The 30-STR panel showed a combined matching probability of 1.89 × 10-25 and a power of exclusion for duos of 0.99998. However, commercial STR panels showed a limited efficiency for a reliable parentage analysis in Pirenaica, and at least a 21-STR panel is needed to reach a power of exclusion of 0.9999. Machine-learning analysis also demonstrated a 95% accuracy in assignments selecting the markers with the highest FST in Pirenaica individuals. Overall, the present study shows the genetic characterization of Pirenaica and its phylogeny compared with other breeds typically raised in the Basque region. Finally, a 21-STR panel with the highest FST markers is proposed for a confident parentage analysis and high traceability.

Gamarra David, Taniguchi Masaaki, Aldai Noelia, Arakawa Aisaku, Lopez-Oceja Andres, de Pancorbo Marian M

2020-Sep-05

Blonde d´Aquitaine, Holstein-Friesian, Limousin, Salers, Terreña, assignment test, identity, microsatellite, multiplex PCR, structure

General General

Is it safe to lift COVID-19 travel bans? The Newfoundland story.

In Computational mechanics

A key strategy to prevent a local outbreak during the COVID-19 pandemic is to restrict incoming travel. Once a region has successfully contained the disease, it becomes critical to decide when and how to reopen the borders. Here we explore the impact of border reopening for the example of Newfoundland and Labrador, a Canadian province that has enjoyed no new cases since late April, 2020. We combine a network epidemiology model with machine learning to infer parameters and predict the COVID-19 dynamics upon partial and total airport reopening, with perfect and imperfect quarantine conditions. Our study suggests that upon full reopening, every other day, a new COVID-19 case would enter the province. Under the current conditions, banning air travel from outside Canada is more efficient in managing the pandemic than fully reopening and quarantining 95% of the incoming population. Our study provides quantitative insights of the efficacy of travel restrictions and can inform political decision making in the controversy of reopening.

Linka Kevin, Rahman Proton, Goriely Alain, Kuhl Ellen

2020-Aug-29

COVID-19, Epidemiology, Machine learning, Reproduction number, SEIR model

General General

COVID CT-Net: Predicting Covid-19 From Chest CT Images Using Attentional Convolutional Network

ArXiv Preprint

The novel corona-virus disease (COVID-19) pandemic has caused a major outbreak in more than 200 countries around the world, leading to a severe impact on the health and life of many people globally. As of Aug 25th of 2020, more than 20 million people are infected, and more than 800,000 death are reported. Computed Tomography (CT) images can be used as a as an alternative to the time-consuming "reverse transcription polymerase chain reaction (RT-PCR)" test, to detect COVID-19. In this work we developed a deep learning framework to predict COVID-19 from CT images. We propose to use an attentional convolution network, which can focus on the infected areas of chest, enabling it to perform a more accurate prediction. We trained our model on a dataset of more than 2000 CT images, and report its performance in terms of various popular metrics, such as sensitivity, specificity, area under the curve, and also precision-recall curve, and achieve very promising results. We also provide a visualization of the attention maps of the model for several test images, and show that our model is attending to the infected regions as intended. In addition to developing a machine learning modeling framework, we also provide the manual annotation of the potentionally infected regions of chest, with the help of a board-certified radiologist, and make that publicly available for other researchers.

Shakib Yazdani, Shervin Minaee, Rahele Kafieh, Narges Saeedizadeh, Milan Sonka

2020-09-10

oncology Oncology

Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis.

In European thyroid journal

Background : Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists.

Objective : To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules.

Methods : PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460).

Results : Nineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning- and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79-0.92], specificity 0.85 [95% CI 0.77-0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91-56.20]; deep learning: sensitivity 0.89 [95% CI 0.81-0.93], specificity 0.84 [95% CI 0.75-0.90], DOR 40.87 [95% CI 18.13-92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78-0.93] vs. 0.87 [95% CI 0.85-0.89], specificity 0.85 [95% CI 0.76-0.91] vs. 0.87 [95% CI 0.81-0.91], DOR 40.12 [95% CI 15.58-103.33] vs. DOR 44.88 [95% CI 30.71-65.57]).

Conclusions : The CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.

Xu Lei, Gao Junling, Wang Quan, Yin Jichao, Yu Pengfei, Bai Bin, Pei Ruixia, Chen Dingzhang, Yang Guochun, Wang Shiqi, Wan Mingxi

2020-Jul

Artificial intelligence, Thyroid nodule, Ultrasonography

General General

Diabetic Kidney Disease: Challenges, Advances, and Opportunities.

In Kidney diseases (Basel, Switzerland)

Background : Diabetic kidney disease (DKD) is the most common cause of the end-stage renal disease (ESRD). Regardless of intensive treatments with hyperglycemic control, blood pressure control, and the use of renin-angiotensin system blockades, the prevalence of DKD remains high. Recent studies suggest that the spectrum of DKD has been changed and many progresses have been made to develop new treatments for DKD. Therefore, it is time to perform a systemic review on the new developments in the field of DKD.

Summary : Although the classic clinical presentation of DKD is characterized by a slow progression from microalbuminuria to macroalbuminuria and by a hyperfiltration at the early stage and progressive decline of renal function at the late stage, recent epidemiological studies suggest that DKD patients have a variety of clinical presentations and progression rates to ESRD. Some DKD patients have a decline in renal function without albuminuria but display prominent vascular and interstitial fibrosis on renal histology. DKD patients are more susceptible to acute kidney injury, which might contribute to the interstitial fibrosis. A large portion of type 2 diabetic patients with albuminuria could have overlapping nondiabetic glomerular disease, and therefore, kidney biopsy is required for differential diagnosis for these patients. Only a small portion of DKD patients eventually progress to end-stage renal failure. However, we do not have sensitive and specific biomarkers to identify these high-risk patients. Genetic factors that have a strong association with DKD progression have not been identified yet. A combination of circulating tumor necrosis factor receptor (TNFR)1, TNFR2, and kidney injury molecular 1 provides predictive value for DKD progression. Artificial intelligence could enhance the predictive values for DKD progression by combining the clinical parameters and biological markers. Sodium-glucose co-transporter-2 inhibitors should be added to the new standard care of DKD patients. Several promising new drugs are in clinical trials.

Key Messages : Over last years, our understanding of DKD has been much improved and new treatments to halt the progression of DKD are coming. However, better diagnostic tools, predictive markers, and treatment options are still urgently needed to help us to better manage these patients with this detrimental disease.

Chen Ya, Lee Kyung, Ni Zhaohui, He John Cijiang

2020-Jul

Diabetic kidney disease, Inflammation, Nonproteinuric pathway, Progression, Treatment