Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

Internal Medicine Internal Medicine

COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States.

In Molecular psychiatry ; h5-index 103.0

The global pandemic of COVID-19 is colliding with the epidemic of opioid use disorders (OUD) and other substance use disorders (SUD) in the United States (US). Currently, there is limited data on risks, disparity, and outcomes for COVID-19 in individuals suffering from SUD. This is a retrospective case-control study of electronic health records (EHRs) data of 73,099,850 unique patients, of whom 12,030 had a diagnosis of COVID-19. Patients with a recent diagnosis of SUD (within past year) were at significantly increased risk for COVID-19 (adjusted odds ratio or AOR = 8.699 [8.411-8.997], P < 10-30), an effect that was strongest for individuals with OUD (AOR = 10.244 [9.107-11.524], P < 10-30), followed by individuals with tobacco use disorder (TUD) (AOR = 8.222 ([7.925-8.530], P < 10-30). Compared to patients without SUD, patients with SUD had significantly higher prevalence of chronic kidney, liver, lung diseases, cardiovascular diseases, type 2 diabetes, obesity and cancer. Among patients with recent diagnosis of SUD, African Americans had significantly higher risk of COVID-19 than Caucasians (AOR = 2.173 [2.01-2.349], P < 10-30), with strongest effect for OUD (AOR = 4.162 [3.13-5.533], P < 10-25). COVID-19 patients with SUD had significantly worse outcomes (death: 9.6%, hospitalization: 41.0%) than general COVID-19 patients (death: 6.6%, hospitalization: 30.1%) and African Americans with COVID-19 and SUD had worse outcomes (death: 13.0%, hospitalization: 50.7%) than Caucasians (death: 8.6%, hospitalization: 35.2%). These findings identify individuals with SUD, especially individuals with OUD and African Americans, as having increased risk for COVID-19 and its adverse outcomes, highlighting the need to screen and treat individuals with SUD as part of the strategy to control the pandemic while ensuring no disparities in access to healthcare support.

Wang Quan Qiu, Kaelber David C, Xu Rong, Volkow Nora D

2020-Sep-14

Radiology Radiology

Training Radiology Residents, Bloom Style.

In Academic radiology

Bloom's Taxonomy, an integral component of learning theory since its inception, describes cognitive skill levels in increasing complexity (Remember, Understand, Apply, Analyze, Evaluate, and Create). Considering Bloom's Taxonomy when writing learning objectives and lecture material, teaching residents at the workstation and creating multiple choice questions can increase an educator's effectiveness. The incorporation of higher Bloom levels aids in cultivating critical thinking skills vital to image interpretation and patient care, and becomes increasingly important as the radiologist's role evolves with the continued development of artificial intelligence. Following established tenets of multiple choice question writing, involving trainees in the question writing process, and incorporating audience response systems into lectures are all strategies in which higher Bloom level skills can be accomplished.

Smith Elana B, Gellatly Matthew, Schwartz Cody J, Jordan Sheryl

2020-Sep-10

“Blooms taxonomy”, educational assessment, graduate medical education, radiology education, teaching methods

General General

Past, present and future EEG in the clinical workup of dementias.

In Psychiatry research. Neuroimaging

Electroencephalography (EEG), as non-invasive, global measure of neuronal activity, is a prime candidate functional marker of synapse dysfunction and loss in dementias. Nevertheless, EEG currently has no established role in the clinical workup of individual patients. This opinion paper presents our critical view on why EEG has so far failed to keep its promise, and where we believe EEG will be clinically useful for patients threatened with cognitive decline in the future. Individual EEGs are an integral outcome of many causally intermixing upstream factors contributing to dementia. Therefore, EEG cannot become a clinically useful "simple" stand-alone biomarker of some pathognomic accumulations of specific brain proteins, but rather offer unique opportunities for more comprehensive and richly faceted insights into the functional status of brain systems. EEG may thus remain an essential window into the brain when it comes to the at-risk and presymptomatic phases of dementias, where it can be uniquely informative about concepts such as burdens of plasticity and repair, cognitive reserve, and sleep. Jointly with rapid gains in usability, portability, machine learning, closed loop systems, and understanding of the role of EEG-based sleep stages for memory and brain repair, EEG may come to keep its initial promise after all.

Koenig Thomas, Smailovic Una, Jelic Vesna

2020-Sep-04

Biomarker, Brain vitality, Cognitive reserve, Connectivity, Disease modification, Inverse problem, Levy body dementia, Microstates, Plasticity, Prevention, Proteinopathies, Sleep, Synaptic dysfunction, Synchronization

General General

Beyond Infection: Integrating Competence into Reservoir Host Prediction.

In Trends in ecology & evolution

Most efforts to predict novel reservoirs of zoonotic pathogens use information about host exposure and infection rather than competence, defined as the ability to transmit pathogens. Better obtaining and integrating competence data into statistical models as covariates, as the response variable, and through postmodel validation should improve predictive research.

Becker Daniel J, Seifert Stephanie N, Carlson Colin J

2020-Sep-10

SARS-CoV-2, machine learning, vector-borne disease, within-host, zoonoses

Radiology Radiology

Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learning.

In European journal of radiology ; h5-index 47.0

PURPOSE : During the emerging COVID-19 pandemic, radiology departments faced a substantial increase in chest CT admissions coupled with the novel demand for quantification of pulmonary opacities. This article describes how our clinic implemented an automated software solution for this purpose into an established software platform in 10 days. The underlying hypothesis was that modern academic centers in radiology are capable of developing and implementing such tools by their own efforts and fast enough to meet the rapidly increasing clinical needs in the wake of a pandemic.

METHOD : Deep convolutional neural network algorithms for lung segmentation and opacity quantification on chest CTs were trained using semi-automatically and manually created ground-truth (Ntotal = 172). The performance of the in-house method was compared to an externally developed algorithm on a separate test subset (N = 66).

RESULTS : The final algorithm was available at day 10 and achieved human-like performance (Dice coefficient = 0.97). For opacity quantification, a slight underestimation was seen both for the in-house (1.8 %) and for the external algorithm (0.9 %). In contrast to the external reference, the underestimation for the in-house algorithm showed no dependency on total opacity load, making it more suitable for follow-up.

CONCLUSIONS : The combination of machine learning and a clinically embedded software development platform enabled time-efficient development, instant deployment, and rapid adoption in clinical routine. The algorithm for fully automated lung segmentation and opacity quantification that we developed in the midst of the COVID-19 pandemic was ready for clinical use within just 10 days and achieved human-level performance even in complex cases.

Anastasopoulos Constantin, Weikert Thomas, Yang Shan, Abdulkadir Ahmed, Schmülling Lena, Bühler Claudia, Paciolla Fabiano, Sexauer Raphael, Cyriac Joshy, Nesic Ivan, Twerenbold Raphael, Bremerich Jens, Stieltjes Bram, Sauter Alexander W, Sommer Gregor

2020-Aug-28

COVID-19, Computed tomography, Machine learning, Software

General General

GameTag: A New Sequence Tag Generation Algorithm Based on Cooperative Game Theory.

In Proteomics

Sequence tag-based peptide search is a critical technology in proteomics for the characterization of proteins from tandem mass spectrometry data. However, the main reason for hindering the full application of such an approach lies that accurately extracting sequence tags responsible for each experimental spectrum. Towards that end, we propose GameTag, a novel cooperative game framework for sequence tag generation, which includes a tag generator and a tag discriminator to collaboratively generate sequence tags. Specifically, the tag generator works to extract as many correct tag candidates as possible and the tag discriminator serves to determine the correctness of tag candidates and reduce the total number of output tags simultaneously. Through the dynamic two-player game, the number of extracted tags is decreased while the number of correct tags gets boosted. We also investigate the performance of our proposed method under various hyperparameter and structure settings. Extensive experiments on a wide variety of data sets from different species demonstrate that GameTag outperforms previous state-of-the-art methods, InsPecT, PepNovo+, DirecTag, and the existing tag-extraction method in Open-pFind, increasing by at least 10% the number of spectra extracted more than one correct tag. This article is protected by copyright. All rights reserved.

Fei Zheng-Cong, Wang Kaifei, Chi Hao

2020-Sep-14

cooperative game, deep learning, proteomics, sequence tag generation