AIMC Topic: COVID-19
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Improved Prediction of Drug-Protein Interactions through Physics-Based Few-Shot Learning.
Journal of chemical information and modeling
Jun 17, 2025
Accurate prediction of drug-protein interactions is crucial for drug discovery. Due to the bottleneck of traditional scoring functions, many machine learning scoring functions (MLSFs) have been proposed for structure-based drug screening. However, ex...
Measurement, Characterization, and Mapping of COVID-19 Misinformation in Spain: Cross-Sectional Study.
JMIR infodemiology
Jun 16, 2025
BACKGROUND: The COVID-19 pandemic has been accompanied by an unprecedented infodemic characterized by the widespread dissemination of misinformation. Globally, misinformation about COVID-19 has led to polarized beliefs and behaviors, including vaccin...
AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.
Clinical and experimental medicine
Jun 14, 2025
The SARS-CoV-2 RNA virus, with its rapid spread and frequent genetic changes, has posed unparalleled obstacles for public health and treatment efforts. Early diagnosis of the disease and the development of effective treatment strategies are the main ...
Stem loop binding protein promotes SARS-CoV-2 replication via -1 programmed ribosomal frameshifting.
Signal transduction and targeted therapy
Jun 13, 2025
The -1 programmed ribosomal frameshifting (-1 PRF) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for keeping the balance between pp1a and pp1ab polyproteins. To date, the host factors influencing this process remain poorl...
Gut microbiota alterations are linked to COVID-19 severity in North African and European populations.
NPJ biofilms and microbiomes
Jun 13, 2025
Although COVID-19 primarily affects the respiratory system, many patients experience gastrointestinal symptoms, suggesting a role for the gut microbiota in disease pathogenesis. To explore this, we performed shotgun metagenomic sequencing on stool sa...
Evaluation of semi-automated versus fully automated technologies for computed tomography scalable body composition analyses in patients with severe acute respiratory syndrome Coronavirus-2.
Clinical nutrition ESPEN
Jun 11, 2025
RATIONALE AND OBJECTIVES: Fully automated, artificial intelligence (AI) -based software has recently become available for scalable body composition analysis. Prior to broad application in the clinical arena, validation studies are needed. Our goal wa...
Advancing respiratory disease diagnosis: A deep learning and vision transformer-based approach with a novel X-ray dataset.
Computers in biology and medicine
Jun 9, 2025
With the increasing prevalence of respiratory diseases such as pneumonia and COVID-19, timely and accurate diagnosis is critical. This paper makes significant contributions to the field of respiratory disease classification by utilizing X-ray images ...
Tailoring task arithmetic to address bias in models trained on multi-institutional datasets.
Journal of biomedical informatics
Jun 8, 2025
OBJECTIVE: Multi-institutional datasets are widely used for machine learning from clinical data, to increase dataset size and improve generalization. However, deep learning models in particular may learn to recognize the source of a data element, lea...
Mortality Prediction Performance Under Geographical, Temporal, and COVID-19 Pandemic Dataset Shift: External Validation of the Global Open-Source Severity of Illness Score Model.
Critical care explorations
Jun 4, 2025
BACKGROUND: Risk-prediction models are widely used for quality of care evaluations, resource management, and patient stratification in research. While established models have long been used for risk prediction, healthcare has evolved significantly, a...
A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation.
Journal of medical Internet research
Jun 3, 2025
BACKGROUND: The COVID-19 pandemic has highlighted the need for robust and adaptable diagnostic tools capable of detecting the disease from diverse and continuously evolving data sources. Machine learning models, particularly convolutional neural netw...
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