AIMC Topic: COVID-19

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Deep Learning-Based Comparative Prediction and Functional Analysis of Intrinsically Disordered Regions in SARS-CoV-2.

International journal of molecular sciences
This study explores the role of intrinsically disordered regions (IDRs) in the SARS-CoV-2 proteome and their potential as targets for small-molecule drug discovery. Experimentally validated intrinsic disordered regions from the literature were utiliz...

Clinical microbiology and artificial intelligence: Different applications, challenges, and future prospects.

Journal of microbiological methods
Conventional clinical microbiological techniques are enhanced by the introduction of artificial intelligence (AI). Comprehensive data processing and analysis enabled the development of curated datasets that has been effectively used in training diffe...

MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.

Science advances
Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provides a powerful approach for in-depth T cell immune function research. Here, we introduce a deep learning framework for single-T cell transcriptome and ...

Machine learning approaches for real-time ZIP code and county-level estimation of state-wide infectious disease hospitalizations using local health system data.

Epidemics
The lack of conventional methods of estimating real-time infectious disease burden in granular regions inhibits timely and efficient public health response. Comprehensive data sources (e.g., state health department data) typically needed for such est...

Using artificial intelligence tools to automate data extraction for living evidence syntheses.

PloS one
Living evidence synthesis (LES) involves repeatedly updating a systematic review or meta-analysis at regular intervals to incorporate new evidence into the summary results. It requires a considerable amount of human time investment in the article sea...

Machine learning in point-of-care testing: innovations, challenges, and opportunities.

Nature communications
The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (...

Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data.

Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia
OBJECTIVE: To predict COVID-19 in hospitalized patients with SARS in a city in southern Brazil by using machine learning algorithms.

Combination of exhaled volatile organic compounds with serum biomarkers predicts respiratory infection severity.

Pulmonology
OBJECTIVE: During respiratory infections, host-pathogen interaction alters metabolism, leading to changes in the composition of expired volatile organic compounds (VOCs) and soluble immunomodulators. This study aims to identify VOC and blood biomarke...

Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools.

Molecular diversity
Antiviral peptides (AVPs) represent a novel and promising therapeutic alternative to conventional antiviral treatments, due to their broad-spectrum activity, high specificity, and low toxicity. The emergence of zoonotic viruses such as Zika, Ebola, a...

Clinicians' Perceptions and Potential Applications of Robotics for Task Automation in Critical Care: Qualitative Study.

Journal of medical Internet research
BACKGROUND: Interest in integrating robotics within intensive care units (ICUs) has been propelled by technological advancements, workforce challenges, and heightened clinical demands, including during the COVID-19 pandemic. The integration of roboti...