Infectious Disease

Respiratory Syncytial Virus Vaccine Design Using Structure-Based Machine-Learning Models.

When designing live-attenuated respiratory syncytial virus (RSV) vaccine candidates, attenuating mutations can be developed through biologic selection or reverse-genetic manipulation and may include point mutations, codon and gene deletions, and genome rearrangements. Attenuation typically involves the reduction in virus replication, due to direct effects on viral structural and replicative machin...

Predicting Schistosomiasis Intensity in Africa: A Machine Learning Approach to Evaluate the Progress of WHO Roadmap 2030.

The World Health Organization (WHO) 2030 Roadmap aims to eliminate schistosomiasis as a public health issue, targeting reductions in the heavy intensity of infections. Previous studies, however, have predominantly used prevalence as the primary indicator of schistosomiasis. We introduce several machine learning (ML) algorithms to predict infection intensity categories, using morbidity prevalence, ...

A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis.

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machi...

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration.

Myocarditis poses a significant health risk, often precipitated by viral infections like coronavirus disease, and can lead to fatal cardiac complicati...

COVID‑19 detection from chest X-ray images using transfer learning.

COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is it...

Prediction of incomplete immunization among under-five children in East Africa from recent demographic and health surveys: a machine learning approach.

The World Health Organization as part of the goal of universal vaccination coverage by 2030 for all individuals. The global under-five mortality rate ...

What Are Humans Doing in the Loop? Co-Reasoning and Practical Judgment When Using Machine Learning-Driven Decision Aids.

Within the ethical debate on Machine Learning-driven decision support systems (ML_CDSS), notions such as "human in the loop" or "meaningful human cont...

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurologi...

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 instituti...

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.

Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim of this study was to develop an interpretable machi...

Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods.

We present an electrochemical platform designed to reduce time of Escherichia coli bacteria detection from 24 to 48-h to 30 min. The presented approac...

Surveying haemoperfusion impact on COVID-19 from machine learning using Shapley values.

BACKGROUND: Haemoperfusion (HP) is an innovative extracorporeal therapy that utilizes special cartridges to filter the blood, effectively removing pro...

A complex fuzzy decision model for analysing the post-pandemic immuno-sustainability.

The post-effects of the COronaVIrus Disease (COVID-19) vary depending on socioeconomic and biological factors. Similarly, the effects of vaccination o...

Hybrid machine learning model based predictions for properties of poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose.

Supermacroporous composite cryogels with enhanced adjustable functionality have received extensive interest in bioseparation, tissue engineering, and ...

Random forest models of food safety behavior during the COVID-19 pandemic.

Machine learning approaches are increasingly being adopted as data analysis tools in scientific behavioral predictions. This paper utilizes a machine ...

The Surgical Clerkship in the COVID Era: A Natural Language Processing and Thematic Analysis.

INTRODUCTION: Responses to COVID-19 within medical education prompted significant changes to the surgical clerkship. We analyzed the changes in medica...

Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning.

Biomacromolecule structures are essential for drug development and biocatalysis. Quantum refinement (QR) methods, which employ reliable quantum mechan...

Predicting malaria outbreak in The Gambia using machine learning techniques.

Malaria is the most common cause of death among the parasitic diseases. Malaria continues to pose a growing threat to the public health and economic g...

Optimizing deep learning-based segmentation of densely packed cells using cell surface markers.

BACKGROUND: Spatial molecular profiling depends on accurate cell segmentation. Identification and quantitation of individual cells in dense tissues, e...

Molecular Mechanism of Phosphorylation-Mediated Impacts on the Conformation Dynamics of GTP-Bound KRAS Probed by GaMD Trajectory-Based Deep Learning.

The phosphorylation of different sites produces a significant effect on the conformational dynamics of KRAS. Gaussian accelerated molecular dynamics (...