Environmental Health

Methane Cycling Microbes are Important Predictors of Methylmercury Accumulation in Rice Paddies

Microbial production of methylmercury from inorganic mercury in rice paddies poses health risks to consumers of this essential dietary staple. Although mercury-methylating communities are well characterized, the microbial guilds contributing to methylmercury accumulation in rice paddies remain unclear. Here, we collected paddy soils across a mercury concentration gradient throughout the rice growi...

Integrating Multi-Structure Covalent Docking with Machine Learning Consensus Scoring Enhances Virtual Screening of Human Acetylcholinesterase Inhibitors

Acetylcholinesterase (AChE) inhibition is a key mechanism in the treatment of neurodegenerative diseases and in counteracting toxic exposures to pesticides and nerve agents. However, virtual screening of AChE remains challenging due to the enzyme’s structural flexibility and the chemical diversity of its covalently binding inhibitors. In this study, we developed an in silico protocol that integrat...

CDR Conformation Aware Antibody Sequence Design with ConformAb

Antibody lead optimization methods seek to enhance a lead candidate’s therapeutic properties through targeted sequence mutation. However, the mutation...

A PLM-Based Method for Predicting Protein Ion Channel Modulators for Drug Discovery and Safety Evaluation

Ion channels are central to regulating neuronal communication, cardiac rhythm, and muscle contraction. Their modulation can induce therapeutic benefit...

Predicting strain-specific metabolic capabilities in the Genus Pseudomonas using a Flux-to-AI approach unravels hidden cell envelope properties

Bacteria from the Pseudomonas genus are omnipresent in air, soil, and water. They have been widely studied for their broad metabolic versatility and p...

AutoLead: An LLM-Guided Bayesian Optimization Framework for Multi-Objective Lead Optimization

The process of lead optimization in drug discovery is a complex, multi-objective challenge that remains a major bottleneck in the development of new t...

Phenotypic Screening Coupled with AI-Driven Target Deconvolution Identifies α-Terthienyl as a Dual DPP-IV/HSD17β13 Modulator with Efficacy in a Mouse Model of MASLD

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent condition characterized by fat build-up in the liver and ranges...

Predicting ADC Map Quality from T2-Weighted MRI: A Deep Learning Approach for Early Quality Assessment to Assist Point-of-Care

Poor quality prostate MRI images, especially ADC maps, can lead to missed lesions and unnecessary repeat scans. To address this issue, we aimed to dev...

Foundation models for generalizable electrocardiogram interpretation: comparison of supervised and self-supervised electrocardiogram foundation models

The 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solutions for automated in...

Artificial Intelligence-Based Automated Interpretation of Images of Electrocardiograms: Development and Multinational Validation of ECG-GPT

Timely and accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and clinically managing patients. Current workflows r...

Development and validation of diagnostic and prognostic prediction tools for dental caries in young children: A protocol

Dental caries is the most common oral disease worldwide, affecting up to 90% of children globally. It can lead to pain, infection, and impaired qualit...

The allostatic overload in pregnancy during the COVID-19 pandemic and potential effects on the health of the mother-child dyad: Study Protocol

Allostatic load refers to the cumulative burden of stress and life events that involve the interaction of various physiological systems at differing l...

Wearable-Echo-FM: An ECG-echo foundation model for single lead electrocardiography

Artificial intelligence (AI) models can now detect patterns of structural heart diseases (SHDs) from electrocardiograms (ECGs), though scaling them re...

The miniECG: Enabling interpretable detection of amplitude and intraventricular conduction ECG-abnormalities with a novel ECG device

The miniECG, a smartphone-sized, multi-lead device, offers a simple and fast alternative to the 12-lead ECG. We aimed to demonstrate the potential of ...

Machine Learning for Paediatric Related Decision Support in Emergency Care – A UK and Ireland Network Survey Study

This study explores clinician understanding and perception at site lead level towards machine learning (ML) decision support tools for paediatric rela...

Machine learning to classify left ventricular hypertrophy using ECG feature extraction by variational autoencoder

Traditional ECG criteria for left ventricular hypertrophy (LVH) have modest diagnostic yield. Develop and validate machine learning models for LVH dia...

Predicting Vaping Cessation in Young Adults: A Machine Learning and Explainable Artificial Intelligence (XAI) Approach to Public Health Intervention

The public health impact of vaping in the United States reflects a complex balance of potential benefits and emerging risks. While e-cigarettes can su...

Forecasting left ventricular systolic dysfunction in heart failure with artificial intelligence

Objective assessment of left ventricular function remains a key prognosticator that is used to guide therapeutic decisions for patients with heart fai...

Deep Learning Prediction of Left Atrial Structure and Function from 12-lead Electrocardiograms

Abnormal cardiac atrial structure and function (atrial cardiopathy)1 typically precedes atrial fibrillation (AF) and predicts other cardiovascular com...

Prediction of Pulmonary Vein Isolation and Gap Recurrence on 12-Lead ECG Using Deep Learning

Pulmonary vein isolation (PVI) is key to atrial fibrillation (AF) ablation, but arrhythmia often recurs due to conduction gaps permitting pulmonary ve...