Environmental Health

Real-world results from a Machine Learning-guided, phenotypic High-Throughput Screen for novel antibiotics

Antimicrobial resistance is an urgent global health threat, with over 2.8 million multidrug-resistant infections killing over 35,000 annually in the US. Machine Learning (ML) has emerged as a potential solution to improve efficiency of antibiotic high-throughput screens (HTS). We report ML-guided high-throughput screening against E. coli. Large-scale Learning-to-Rank models were trained on public ...

Physiology-Aware CNN and Zero-Shot Multimodal LLMs for ECG Image Classification: A Comparative Study

Multimodal large language models (LLMs) are increasingly adopted to interpret 12-lead ECG images, though the interpretations often lack validation. However, ECG image understanding significantly differs from general images as it depends on precise waveform morphology, lead relationships and accurate interval measurements. This study investigated whether zero-shot multimodal LLMs can reliably disti...

Host-derived bile acids drive dysbiosis by selecting bile-resistant epimerizing bacteria in inflammatory bowel disease

Microbial dysbiosis is a hallmark of inflammatory bowel diseases (IBD); however, its drivers and impact on disease pathophysiology are poorly understo...

Wearable-Grade Lead Reduction Disproportionately Degrades ECG AI Performance in Elderly Patients: Evidence from PTB-XL and MIT-BIH

Consumer wearable devices increasingly use single-lead electrocardiograms (ECGs) for cardiac monitoring, but these signals contain substantially less ...

Identification of Direct and Network-Mediated Activation of Retinal Ganglion Cells from Visually Evoked Potentials Using Machine Learning

Objective. To preserve the encoding of visual information in prosthetic vision as close to natural as possible, subretinal photovoltaic implants, whic...

AI-enabled discovery of small molecules targeting complementary pathways for hair follicle rejuvenation

Hair thinning arises from multi-faceted dysfunction within the hair follicle, driven by both intrinsic cellular pathways and pathways responding to ex...

Machine Learning-Guided Discovery of Bacterial-Selective Membrane-Active Compounds Reveals Mechanistic Bias in Antibiotic Training Datasets

The rise of antibiotic resistance necessitates the discovery of antibacterial compounds with novel mechanisms of action (MoAs). Recent machine learnin...

ECG-derived age deviation predicts cardiovascular diseases across lead configurations and cohorts

Cardiovascular diseases (CVDs) remain the primary global health burden, motivating the search for robust, non-invasive risk biomarkers. We harness a f...

IB-HFN: Information Bottleneck-Driven SAR-Optical Fusion Network for High-Fidelity Cloud Removal

Synthetic aperture radar (SAR)-assisted optical cloud removal aims to recover surface information obscured by clouds in optical remote sensing images ...

Deep learning-guided design of hydrolases for crystalline PET depolymerization

Poly(ethylene terephthalate) (PET), a ubiquitous polyester used in packaging and textiles, persists in the environment due to its high crystallinity a...

Enhanced precision of tensor electrocardiography through increased cumulative distribution function resolution: Validation in healthy individuals

Deep-learning ECG analysis is advancing rapidly but lacks stable, physiologically interpretable indicators to anchor explainable artificial intelligen...

GraphTox: A Semi-Supervised Pre-Trained Framework for Peptide Toxicity Prediction using Geometric Graph Transformer and LORA-Based Finetuning

Peptides are widely used as potential therapeutic agents in drug discovery and biotechnology because they are specific, effective, and relatively inex...

Spatio-temporal machine learning for multi-horizon prediction of bluetongue outbreaks

Reliable early warning of infectious disease outbreaks remains a major challenge for surveillance systems, particularly for vector-borne pathogens who...

CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation

Real-time cognitive load assessment is essential for adaptive human-computer interaction but remains challenging due to limited labeled data and poor ...

HexagonalWarriorMamba: Superior Threshold-Dependent Multi-label Classification of 12-Lead ECG Cardiac Abnormalities

The accurate automated diagnosis of cardiac abnormalities from 12-lead electrocardiograms (ECGs) is critical for managing cardiovascular disease. Howe...

Identification of non-covalent inhibitors for the atypical peroxiredoxin PRDX5 as a therapeutic strategy in malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) is an aggressive asbestos-linked cancer with limited therapeutic options and a dismal 5-year survival rate of ~5%...

Automated Detection of Abnormalities in Zebrafish Development

Zebrafish embryos are a valuable model for drug discovery due to their optical transparency and genetic similarity to humans. However, current evaluat...

Stego Battlefield: Evaluating Image Steganography Attacks and Steganalysis Defenses

Image steganography is widely used to protect user privacy and enable covert communication. However, it can also be abused by the adversary as a cover...

Validating the Clinical Utility of CineECG 3D Reconstructions through Cross-Modal Feature Attribution

Deep learning models for 12-lead electrocardiogram (ECG) analysis achieve high diagnostic performance but lack the intuitive interpretability required...

Perception of Safety in Behavioral Health Crisis Units among Patients and Care Partners versus Artificial Intelligence (AI): A Multimethod Study

Background: Safety is a critical concern in behavioral health crisis units (BHCUs), where environmental risks (e.g., ligature points) can lead to inju...