Environmental Health

A deep learning framework for multi-lead ECG arrhythmia classification with CAM interpretability and LLM-driven clinical guidance.

Cardiac arrhythmias remain a major cause of morbidity and mortality, requiring accurate and interpretable automated diagnosis. This reserach presents a lightweight deep learning framework for multi-lead ECG arrhythmia classification. A Winograd-scaled 1D MobileNet efficiently extracts discriminative features, while the Lead-aware Skip Weighting Residual ConvNeXt Attention with Builder Optimizer (L...

Toxic effects and mechanisms of methyl 4-hydroxybenzoate in allergic rhinitis: a multi-omics and toxicology study.

PURPOSE: Exogenous chemical exposure is closely associated with allergic rhinitis (AR). Methyl 4-hydroxybenzoate (MeP), a widely used preservative, poses risks of long-term human exposure. This study aimed to explore the potential association between MeP and AR as well as its underlying molecular characteristics. METHODS: Integrating toxicological assessment, bioinformatics analysis, and machine l...

Comments on "Advancing air pollution forecasting: a review of physical, statistical, and machine learning methods".

Advancing air pollution forecasting: a review of physical, statistical, and machine learning methods provides a timely and comprehensive overview of d...

Wellens' Sign and Syndrome Detected With the Home Use of a Novel 12-Lead Electrocardiogram.

BACKGROUND: Wellens' sign is a high-risk electrocardiogram (ECG) pattern associated with proximal left anterior descending artery stenoses and high ri...

Dynamic feature-adaptive representation learning for spatiotemporal air pollution forecasting.

Atmospheric pollution remains a major concern for public health and city sustainability, especially in densely populated metropolitan regions with com...

A 1T-MoS2/FBC composite electrode for efficient removal of Cr(VI) through capacitive deionization: performance, mechanism, and BP neural network modeling.

Hexavalent chromium (Cr(VI)), a highly toxic and mobile heavy metal, poses significant environmental and health risks, making its removal a critical c...

Perovskites in Environmental and Energy Catalysis: Transitioning From Lead-Based to Lead-Free Architectures.

Perovskite materials have emerged as versatile platforms for sustainable photocatalytic and photoelectrocatalytic applications, addressing critical ch...

Microplastics drive both gradual and abrupt shifts in soil multifunctionality along concentration gradients.

Microplastics are increasingly recognized as emerging contaminants in terrestrial ecosystems, yet their cumulative impacts on soil multifunctionality ...

ECG-Based Artificial Intelligence for Classifying Left Ventricular Dysfunction and Heart Failure With Preserved Ejection Fraction.

BACKGROUND: Left ventricular (LV) dysfunction and heart failure with preserved ejection fraction (HFpEF) often present with early signs that are frequ...

Transforming Orthopaedic Trauma Care: Forecasting Operating Room Demand by Harnessing Time-Series Analysis and Machine Learning.

BACKGROUND: Efficient allocation of operating room (OR) time is critical in trauma centers, where unpredictable volumes lead to wasted resources. Fore...

Long-Term Particulate Matter Exposure and Coronary Inflammation: A Large-Scale Cohort Study Assessed by Pericoronary Fat Attenuation Index.

Particulate matter (PM) is known to accelerate atherosclerosis through systemic inflammation, yet its specific impact on local coronary inflammation r...

High-content imaging driven algal phenotypes enable precise multilevel discrimination of heavy metal stress.

Conventional water quality monitoring based on fixed chemical thresholds often fails to capture the integrated biological effects of pollutant mixture...

GAN-augmented machine learning enables accurate band gap prediction for 2D lead iodide perovskites with limited data.

Low-dimensional hybrid lead iodide perovskites exhibit band gaps that are highly sensitive to subtle octahedral distortions, yet accurate prediction r...

Molecular mechanism of lead exposure induced atrial fibrillation: A systematic study based on network toxicology, machine learning, and in vivo and in vitro experiments.

Lead is a ubiquitous environmental toxic metal. Lead exposure is closely linked to an increased risk of atrial fibrillation (AF), yet its molecular me...

Socioeconomic drivers of riverine biochemical oxygen demand: Insights from interpretable machine learning.

Biochemical oxygen demand (BOD5) is a comprehensive indicator for assessing organic pollution and water quality in rivers. Socioeconomic factors, such...

AI-Integrated Molecularly Imprinted Gate-Controlled Nanozyme-Based Field-Deployable Multimodal Assay of Per- and Polyfluoroalkyl Substances in Environmental Waters.

The significant risks posed by per- and polyfluoroalkyl substances (PFAS) to water quality and public health have attracted increasing attention as a ...

Answer to Comments on "Advancing air pollution forecasting: a review of physical, statistical, and machine learning methods".

R-R Interval Histogram-Based Deep Learning for 3-Class Atrial Fibrillation Screening in Garment-Type Wearable Holter Electrocardiogram Monitoring: Algorithm Development and Validation Study.

BACKGROUND: Long-term garment-type wearable Holter electrocardiographic (ECG) monitoring is frequently affected by noise contamination, which complica...

Spatiotemporal evolution of global soil heavy metal(loid)s mobility under climate change.

The persistent accumulation of heavy metal(loid)s (HMs) in soils poses a major risk to environmental quality worldwide, while their migration and tran...

Biodegradable and bioresorbable rechargeable batteries: chemistries, AI-driven material discovery, and emerging applications.

Biodegradable and bioresorbable rechargeable batteries are emerging as key enabling technologies for transforming the manner in which electronic syste...