Environmental Health

Crystal Facet Engineering-Photoexcitation-Machine Learning Synergy on Cu2O/CuO Heterojunctions for High-Performance Triethylamine Sensing.

Triethylamine (TEA), a typical biogenic amine indicative of protein spoilage and a common toxic chemical pollutant, requires highly sensitive detection to ensure both food safety and occupational health. However, existing TEA sensors are often limited by low response and high operating temperatures. To address these limitations, we synthesized Cu2O/CuO heterojunctions in situ via a semisacrificial...

Artificial intelligence and advanced monitoring for air and water pollution control in the USA: opportunities, challenges, and policy directions.

In this review article, we present the current state of artificial intelligence and advanced technologies in air and water pollution monitoring in the USA. Recent studies in this field show promising outcomes, including the ability to generate more granular data in real-time, using predictive analytics to identify and prevent pollution, and better modeling of complex exposure. Compared to traditio...

Tetrahedral framework nucleic acids as a promising vehicle for small molecule drugs of traditional Chinese medicine: application to disease prevention and treatment.

The modernization of Traditional Chinese Medicine (TCM) faces considerable challenges, primarily due to the poor water solubility, low chemical stabil...

Efficiently quantifying thermodynamic and kinetic effects of biochar pyrolysis parameters on antibiotic adsorption from water using interpretable causal machine learning.

Antibiotic pollution in aquatic environments threatens ecological and public health. Biochar is a promising biomass-derived adsorbent, yet its perform...

Decoding seasonal irrigation suitability of drain water using integrated indexical, statistical, graphical and machine learning approaches.

SCOPE: Rapid urbanization and industrialization have exacerbated freshwater scarcity while simultaneously increasing wastewater generation, making the...

Global patterns of air pollution-attributable neonatal preterm birth mortality rates: a machine learning analysis using LSTM autoencoder and K-means clustering.

Neonatal preterm birth is a leading cause of neonatal mortality worldwide, and maternal exposure to air pollution is increasingly recognized as a cont...

Operational 24-h PM2.5 forecasting using ensemble methods and open-access satellite data: application to Dakar, Senegal.

Accurate short-term air quality forecasting is urgently needed in African megacities. Chronic PM2.5 pollution threatens public health in these cities....

Integrative Bioinformatics, Experimental Validation, and Interpretable Machine Learning Reveal Oxyresveratrol-Mediated Protection Against Cadmium-Induced Lung Adenocarcinoma-Related Transcriptional Dysregulation.

Cadmium (Cd) is a toxic heavy metal strongly implicated in lung adenocarcinoma (LUAD) through mechanisms involving oxidative stress, epigenetic dysreg...

Trends and developments in marine debris studies: A bibliometric review over two decades (2004-2024).

Marine debris has become a serious global environmental issue due to its increasing impacts on marine ecosystems, biodiversity, and human health. Larg...

Tubulointerstitial Diseases: An Updated Framework for Diverse and Emerging Entities.

Tubulointerstitial diseases represent a heterogeneous group of kidney diseases with diverse causes and overlapping histopathologic and immunophenotypi...

Identifying potential relationships between air pollutants and neutrophil extracellular traps formation in metabolic dysfunction-associated fatty liver disease by integrating computational toxicology and multi-omics data.

Metabolic dysfunction-associated fatty liver disease (MASLD) is a highly prevalent liver condition with a complex etiology increasingly linked to air ...

Combination of Chromatographic and Machine Learning-Driven Virtual Fractionation Identifies Aryl Hydrocarbon Receptor Agonists in Sediments.

Complex organic chemical mixtures in aquatic ecosystems may cause adverse effects on aquatic and sediment-dwelling organisms. Identified chemicals typ...

Change-point detection and early warning systems.

This paper presents a statistical framework for early warning change-point detection in electrical grid frequency time series. Frequency deviations ou...

Atrial repolarization wave: a new approach using spline-based feature engineering and explainable AI for atrial arrhythmia diagnosis.

OBJECTIVES: The atrial repolarization (Ta wave) characteristics remains largely unexplored, given its inherently low amplitude and obscured by the QRS...

Quantifying wildfire impacts on atmospheric pollutants using fire exposure metrics and machine-deep learning approaches.

Wildfires are increasingly recognised as major contributors to atmospheric pollution, yet their spatial-temporal influence on regional air quality rem...

Machine learning and multi-dimensional transcriptomics reveal the key molecular network of benzo(a)pyrene/NNK in promoting laryngeal cancer and develop prognostic models.

Benzo(a)pyrene (BaP) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), as typical environmental carcinogens, are widely present in tobacco smo...

The detectability paradox: bilingual medical report generation with open-weight models and the limits of human oversight.

OBJECTIVES: The automation of medical report generation using large language models (LLMs) could significantly reduce physicians' documentation burden...

Unveiling the drivers of groundwater quality in an industrial plain: An integrated hydrogeochemical and stacking ensemble learning approach.

Understanding the hydrochemical evolution and primary chemical components governing groundwater quality in industrial regions is crucial for sustainab...

Ensemble Machine Learning for Interpretable Prediction of Acute Toxicity in Metal-Organic Framework Linkers.

Metal-organic frameworks (MOFs) are promising platforms for drug delivery due to their high porosity, tunable chemistry, and controlled release capabi...

Leveraging the Metabolic Fingerprint of Sleep Deprivation and Sleep Restriction for Forensic Applications: A Machine Learning Study in Oral Fluid Metabolomics.

As sleep loss leads to accidents and impaired safety, a direct metabolic marker would be beneficial for forensic interpretation. In a sufficiently pow...