Environmental Health

Integrated multi-omics and experimental validation reveal CSK and FBLN1 as key targets of stanozolol in accelerating atherosclerosis.

BACKGROUND: Stanozolol, a synthetic anabolic androgenic steroid (AAS) widely abused to enhance performance, has poorly defined toxicological mechanisms in atherosclerosis (AS). METHODS: An AS model was established in ApoE-/- mice, followed by a 12-week stanozolol intervention. Effects on lipid metabolism, systemic inflammation, and plaque formation were assessed. Potential targets were predicted v...

Fires reverse progress toward ozone air quality standards in the United States.

Recent surges in wildfire emissions have exacerbated surface ozone pollution in the United States. Using deep learning, we developed a gapless daily surface ozone dataset at 1-kilometer resolution for 2003-2024. This dataset revealed a reversal in national policy-relevant ozone trends that had gone undetected by the sparse monitoring network: from -0.65 parts per billion (ppb) per year (2003-2015)...

Rural Airborne PM2.5-bound Microplastics and Heavy Metals: GIS-AI Risk Assessment Review.

Fine particulate matter (PM2.5) is a critical air pollutant and carrier of toxic co-contaminants, including microplastics (MPs) and heavy metals (HMs)...

Electrocardiogram-based short-term risk stratification for acute kidney injury using time-frequency deep learning analysis.

BACKGROUND AND OBJECTIVES: Acute kidney injury (AKI) is a common and clinically important complication in critically ill and perioperative patients. C...

Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG.

Automated 12-lead ECG interpretation has achieved strong diagnostic accuracy through deep learning, yet systematic demographic disparities-particularl...

Anthropogenic nitrogen impacts on global PM2.5-O3 co-pollution health burden.

The growing recognition of the adverse health impacts posed by ambient fine particulate matter (PM2.5) and ozone (O3) has driven global efforts to imp...

Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.

Artificial intelligence (AI) in environmental health science is revolutionizing data analysis and problem-solving approaches. These technologies facil...

Interpretable machine learning and greenness assessment for sustainable tetracycline adsorption and phytotoxicity mitigation using almond peel - derived activated carbon.

This study presents, the conversion of almond peel waste into activated carbon (APAC) and its use for removing tetracycline (TC) antibiotic from water...

Heritability of the Olfactory Bulb and Its Associated Brain Network.

The olfactory bulb (OB), the first central relay of the olfactory pathway, plays a critical role in odor perception and exhibits remarkable structural...

Global Characterization of Sea Turtle Ecological Traps under Compound Marine Heatwaves and Microplastics.

The concurrent intensification of marine heatwaves (MHW) and microplastics (MPs) pollution presents a complex challenge for environmental risk assessm...

Reengineering a thermostable keratinase past the boiling point.

Millions of tons of keratin-rich waste are disposed of annually, which could be revalorized, mitigating greenhouse emissions and pollution. Microbial ...

From metabolic reprogramming to diagnostic tool: A machine learning framework identifies biomarkers for soil microplastic stress in rice.

As emerging environmental contaminants, microplastics (MPs) increasingly infiltrate agricultural ecosystems, threatening crop safety and productivity....

Evolution of PM2.5 chemical composition during summer new particle formation in a coastal city in the Yangtze River Delta: insights from pollution episodes, machine learning, and air mass origins.

To investigate the influence of O3 on new particle formation (NPF) events and associated changes in PM2.5 chemical composition, an observation campaig...

Innovations in Electrochemical Sensors for TNT Explosive Detection: From Nanomaterials to Wearable Devices and Artificial Intelligence.

The detection of explosive residues, particularly nitroaromatic compounds such as 2,4,6-trinitrotoluene (TNT), is a major global concern because of th...

Feasibility and Preliminary Effectiveness of the ChulaCancer Mobile Chatbot for Supportive Care of Patients With Breast or Colorectal Cancer Receiving Chemotherapy: Pilot Randomized Controlled Trial.

BACKGROUND: Chemotherapy-related toxicities often lead to unscheduled health care use and diminished quality of life. Digital health interventions, su...

Prediction of Ligand Binding to Transthyretin Using Machine Learning Algorithms and Low-Dimensional Molecular Descriptors: A Tox24 Challenge Study.

This study presents a comprehensive machine learning approach for predicting the percent displacement of ANSA from human transthyretin (TTR) at fixed ...

From Forecast to Action: A Deep Learning Model for Predicting Power Outages During Tropical Cyclones.

Power outages caused by tropical cyclones (TCs) pose serious risks to electric power systems and the communities they serve. Accurate, high-resolution...

Mechanistic Insights into PET-MPs-Aggravated Ischemic Stroke via Integrated Computational-Experimental Approaches.

Microplastics, especially the environmentally pervasive polyethylene terephthalate microplastics (PET-MPs), are important environmental pollutants, an...

Deep learning-based monitoring and localization in autonomous underwater vehicle networks for aquatic ecosystem analysis.

Monitoring the aquatic ecosystem is crucial for effective communication and issuing alerts regarding underwater environmental conditions. None of the ...

Prediction of low birth weight using machine learning-based analysis of environmental and maternal risk factors: insights from the Korean CHildren's ENvironmental health Study (Ko-CHENS).

Adverse birth outcomes such as low birth weight (LBW) increase the risk of metabolic disorders and hypertension later in life. Although previous studi...