Environmental Health

Integrating target, nontarget analysis with machine learning to illuminate PFAS characteristics and health risks in Chinese cosmetics.

Per- and polyfluoroalkyl substances (PFAS) are extensively incorporated into cosmetics to enhance durability and water resistance. However, their pervasive application in cosmetic products has raised concerns regarding the potential health risks associated with dermal exposure of PFAS. To date, studies focusing on Chinese market cosmetics have primarily relied on target analysis of a limited array...

Harnessing bacterial power and omics technologies for sustainable plastic waste biodegradation.

Plastic pollution constitutes a critical environmental concern of this era, with synthetic polymers, i.e., polyethylene (PE), polyethylene terephthalate (PET), polystyrene (PS), and polyurethane (PU), accumulating in terrestrial and aquatic ecosystems at alarming rates. One of the promising solutions to this worldwide problem is microbial plastic degradation, particularly by bacteria that can conv...

6PPD-quinone promotes ovarian cancer progression: Insights from network toxicology, machine learning, and in vitro validation.

6PPD-quinone (6PPD-Q), a toxic tire-derived antioxidant transformation product, is a pervasive environmental contaminant linked to reproductive toxici...

Prediction of groundwater total nitrogen via an interpretable ensemble machine learning framework: Implications for groundwater diversion management in complex catchments.

Groundwater total nitrogen (TN) is a key indicator of groundwater ecological security, and accurate prediction and driver identification are essential...

PM2.5 exposure induces functional alterations in pregnant rats heart and in human stem cell derived cardiac spheroids.

Air pollution is a major cardiovascular risk factor, with particulate matter (PM) posing significant threats. The Po Valley remains among Europe's mos...

An integrated water quality assessment and its prediction utilizing the CWQI, CRITIC based-improved CWQI, sensitivity analysis, multivariate techniques, machine learning algorithms and GIS approaches to determine pollution intensities in the river basin (Odisha).

The Mahanadi River is a vital water resource for the Odisha region, supporting both local agriculture and drinking water needs. However, increasing hu...

Interpretable machine learning assisted optimization of antimony adsorption by metal-organic frameworks: combined with characterization methods for adsorption mechanism analysis.

Antimony (Sb) contamination in aquatic environments poses serious risks to human health and ecosystem integrity. Conventional remediation techniques f...

Highly sensitive superhydrophobic SERS substrate combined with machine learning for precise identification and classification of nanoplastics.

The ubiquitous use of plastics in modern society is accompanied by the pervasive occurrence of plastic pollution. Of particular concern are nanoplasti...

NeuroCardioSense (NCS): A Time-Aware Fuzzy Decision Framework for Multi-Lead ECG Classification and Arrhythmia Detection.

Accurate classification of electrocardiogram (ECG) signals is essential for automated arrhythmia detection and clinical decision support. Existing dee...

Harnessing machine learning to explore influencing mechanism in the dual pro-environmental intention-behavior gap.

Fostering pro-environmental behaviors (PEBs) is crucial for advancing low-carbon development. A significant obstacle in this endeavor is the intention...

Identifying Shared Diagnostic Genes and Underlying Mechanisms Between Skin Exposure to Air Pollution and Skin Barrier Damage: A Bioinformatics Approach.

Air pollution poses a significant threat to skin health, contributing to inflammation, aging, and disruption of the epidermal barrier. This study aims...

Spatial analysis of air pollution and cancer prevalence in Texas: a machine learning approach using satellite remote sensing data.

Cancer prevalence in the world has been attributed to exposure to air pollutants. However, spatial analyses utilizing remote sensing data have been li...

EFFNet: Efficient feature fusion network for left ventricular hypertrophy identification based on 12-lead electrocardiogram signals.

BACKGROUND: Left ventricular hypertrophy (LVH) is a common cardiovascular disorder, yet its detection from electrocardiogram (ECG) signals remains cha...

Digital Stethoscope-Derived Single-Lead Electrocardiogram and Artificial Intelligence to Detect Low Ejection Fraction: A Multicenter Observational Study.

OBJECTIVES: Identifying patients likely to benefit from an echocardiogram before surgery is prudent in resource-limited settings. Recently, artificial...

Carbon quantum dots exacerbate Parkinson's disease-like pathology through microglia-mediated neuroinflammatory responses with toxicological implications.

The global burden of Parkinson's disease (PD) is projected to double by 2050, with early-onset cases demonstrating accelerated progression and limited...

Three-Dimensional Burrowing Behavior of Earthworms for Ecotoxicological Studies.

Earthworm behavioral biomarkers provide more sensitive and comprehensive assessments for environmental contaminants. However, natural soil opaqueness ...

Selective Sensing of Hydrogen and Ammonia Using a Single CMOS-Compatible Sensor and Transfer Learning Methods.

The development of high-performance gas sensors is crucial for ensuring safety and efficiency in the emerging hydrogen economy, particularly for detec...

Source identification of sudden water pollution events in the Dongliao River using a hybrid machine learning framework.

Rapid and accurate identification of pollution sources is critical for emergency management but remains challenged by the high computational cost and ...

Advancing operational global aerosol forecasting with machine learning.

Aerosol forecasting is important for air-quality management, health risk assessment and climate change mitigation1,2. However, it is more complex than...

Real-time digital prescriptions unlock influenza dynamics: evidence from 21 million transactions.

Traditional influenza surveillance suffers from 1-2 week reporting delays that compromise outbreak response. We analyzed 21.08 million digital prescri...