Environmental Health

Evaluating Elicit's systematic reviews workflow in an umbrella review on air pollution and acute lower respiratory infections: a methodological study for quality appraisal.

BACKGROUND: The exponential growth of scientific publications has increased the complexity of evidence synthesis. Systematic reviews remain essential but highly resource-intensive. Large language models (LLMs) offer new opportunities to support or partially automate key steps of this process. This study evaluates the performance of Elicit's Systematic Reviews workflow in comparison to the traditio...

Machine learning identifies traffic-related pollutant mixtures as potential factors suggestive of association with osteoporosis in a cross-sectional analysis of NHANES.

The combined effect of traffic-related pollutant mixtures on osteoporosis (OP) remains unclear. This study aimed to evaluate such associations using machine learning and mixture modeling approaches. A cross-sectional analysis was conducted among 3053 participants from the National Health and Nutrition Examination Survey 2015 to 2018. Eighteen exposures were assessed, including heavy metals, polycy...

A Multidimensional Integrated Strategy for Identifying Anti-thrombotic Quality Markers(Q-Markers) in Taohong Siwu Decoction through Combined Analysis of Chemical Profiling, Bioactivity, and Pharmacodynamic Efficacy.

ETHNOPHARMACOLOGICAL RELEVANCE: Taohong Siwu Decoction (TSD), originating from the Qing Dynasty medical text Gynecology Ice Mirror, is a representativ...

Deep learning-based prediction of trace silver concentrations in marine sediments from mid-infrared spectroscopy.

Silver (Ag) is a biologically toxic element, and the rapid detection of its concentrations in environmental samples is thus of great importance. This ...

Network toxicology and bioinformatics reveal potential molecular links between cadmium exposure and pancreatic cancer.

BACKGROUND: Environmental cadmium (Cd) pollution poses a severe threat to human health due to its strong bioaccumulation and high carcinogenicity. Alt...

Fluorescence sensor array integrated with machine learning: robust discrimination of multiple metal ions using carboxyl-rich carbon dots.

Accurate identification and reliable monitoring of metal ions (MIs) are critically important due to their persistent toxicity, bioaccumulation potenti...

Integrated multi-endpoint hazard prioritization of unregulated industrial contaminants: A safety-first screening framework for emerging environmental threats.

The rapid expansion of the industrial chemical space has outpaced regulatory oversight, creating a critical regulatory and environmental blind spot re...

Unveiling population heterogeneity in health risks posed by environmental hazards using regression-guided neural network.

As environmental hazards become more frequent, it is critically important to understand their health impacts and identify individuals at disproportion...

Melan-YOLO: a dual-strategy framework with custom architectures for accurate and efficient underwater object detection.

Effective autonomous monitoring of marine litter is vital to reduce marine pollution and protect marine ecosystems. However, current underwater detect...

Nonlinear meteorological controls on coastal air quality along the Indian East Coast: Insights from a multi-city interpretable machine learning framework.

This study investigates the role of meteorological variability in shaping ambient air quality across five coastal cities along the Indian East Coast (...

Advances in multispectral and hyperspectral inversion for soil heavy metal contamination: Mechanisms, machine learning algorithms, and future perspectives.

Heavy metal (HM) contamination in soil exhibits insidious and cumulative effects, posing long-term risks to ecosystems and human health. Traditional f...

Machine Learning-Driven Prediction of Microplastic Aging Processes and Environmental Risk Assessment Across Multi-Media Systems.

Machine learning (ML) holds promise for reconstructing microplastic (MP) aging and assessing risks, but current studies rely on small-scale, accelerat...

Unveiling Hydrogen Fluoride Emission Mechanisms in Municipal Solid Waste Incineration Using a Machine Learning Approach.

Hydrogen fluoride (HF) emissions from municipal solid waste incineration (MSWI) pose significant environmental and health risks. However, their comple...

Reassessing the Heterogeneous Effects of Greenspace on Air Pollution Conditioned on Seasons, Vegetation Types, and Street Structures.

Urban greenspace is generally assumed to mitigate air pollution. However, clear large-scale real-world evidence remains limited, particularly regardin...

A case-aware feature modulation framework for defect classification in power lines.

Conductors and grounded transmission towers are separated by non-conductive overhead transmission line insulators are known as materials. They frequen...

Incorporating geospatial environmental exposure indicators in individual hypertension risk prediction: a multi-stage machine learning pipeline.

BACKGROUND: Environmental exposures are known contributors to chronic disease but are rarely incorporated into risk prediction models. OBJECTIVE: We d...

Missing data imputation in hourly CO measurements for air quality monitoring: a case study in the city of Salvador, Brazil.

Continuous and uninterrupted air quality monitoring is essential for environmental management and public policy formulation, which requires the absenc...

Probabilistic risk assessment of heavy metals sources by integrating interpretable machine learning with receptor models.

Accurate source apportionment of heavy metals (HMs) in surface water and the development of a source-based risk assessment system is essential for for...

Next generation risk assessment and new approach methodologies for sustainable management of heavy metal contaminated environments: A critical review.

Environmental stability forms the foundation for sustaining healthy ecosystems and protecting human life, but the relentless accumulation of persisten...

Integrative analysis and experimental validation of dioxin-interacting genes reveal diagnostic and prognostic biomarkers in lung adenocarcinoma.

Dioxins are persistent environmental pollutants and key components of the human exposome with established carcinogenic potential. As airborne toxicant...