Environmental Health

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, such as chatbots, offer a scalable solution for supportive care; however, evidence regarding their effectiveness in resource-limited, low- and middle-income settings remains limited. OBJECTIVE: This study aimed to evaluate the feasibility, use, and pre...

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 assay conditions as defined in the Tox24 Challenge. TTR is a critical serum transport protein for thyroid hormones, and its disruption by environmental chemicals can lead to endocrine system dysregulation with serious developmental and metabolic cons...

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...

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 ...

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 m...

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...

Mapping the spatial heterogeneity of nitrogen species in road dust through integrated multi-source remote sensing and interpretable machine learning.

Nitrogen accumulation in road dust constitutes a significant yet underexplored threat to environmental quality, particularly in regions where rapid ur...