Environmental Health

Research on quality homogenization of preparations from multi-part botanical materials based on pseudotargeted metabolomics and machine learning: a case study of Cocculus orbiculatus (L.) DC.

The use of multiple medicinal parts from a single herbal source for disease treatment is a common practice in traditional Chinese medicine. However, variations in the ratio of these parts can lead to inconsistencies in product quality. Therefore, ensuring uniform quality by controlling the ratio of different plant parts is a key challenge for Traditional Chinese Medicine (TCM) products. In this st...

Conditional superiorities and unaddressed bottlenecks: a critical review of artificial intelligence for waterborne microbial detection.

Although conventional microbial detection approaches for water samples are widely applied, they still suffer from prolonged assay durations (24-72 h), low sensitivity, and the absence of real-time monitoring capacity. Artificial intelligence (AI) has demonstrated conditional advantages in specific experimental environments, such as achieving a sensitivity of 99% for detecting Cryptosporidium and G...

A deep learning model for predicting daily PM2.5 concentration in response to emission reduction.

Air pollution remains a leading global health threat, with fine particulate matters (PM2.5) causing millions of premature deaths annually. Chemical tr...

Protective Effects of p-Coumaric Acid Against Lead-Induced Hepatic Oxidative Stress, Inflammation, and Apoptosis: Integrated Molecular and Machine Learning-Based Analysis.

Lead (Pb) exposure is a major environmental health concern that induces hepatic injury through oxidative stress, inflammation, and apoptosis. This stu...

An end-to-end deep learning pipeline for urban noise monitoring with spatiotemporal analysis.

Urban noise pollution is a critical public health problem affecting millions of people around the world. Current acoustic monitoring systems largely f...

Exploring the non-linear impacts of environmental and anthropogenic factors on land surface temperature in southwestern Bangladesh using explainable AI.

Land surface temperature (LST) in southwestern Bangladesh has changed rapidly in recent years due to both environmental conditions and human activitie...

Screening of core targets for Di(2-ethylhexyl) Phthalate-related gastric cancer based on machine learning, molecular docking, and SHAP analysis.

PURPOSE: Given the existing uncertainties regarding the link between Di(2-ethylhexyl) phthalate (DEHP) exposure and gastric cancer (GC) progression, t...

Acute Aluminum Phosphide Poisoning: Evolving Perspectives on Toxic Mechanisms and Evidence-Based Clinical Management.

Aluminum phosphide (AlP) is a chemical compound that is used as a pesticide for suicidal purposes and can cause death, and it poses a challenge to hea...

Automated analysis of wearable ECG: machine learning methods, preprocessing pipelines, and benchmark datasets.

Wearable and mobile electrocardiography (ECG) has rapidly expanded access to rhythm monitoring outside of clinical settings, but the single-or few-lea...

Developmental neurotoxic effects of organophosphate flame retardants (OPFRs) across different life stages and the central role of pioneer transcription factors Neurog2/Ascl1 during the embryonic period.

In contemporary consumer products markets, organophosphorus flame retardants (OPFRs) are widely used, associated with non-carcinogenic health risks an...

Machine Learning-Driven Biomimetic MXene-Based Sensor Array for Ultraefficient Multiplexed Oxyanions Detection.

The pervasive contamination of groundwater by highly toxic oxyanions necessitates advanced sensing technologies capable of high-throughput screening. ...

Detecting Performance Drift in AI Models for Medical Image Analysis Using CUSUM Chart.

The use of artificial intelligence (AI) models to assist clinical decisions for diagnosis and prediction has been shown to improve patient outcomes an...

Hybrid multilayer perceptron models optimized by evolutionary algorithms for urban air quality forecasting: a case study of Shiraz, Iran.

Accurate air quality predictions are vital for environmental management and public health, especially in urban areas facing rising pollution levels. T...

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape lea...

Air pollution and autism-like traits: sensitive periods and joint effects of exposure to sources and constituents of fine particulate matter.

Growing evidence suggests that exposure to air pollution during early life is associated with autism spectrum disorder (ASD). However, results have be...

Dynamic urban land surface monitoring in developing countries using remote sensing and machine learning: real-time investigational approach.

Land Surface Changes (LSC) are significantly impacted by dynamic environmental variations, particularly armed conflicts and associat-ed failures, acco...

Portraying Ethical Risks of Medical AI: Mixed Methods Study From Connotation Definition to a Survey on Physicians' Cognition.

BACKGROUND: Ethical risks of medical artificial intelligence (AI) are a global concern, but existing understanding remains fragmented without an integ...

Future mercury emissions from coal-fired power plants in China under dual-carbon targets: An emission-driven sensitivity assessment integrating machine learning and GEOS-Chem modeling.

Mercury (Hg) is a highly toxic trace metal that poses severe threats to ecosystems and human health. Coal-fired power plants (CFPPs) have historically...

Ultrasound assisted persulfate for enhanced degradation of Alizarin Red S with experimental design and AI driven optimization.

This study investigates the enhanced degradation of Alizarin Red S (ARS) from aqueous solutions using an ultrasound-assisted persulfate (PS/US) system...

Explainable machine learning integrating environmental chemical biomarkers and maternal clinical factors for prediction of hypertensive disorder of pregnancy.

Hypertensive disorders of pregnancy (HDP) represent a leading cause of maternal morbidity, yet environmental chemical contributors to HDP risk remain ...