Environmental Health

Use of Electrocardiograms to Identify Coronary Artery Disease: Cross-Validation of an Artificial Intelligence Model.

BACKGROUND: The current gold standard for the diagnosis of coronary artery disease (CAD) is invasive angiography; however, it is an invasive procedure. Therefore, we developed an artificial intelligence model designed to predict significant CAD from a resting digital 12-lead electrocardiogram (ECG). OBJECTIVES: This retrospective study assessed the model's ability to predict clinically significant...

Integrated omics and machine learning uncover the molecular basis of environmental toxicant 6PPD-Qinduced non-obstructive azoospermia.

BACKGROUND: N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone (6PPD-Q), a transformation product of tire rubber antioxidants, has been increasingly recognized as an emerging environmental contaminant with potential reproductive toxicity. Although growing evidence implicates 6PPD-Q exposure in male infertility, its role in specific pathological conditions such as sertoli cell-only syndrome...

Improved prediction of heavy metal concentration in typical agricultural soil in Hainan by Machine Learning method.

As machine learning (ML) techniques continue to evolve, researchers are becoming more dedicated to applying these methods to model and predict heavy m...

AI-Driven nanofiber platforms for essential oil delivery in dry period cows: a sustainable strategy against mastitis and antimicrobial resistance.

The routine utilisation of prophylactic antibiotics in dairy cows during the dry period has been demonstrated to accelerate the rise of antimicrobial ...

Identification of Pyrrolo[2,3-d]pyrimidine-Based Dual MERTK and FLT3 Inhibitor: Hit-to-Lead, Machine Learning, Modeling, Synthesis, and Biological Evaluation.

Overexpression of MERTK and FLT3 plays a crucial role in activating signal transduction pathways in various human hematological malignancies. These si...

NeuroTDPi: Interpretable Deep Learning Models with Multimodal Fusion for Identifying Neurotoxic Compounds.

Chemical neurotoxicity remains a critical safety concern in the domains of drug development and environmental risk assessment. In these contexts, reli...

Network toxicology and experimental validation reveal TDCPP as an emerging environmental risk factor for chronic kidney disease.

BACKGROUND: Tris(1,3-dichloro-2-propyl) phosphate (TDCPP) is a widely used organophosphorus flame retardant that has raised growing concern because it...

Predicting ecological risks of heavy metals in watersheds based on interpretable machine learning models: under the framework of data scarcity.

Heavy metal contamination poses a serious threat to watershed ecosystem health, and predicting ecological risk from toxicity data is crucial for water...

Associations of fine particulate matter pollution components with overall, prostate cancer, and cardiovascular disease mortality in men with prostate cancer: A cohort study.

BACKGROUND: Over 3.5 million US men are living with prostate cancer (Pica), many with underlying cardiovascular disease (CVD). Fine particulate matter...

Integrated LIBS-Raman spectroscopic platform for concurrent elemental and molecular analysis.

Microplastic particles (MPs) are emerging environmental contaminants that can adsorb toxic metals and organic species, posing risks to ecosystems and ...

AI-assisted optimization of lead-free Rb2SiX6 (X = F, Cl, Br, and I) perovskite solar cells with a 2D buffer layer design using DFT and SCAPS-1D.

In this work, the potential of the environmentally benign double halide perovskite Rb2SiX6 (X = F, Cl, Br, and I) as a solar cell absorber is investig...

Neuroimmune Cross-Talk and Multilevel Cascades in Fentanyl Toxicity: Interplay of Hypoxic Stress, Glial Activation, and Synaptic Dysregulation in Systems-Level Neurodegeneration.

Fentanyl, an ultra-potent synthetic opioid, has traditionally been characterized by its acute toxic effects, particularly respiratory depression. Howe...

Cross-sensor spectral fusion for coastal water physicochemical assessment using optimized ensemble machine learning approaches.

Rising coastal pollution has increased use of remote sensing-machine learning monitoring frameworks. However, most studies rely on limited spectral fe...

Development of fuzzy logic algorithm for predicting heavy metal content in poultry product.

Poultry products are important global protein sources but are vulnerable to contamination by toxic metals such as copper, cadmium, lead, and arsenic. ...

Beyond Residential Ambient Concentrations: Quantifying Exposure Error and Advancing Personal PM2.5 Prediction with a Scalable Modeling Framework.

Accurate assessment of personal PM2.5 exposure is essential but challenging in large-scale epidemiology, as conventional residential ambient data ofte...

Advancing Screening of Aquatic Persistent, Mobile, and Toxic Substances via Integration of Graph Convolutional Networks and Multitask Learning.

The accurate identification of persistent, mobile, and toxic (PMT) substances, which are typical emerging contaminants, is critical for assessing risk...

HMRPred: A Machine Learning-Based Web Resource for Identification of Heavy Metal Resistance Proteins.

Heavy metal contamination poses a significant threat to environmental health, agriculture, and microbial ecosystems, necessitating the identification ...

Hierarchical Cross-scale Machine Learning for Enhanced Interpretation and Prediction of Phosphorus Removal by Metal Oxides Materials.

Phosphorus pollution necessitates advanced water remediation technologies. Metal-oxide materials show significant promise but face complexity arising ...

The association of environmental exposure with multiple sclerosis severity score: A study based on sequential data modeling.

Introduction Multiple Sclerosis (MS) is a chronic neuroinflammatory disease influenced by clinical, demographic, and environmental factors. Predicting...

AI-driven optimization of sustainable solvent-based extraction: A deep learning approach for green sample preparation.

BACKGROUND: This study introduces a data-driven deep learning framework for optimizing the extraction of plant biomolecules, aiming to improve both ef...