Environmental Health

Complex networks for modeling texture and spectral features of hyperspectral images for environmental analysis.

A complex network method is introduced for high-resolution hyperspectral image analysis and classification. The method is applied to detecting environmental pollution with the Jacaranda caroba plant species. Using confocal laser scanning microscopy (CLSM), detailed spectral data were captured from leaves exposed to different levels of potassium fluoride. Unlike most studies that focus on pixel- or...

ML-Assisted Olfactory Epidemiology Survey Urbanizing China: A Population-Based Study in Yancheng.

OBJECTIVES: Urbanization-related air pollution may be associated with olfactory dysfunction (OD) in China, yet population studies are lacking. METHODS: We conducted a cross-sectional study of 1500 participants in urbanizing Yancheng, China (2023-2025). Olfactory function was assessed via Sniffin' Sticks. Machine learning (XGBoost, k-means clustering) was used to analyze risk factors and phenotypes...

MMRCL: An interpretable multi-modal deep learning framework for predicting hERG blockers.

The human ether-a-go-go-related gene (hERG) encodes a voltage-gated potassium channel essential for cardiac action potential repolarization. Drug-indu...

From conventional monitoring to intelligent prediction: data-driven analysis of inorganic elements in atmospheric wet deposition at an urban site in Lanzhou.

Atmospheric wet deposition represents a key pathway linking atmospheric pollution to terrestrial ecosystems, with its chemical composition and deposit...

Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.

The integration of artificial intelligence, protein engineering, and sustainable nanomedicine is driving a paradigm shift in theranostics by enabling ...

Circulating tumor DNA dynamics predict pathological response and guide therapy personalization in the neoadjuvant setting.

Neoadjuvant therapy is a cornerstone of modern oncology, yet its efficacy is traditionally assessed only after treatment completion, creating a "black...

Childhood dyslexia risk elevated by heavy metal mixtures from e-waste: A machine learning-driven mixture modeling study.

Environmental heavy metal mixtures from informal e-waste recycling are potential neurotoxicants, but their link to developmental dyslexia remains uncl...

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...

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 increa...

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...