Environmental Health

Metal-Organic Frameworks for Toxic Gas Sensing: From Molecular Engineering to Device Integration.

In recent years, metal-organic frameworks (MOFs) have emerged as highly promising materials for advanced gas sensing owing to their tunable pore structures, high surface area, and chemical versatility. The urgent need for portable, selective detection of industrial pollutants and chemical warfare agents motivates a comprehensive assessment of MOF-based sensors. This review examines the evolution o...

Multifunctional Piezoelectric Foams Enabling Noise Absorption and Deep-Learning-Driven Motion Recognition.

Industrial and traffic noise pollution has emerged as a growing concern. However, conventional porous materials are restricted by high density and limited functionality. Lightweight and multifunctional acoustic materials are desirable for environmental and wearable applications. In this study, the bacterial cellulose-Ba0.85Ca0.15Zr0.1Ti0.9O3 (BCZT)-melamine composite foams (BZMFs) with hierarchica...

Spatial distribution of per- and polyfluoroalkyl substances in the Yellow River basin: Role of suspended sediment on the PFAS partition and accumulation.

The widespread occurrence of per- and polyfluoroalkyl substances (PFAS) and emerging substitutes in riverine environments has raised growing concern, ...

Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications.

Heavy metals, including lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg), are pervasive environmental toxicants increasingly recognized as nont...

Drug-induced multisystem syndromes: An overlooked challenge in pharmacovigilance.

BackgroundDrug-Induced Multisystem Syndromes (DIMS) represent a clinically significant yet under-recognized group of delayed immune-mediated adverse d...

Environmental Heavy Metal Pollution Disrupts Heading in Major Cereal Crops: Mechanisms and Implications for Food Security.

Heavy-metal contamination threatens cereal food security through a dual pathway: it destabilizes reproductive development around heading and increases...

Machine-learning-guided inverse design of lead-free relaxors enabled by multimodal literature mining.

Lead-free relaxors combining high permittivity with thermal robustness are essential for integrated electronics and power systems operating at elevate...

Hybrid genome‑scale metabolic modeling and machine learning reveal cost‑efficient strategies for phototrophic polyhydroxybutyrate production in Rhodopseudomonas palustris.

Plastic pollution from fossil-based materials is a major global environmental challenge. Microbially derived bioplastics, such as polyhydroxybutyrate ...

Investigation of the molecular network underlying PET-MPs-induced inflammatory bowel disease via integrated machine learning and molecular docking approaches.

Polyethylene terephthalate microplastics (PET-MPs), as environmental contaminants, have raised significant concerns due to their potential toxicity, l...

Coupling Hydrological Connectivity with Environmental Thresholds for Targeted Legacy Phosphorus Management in Subtropical Watersheds.

Legacy phosphorus (P) stored in soils from historical human activities can be remobilized under changing hydrological conditions, undermining water qu...

Trends in vibrational spectroscopy on optical waveguides.

Vibrational spectroscopy, due to its inherent specificity in providing a spectral "fingerprint" of molecular composition, is an important tool to iden...

Machine learning-assisted screening of natural product database for the identification of novel chalcone-based derivative as a potent DHODH inhibitor in cancer therapy.

BACKGROUND: Dihydroorotate dehydrogenase (DHODH), an important enzyme in de-novo pyrimidine synthesis, and its dysregulation has been frequently assoc...

Regulatory patterns and driving factors of soil aggregates on the environmental behavior of heavy metals: A global study based on meta-analysis and machine learning.

Soil heavy metal (HM) pollution poses a global threat, yet the aggregate-governed relationships and mechanisms linking HM adsorption, distribution, an...

An Ultrasoft Initiator-Free Eutectogel for Strain Sensing and Gesture Recognition Assisted by Machine Learning.

With the rapid development of Internet of Things and artificial intelligence technologies, flexible wearable sensors have shown great potential in hum...

Understanding the importance of social and environmental stressors in predicting maternal sleep quality during late pregnancy.

Poor pregnancy sleep quality has been linked to adverse pregnancy and birth outcomes, particularly in late pregnancy when sleep symptoms worsen. We ai...

Simulation of long-term spatio-temporal environmental dynamics using a unified benchmark of neighbor augmenting, LSTM and graph attention models.

Rapid environmental change has increased the need for predicting the long-term geospatial reliably. However, accurately modeling spatio-temporal geosp...

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. ...

Artificial intelligence in dermatology: Clinical promise and environmental impact.

Artificial intelligence (AI) is reshaping dermatology through diagnostic image analysis, clinical documentation, and patient communication tools. Howe...

Assessing long-term spatiotemporal patterns of PM2.5 using hybrid geostatistical modeling.

Air pollution and fine particulate matter (PM2.5) pose significant environmental and public health challenges, particularly in rapidly urbanizing regi...

Analysis of vehicle-related non-exhaust PM10 and emerging pollutants in Beijing with machine-learning.

With the growth of electric vehicles, non-exhaust PM10 is becoming the dominant contributor to vehicle-related particulate pollution and a source of e...