Environmental Health

Ultrasensitive, Rapid, and Incubation-Free Surface-Enhanced Raman Scattering Detection of Sulfasalazine with a GF@PDDA-Ag/rGO Functionalized Membrane and Machine Learning.

Developing rapid detection methods with high sensitivity and real-time capabilities is a critical challenge for monitoring antibiotic pollution. This study presents a flexible surface-enhanced Raman scattering (SERS) sensing substrate (GF@PDDA-Ag/rGO) based on the poly dimethyl diallyl ammonium chloride (PDDA)-bridged assembly for real-time, ultrasensitive sulfasalazine detection. PDDA provided a ...

Integrating microbial bioremediation, multi-omics, and emerging technologies for polycyclic aromatic hydrocarbon (PAHs) detoxification.

Environmental organic pollutants, identified as Polycyclic Aromatic Hydrocarbons (PAHs), are widespread and toxic. These hydrocarbons are commonly produced by industrial activities, burning fossil fuels, and crude oil discharges. Their high hydrophobicity, tendency to bioaccumulate, and mutagenic, carcinogenic, teratogenic, and genotoxic properties lead to significant environmental and human healt...

Development and simulation of an innovative autonomous knowledge-based smart waste collection system.

Substantial efforts have been made to modernize and decarbonize the waste collection industry. Smart sensor-based waste collection (SWC) systems have ...

Daily Mortality Prediction Under Compound Environmental Stressors: Comparing Epidemiological Time Series Models and Machine Learning Models.

Accurate prediction of daily mortality is vital for effective early warning systems and the strategic allocation of medical resources, particularly gi...

In Silico Prediction of Potential Bile Salt Export Pump Inhibitors Using Pharmacophore Models and Consensus Machine Learning.

The ATP-dependent bile salt export pump (BSEP) is a transporter responsible for moving bile salts from hepatocytes into bile canaliculi. Inhibition of...

Uncovering the Global Burden and Future Trajectories of Nanomagnetite Particle Emissions from Biomass Power Plants.

Biomass power plants (BPPs) are expanding rapidly, yet the most toxicologically potent nanoscale fraction of its particulate emissions remains poorly ...

Deep Learning Model Using Transfer Learning for Detecting Left Ventricular Systolic Dysfunction: Retrospective Algorithm Development and Validation Study.

BACKGROUND: Artificial intelligence-augmented electrocardiogram (AI-ECG) models for detecting left ventricular systolic dysfunction (LVSD) often exhib...

Prediction of the respiratory disease incidence based on environmental factors using machine learning techniques in Penang, Malaysia.

Traditional statistical models often struggle to accurately predict global burden of respiratory diseases due to the complex and interdependent nature...

A Deep Neural Network for Interpreting Wearable Electrocardiogram Data in Atrial Fibrillation: Prospective Observational Diagnostic Accuracy Study.

BACKGROUND: Atrial fibrillation (AF) and atrial flutter (AFL) are common arrhythmias associated with the risk of ischemic stroke, which can be reduced...

Interplay of chemical toxicants and urban microenvironments in the oxidative potential and comprehensive risk of road dust.

The oxidative potential (OP) of urban road dust PM2.5 poses major health implications, primarily driven by key toxicants including heavy metals and po...

The impact of fuel standard on sulfur dioxide emissions: Evidence from machine learning technique.

Green transport is increasingly important in improving urban air quality. Using daily air quality monitoring data at the station level, this paper emp...

Intelligent monitoring of coastal outfalls via multi-source remote sensing image fusion.

Coastal outfalls are major point sources of pollutants entering the marine environment, severely threatening coastal water quality and marine ecosyste...

Seasonal synergistic management of PM2.5-O3-CO2 in key Chinese cities: analysis integrating synergistic indexes and machine learning models.

China confronts dual environmental challenges: PM2.5-O3 synergistic pollution and mounting CO2 emissions, demanding a strategic shift from single-poll...

Polyhydroxyalkanoates production by yeasts - Still possible?

Synthetic polymers have become ubiquitous in modern life due to their versatility, durability and low production costs, leading to a significant incre...

Spatiotemporal evolution and ecological risk assessment of heavy metals in agricultural black soils of Northeast China.

Black soils are globally important agricultural resources, yet comprehensive trans-provincial assessments of heavy metal contamination and its spatiot...

Leveraging machine learning and ten-cities observations to disentangle emission and meteorological controls on secondary inorganic aerosols in China.

Secondary inorganic aerosols ((NH4)2SO4 and NH4NO3) play a crucial role in air pollution and climate change, yet the differences in the key drivers an...

Refinement and performance benchmark for range-separated water force field.

In our previous work, we developed a CCSD(T)-level range-separated water force field that combines the power of physics-driven and machine learning mo...

Advances in single-molecule junctions for the detection of hazardous pollutants.

With the continuous development of the global economy, environmental pollution has emerged as a significant challenge impeding sustainable economic de...

Predicting Catalytic Pathways for Thiophenol Decomposition on TM-Doped MoS2: A Comparative Machine Learning Study.

Thiophenol (TP), a high-toxicity compound prevalent in pharmaceuticals and industrial products, necessitates efficient catalytic decomposition methods...

Environmental drivers of soil potentially toxic metals identified through multi-scenario machine learning in an arid region of Southeastern Iran.