Environmental Health

Therapeutic potential of enzymes, neurosteroids, and synthetic steroids in neurodegenerative disorders: A critical review.

Neurodegenerative disorders present a significant challenge to healthcare systems, mainly due to the limited availability of effective treatment options to halt or reverse disease progression. Endogenous steroids synthesized in the central nervous system, such as pregnenolone (PREG), dehydroepiandrosterone (DHEA), progesterone (PROG), and allopregnanolone (ALLO), have been identified as potential ...

Machine learning for ammonia volatilization prediction and slurry application management.

Anthropogenic ammonia emissions primarily originate from agriculture, especially field fertilization. These emissions represent nitrogen loss for farmers and contribute to air pollution, posing risks to human health and the environment. Estimating ammonia emissions is crucial for national inventories and policy-making. Various models exist for predicting emissions, including mechanistic, empirical...

Black carbon in urban Jinan: Variations, health risks, and driving factors analyzed with machine learning.

Atmospheric black carbon (BC) significantly impacts climate change, atmospheric environment, and human health. To explore the temporal variation of BC...

Estimating soil cadmium concentration using multi-source UAV imagery and machine learning techniques.

Urbanization and industrialization have led to widespread soil heavy metals contamination, posing significant risks to ecosystems and human health. Co...

Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh.

Coal mining soils are highly susceptible to heavy metal pollution due to the discharge of mine tailings, overburden dumps, and acid mine drainage. Dev...

Temporally boosting neural network for improving dynamic prediction of PM concentration with changing and unbalanced distribution.

Increasing medical research evidence suggests that even low PM concentrations may trigger significant health issues. Hence, an accurate prediction of ...

Alzheimer's Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis.

A growing body of literature has examined the impact of neighborhood characteristics on Alzheimer's disease (AD) dementia, yet the spatial variability...

Utilizing artificial intelligence to predict and analyze socioeconomic, environmental, and healthcare factors driving tuberculosis globally.

Tuberculosis (TB) is a major global health issue, contributing significantly to mortality and morbidity rates worldwide. Socioeconomic, environmental,...

A sustainable system for predicting appliance energy consumption based on machine learning.

Nowadays, energy conservation is a top priority for sustainable societies, which focus on environmental and economic sustainability to address fossil ...

AI analysis for ejection fraction estimation from 12-lead ECG.

Heart failure (HF) remains a leading global cause of cardiovascular deaths, with its prevalence expected to rise in the upcoming decade. Measuring the...

Methodological Integration of Machine Learning and Geospatial Analysis for PM Pollution Mapping.

Air pollution mitigation necessitates accurate spatial modelling to inform public health interventions. Traditional approaches inadequately capture co...

Identifying and addressing challenges in gross pollutant trap maintenance: perspectives from the Australian stormwater industry.

A common approach to removing pollution from stormwater is through the installation of gross pollutant traps (GPTs). However, GPTs are often not maint...

Developing a novel Temporal Air-quality Risk Index using LSTM autoencoder: A case study with South Korean air quality data.

As public awareness of environmental and health issues grows, providing accurate and accessible environmental risk information is essential for inform...

Calibration and validation-based assessment of low-cost air quality sensors.

BACKGROUND: Air pollution poses a significant threat to public health. Low-cost air quality sensors (LCSs) can provide a data foundation for air quali...

Decoding the Plastic Patch: Exploring the Global Microplastic Distribution in the Surface Layers of Marine Regions with Interpretable Machine Learning.

The marine environment is grappling with microplastic (MP) pollution, necessitating an understanding of its distribution patterns, influencing factors...

Predicting the environmental fate of biodegradable mulch films: A machine learning approach for sustainable agriculture.

Biodegradable plastic mulch films (BDM) have been proposed as one of the dominant strategies for plastic pollution prevention in agriculture. As the B...

Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms.

Alga toxins have recently emerged as environmental risk factors to multiple human health issues. Mitochondrial toxicity is an essential element in the...

The efficacy and toxicity equilibrium of emodin for liver injury: A bidirectional meta-analysis and machine learning.

BACKGROUND: Emodin, a hepatoprotective agent derived from various herbs, exhibits dual effects on liver injury, necessitating further investigation in...

Assessment of POPs in foods from western China: Machine learning insights into risk and contamination drivers.

Persistent organic pollutants (POPs), including PCDD/Fs, PCBs, and PBDEs, are major environmental and food safety concerns due to their bioaccumulativ...

First report on Quantitative Structure-Toxicity Relationship modeling approaches for the prediction of acute toxicity of various organic chemicals against rotifer species.

Nowadays, organic chemicals are crucial components in virtually every aspect of daily life, serving as indispensable elements for modern society. The ...