Environmental Health

Cancer as a main contributor to multimorbidity in the UK Biobank: analysis of risk factors using conventional statistical modelling and machine learning.

Cancer frequently co-occurs with other chronic conditions, contributing substantially to disease burden. Multimorbidity is an increasing public health concern, resulting from complex interactions between sociodemographic, lifestyle, and environmental factors. The study aimed to identify the main predictors of multimorbidity using both conventional statistical models and machine learning approaches...

Colonoscopy Quality and Strategies for Improvement.

Colonoscopy plays a pivotal role in colorectal cancer (CRC) screening and reduces CRC incidence and mortality. Its effectiveness depends on colonoscopist performance, which can vary. Missed lesions during colonoscopy can lead to post-colonoscopy CRC (PCCRC), making high-quality colonoscopy essential for maximizing the preventive benefit of CRC screening. This review highlights the significance of ...

Diffusion for Diffusion: A versatile multiphysics fields refinement framework in pollutants transportation.

Refining coupled multiphysics fields in pollutant transport from sparse measurements is critical for environmental risk assessment and industrial poll...

Modeling and forecasting vibrio vulnificus concentration of long-range dependence on marine environmental conditions.

Vibrio vulnificus (vvh) is an epidemiologically significant bacterium that naturally occurs in coastal waters under favorable environmental conditions...

Multimodal deep learning for acute myocardial infarction detection from 12-lead electrocardiogram: a multi-centre study with cross-hospital validation.

AIMS: Acute myocardial infarction (AMI) remains a leading global cause of mortality, where timely diagnosis is critical to enable early intervention. ...

A semi-supervised learning-based framework for quantifying litter fluxes in river systems.

Supervised deep learning methods have been widely employed to detect floating macroplastic litter (>5 mm) in (fresh)water bodies. However, few studies...

Machine learning-assisted sensing platform for simultaneous and visual detection of tetracycline hydrochloride and ofloxacin.

Antibiotics are essential in modern medicine; however, their overuse and improper disposal have caused significant environmental contamination, which ...

IFCNN-based fusion of GAF and MTF encoded near-infrared spectral images for quantitative analysis of microplastics.

Microplastics pollution has become the second-largest global environmental issue. Therefore, accurate and rapid quantification of microplastics is of ...

Characterization, sources, and driving factors of particulate matter (PM) pollution in a desert hinterland city: Insights from Hotan, China.

Atmospheric particulate matter (PM) pollution is severe in southern Xinjiang, especially near the Taklimakan Desert, where air quality is affected by ...

Applying machine learning to explore association models between environmental hormones, breast cancer, and a dietary and health education improvement program: an investigative study.

Traditional risk factor screening for breast cancer focuses on reproductive and family history. However, an increasing number of young patients withou...

Virtual screening and experimental validation of small-molecule compounds targeting AR in prostate cancer.

Prostate cancer ranks as the second most prevalent malignancy among men, with its progression predominantly driven by androgen receptor (AR) signaling...

Microbial-mycotoxin interactions in food: A review of ecotoxicological implications and omics approaches for understanding detoxification mechanisms.

Mycotoxins, toxic secondary metabolites produced by fungi such as Aspergillus, Fusarium, and Penicillium, frequently contaminate food supplies, posing...

UVENet: A novel end-to-end model for temporal consistency in underwater video enhancement.

Deep-sea submersibles collect extensive underwater data, supporting the study and exploration of marine ecosystems. Efficient processing and analysis ...

GDT-Net: Multi-level feature extraction network for precise diagnosis of atrial and ventricular fibrillation.

Atrial fibrillation (AFIB) and ventricular fibrillation (VFIB) are two critical cardiovascular diseases, where accurate diagnosis is essential for tim...

Leveraging the ADAM10 prodomain for selective inhibition to enhance recovery after myocardial infarction.

BACKGROUND AND PURPOSE: Inflammation plays a crucial role in the development and progression of numerous acute and chronic diseases such as myocardial...

Deep neural network-based detection of lead contamination via Förster resonance energy transfer in live cells.

Lead (Pb), a heavy metal with extensive industrial applications, poses significant risks to human health and the environment. These detrimental effect...

Deep-learning-driven spectral image analysis for intelligent monitoring of multiple pesticides and antibiotics.

With the widespread use of pesticides and antibiotics in agriculture and healthcare, their associated environmental pollution and potential health haz...

Electrochemical sensors revolutionize plant nitrogen monitoring: Real-time, in situ detection for precision agriculture.

Driven by the dual imperatives of global food security and agricultural sustainability, dynamic plant nitrogen monitoring technology has emerged as a ...

Deep learning advances high-throughput toxicity screening of chemicals at multi-biological levels.

Deep learning (DL) has emerged as a powerful tool for modeling unstructured data, thereby improving prediction accuracy and expanding the application ...

Substance flow analysis combined with neural networks for predicting and reducing lead pollution in the secondary lead industry.

The recycling of spent lead-acid batteries has become increasingly crucial for lead (Pb) supply, yet it generates significant secondary pollutants inc...