AIMC Topic: Occupational Exposure

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A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis.

Frontiers in public health
Pneumoconiosis is a widespread occupational pulmonary disease caused by inhalation and retention of dust particles in the lungs, is characterized by chronic pulmonary inflammation and progressive fibrosis, potentially leading to respiratory and/or he...

The Influence of Blood Titanium Levels on DNA Damage in Brazilian Workers Occupationally Exposed to Different Chemical Agents.

Biological trace element research
Occupational exposure to pollutants may cause health-damaging effects in humans. Genotoxicity assays can be used to detect the toxic effects of pollutants. In the present study, we evaluated genetic damage in three populations occupationally exposed ...

Hearing loss prediction equation for Iranian truck drivers using neural network algorithm.

Work (Reading, Mass.)
BACKGROUND:  Given the high prevalence of hearing loss among truck drivers, using artificial neural networks (ANNs) to predict and detect contributing factors early can aid managers significantly.

Prediction and validation of mild cognitive impairment in occupational dust exposure population based on machine learning.

Ecotoxicology and environmental safety
OBJECTIVE: Workers exposed to dust for extended periods may experience varying degrees of cognitive impairment. However, limited research exists on the associated risk factors. This study aims to identify key variables using machine learning algorith...

Supporting the working life exposome: Annotating occupational exposure for enhanced literature search.

PloS one
An individual's likelihood of developing non-communicable diseases is often influenced by the types, intensities and duration of exposures at work. Job exposure matrices provide exposure estimates associated with different occupations. However, due t...

Preservative contact allergy in occupational dermatitis: a machine learning analysis.

Archives of dermatological research
Occupational dermatoses impose a significant socioeconomic burden. Allergic contact dermatitis related to occupation is prevalent among healthcare workers, cleaning service personnel, individuals in the beauty industry and industrial workers. Among r...

Predicting noise-induced hearing loss with machine learning: the influence of tinnitus as a predictive factor.

The Journal of laryngology and otology
OBJECTIVES: This study aimed to determine which machine learning model is most suitable for predicting noise-induced hearing loss and the effect of tinnitus on the models' accuracy.

Responsible Development of Emerging Technologies: Extensions and Lessons From Nanotechnology for Worker Protection.

Journal of occupational and environmental medicine
OBJECTIVES: This paper identifies approaches to the responsible development of emerging technologies to secure worker safety and health.

Deep Learning Neural Network-Guided Detection of Asbestos Bodies in Bronchoalveolar Lavage Samples.

Acta cytologica
INTRODUCTION: Asbestos is a global occupational health hazard, and exposure to it by inhalation predisposes to interstitial as well as malignant pulmonary morbidity. Over time, asbestos fibers embedded in lung tissue can become coated with iron-rich ...