Prediction of pneumoconiosis by serum and urinary biomarkers in workers exposed to asbestos-contaminated minerals

Analyses of oxidative DNA damage among coal vendors via single cell gel electrophoresis and quantification of 8-hydroxy-2'-deoxyguanosine

Looking at the development status of Nigeria and other developing nations, most low-income and rural households often use coal as a source of energy which necessitates its trade very close to the communities. Moreover, the effects of exposure to coal mining activities are rarely explored or yet to be studied, not to mention the numerous street coal vendors in Nigeria. This study investigated the oxidative stress levels in serum and urine through the biomarker 8-OHdG and DNA damage via single cell gel electrophoresis (alkaline comet assay). Blood and urine levels of 8-OHdG from 130 coal vendors and 130 population-based controls were determined by ELISA. Alkaline comet assay was also performed on white blood cells for DNA damage. The average values of 8-OHdG in serum and urine of coal vendors were 22.82 and 16.03 ng/ml respectively, which were significantly greater than those detected in controls (p < 0.001; 15.46 and 10.40 ng/ml of 8-OHdG in serum and urine respectively). The average tail length, % DNA in tail and olive tail moment were 25.06 μm, 18.71% and 4.42 respectively for coal vendors. However, for controls, the average values were 4.72 μm, 3.63% and 1.50 for tail length, % DNA in tail and olive tail moment respectively which were much lower than coal vendors (p < 0.001). Therefore, prolonged exposure to coal dusts could lead to higher serum and urinary 8-OHdG and significant DNA damage in coal vendors observed in tail length, % DNA in tail, and olive tail moment by single cell gel electrophoresis. It is therefore established that coal vendors exhibit a huge risk from oxidative stress and assessment of 8-OHdG with single cell gel electrophoresis has proven to be a feasible tool as biomarkers of DNA damage.

Computer-Aided Diagnosis of Coal Workers' Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review

Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers' pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers' survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases. This systematic literature review (SLR) amis to categorise and summarise the feature extraction and detection approaches of computer-based analysis in CWP using chest X-ray radiographs (CXR). We conducted the SLR method through 11 databases that focus on science, engineering, medicine, health, and clinical studies. The proposed SLR identified and compared 40 articles from the last 5 decades, covering three main categories of computer-based CWP detection: classical handcrafted features-based image analysis, traditional machine learning, and deep learning-based methods. Limitations of this review and future improvement of the review are also discussed.

Artificial Intelligence in Chemistry: Current Trends and Future Directions

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.