Disease prediction using computer-based methods is now an established area of research. The importance of technological intervention is necessary for the better management of disease, as well as to optimize use of limited resources. Various AI-based methods for disease prediction have been documented in the literature.
View Article and Find Full Text PDFClimate change, population growth, and agricultural intensification are increasing nitrogen (N) inputs, while driving the loss of inland water bodies that filter excess N. However, the interplay between N inputs and water body dynamics, and its implications for water quality remain poorly understood. Analyzing data from 1995 to 2015 across China, here, we find a 71% reduction in the area of small (<10 m) water bodies (SWB), primarily in high-N-input agricultural regions.
View Article and Find Full Text PDFElectronic and electrical waste (e-waste) production has emerged to be of global environmental public health concern. E-waste workers, who are frequently exposed to hazardous chemicals through occupational activities, face considerable health risks. To investigate the metabolic and exposomic changes in these workers, we analyzed whole blood samples from 100 male e-waste workers and 49 controls from the GEOHealth II project (2017-2018 in Accra, Ghana) using LC-MS/MS.
View Article and Find Full Text PDFThere is growing scientific and regulatory interest in transcriptomic points of departure (tPOD) values from high-throughput in vitro experiments. To further help democratize tPOD research, here we outline 'TPD-seq' which links microplate-based exposure methods involving cell lines for human (Caco-2, Hep G2) and environmental (rainbow trout RTgill-W1) health, with a commercially available RNA-seq kit, with a cloud-based bioinformatics tool (ExpressAnalyst.ca).
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