Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for early risk prediction. This study aimed to develop a predictive model for occupational As exposure and hypertension using metabolomics and machine learning. A total of 365 male smelting workers from southern regions were selected.

Associations of occupational exposure to micro-LiNiCoMnO particles with systemic inflammation and cardiac dysfunction in cathode material production for lithium batteries.

Micro-LiNiCoMnO (MNCM), a cathode material with highest market share, has increasing demand with the growth of lithium battery industry. However, whether MNCM exposure brings adverse effects to workers remains unclear. This study aimed to explore the association between MNCM exposure with systemic inflammation and cardiac function.