2,4-dichlorophenoxyacetic acid (2,4-D) and arsenic cause severe and extensive biological toxicity in organisms. However, their interactions and toxic mechanisms in co-exposure remain to be fully elucidated. In this study, 28 four-week-old female rats were divided into four groups and exposed to 100 mg/L arsenic or/and 600 mg/L 2,4-D through drinking water for a period of 28 days. As a result, it was revealed that biochemical indicators (ALT, AST, ALP, blood urea nitrogen, and creatinine) were increased and decreased hormonal parameters (FSH, LH, PG, and E2) in arsenic and 2,4-D and arsenic combination-treated groups. Moreover, increased lipid peroxidation (malondialdehyde level) and decreased antioxidant status (superoxide dismutase and catalase activities) were found in the co-exposure groups compared with the individual-exposure groups. Meanwhile, severe DNA damage was observed in co-exposure groups. Additionally, the levels of apoptotic and ) and inflammation (α and ) indexes in the co-exposure groups were markedly increased, whereas the levels of anti-apoptosis index () were decreased. It was also observed that co-exposure with 2,4-D and arsenic caused more histopathological changes in tissues. Generally, these results show that co-exposure to 2,4-D and arsenic can seriously cause oxidative stress, DNA damage, apoptosis and inflammation while having toxicological risk for organisms.
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http://dx.doi.org/10.1093/toxres/tfad047 | DOI Listing |
Front Pharmacol
January 2022
Shanghai Institute for Food and Drug Control, NMPA Key Laboratory for Quality Control of Traditional Chinese Medicine, Shanghai, China.
Earthworms are known to accumulate inorganic contaminants from the soil; they are also used as a traditional Chinese medicine (TCM) called , which might cause safety problems with long-term exposure. Here, this study was conducted to determine and analyze the level of heavy metal contamination such as arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), manganese (Mn), nickel (Ni), and lead (Pb) in and then explore the probabilistic health risks caused by 8 heavy metals in 98 batches of using Monte Carlo simulation. A risk assessment strategy was conducted to assess heavy metal-associated health risk of based on consumption data.
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