Acute and subacute oral toxicity of copper oxide nanoparticles in female albino Wistar rats.

The extensive use of copper oxide nanoparticles (CuO-NPs) in various industries and their wide range of applications have led to their accumulation in different ecological niches of the environment. This excess exposure raises the concern about its potential toxic effects on various organisms including humans. However, the hazardous potential of CuO-NPs in the literature is elusive, and it is essential to study its toxicity in different biological models. Hence, we have conducted single acute dose (2000 mg/kg) and multiple dose subacute (30, 300 and 1000 mg/kg daily for 28 days) oral toxicity studies of CuO-NPs in female albino Wistar rats following OECD guidelines 420 and 407 respectively. Blood analysis, tissue aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase and acetylcholinesterase, superoxide dismutase, catalase, lipid malondialdehyde and reduced glutathione assays, and histopathology of the tissues were carried out. The higher dose treatments of the acute and subacute study caused significant alterations in biochemical and antioxidant parameters of the liver, kidney and brain tissues of the rat. In addition, histopathological evaluation of these three organs of treated rats showed significantly high abnormalities in their histoarchitecture when compared to control rats. We infer from the results that the toxicity observed in the liver, kidney and brain of treated rats could be due to the increased generation of reactive oxygen species by CuO-NPs.

Machine learning aided experimental approach for evaluating the growth kinetics of Candida antarctica for lipase production.

A hybrid machine learning (ML) aided experimental approach was proposed in this study to evaluate the growth kinetics of Candida antarctica for lipase production. Different ML models were trained and optimized to predict the growth curves at various substrate concentrations. Results on comparison demonstrate the superior performance of the Gradient boosting regression (GBR) model in growth curves prediction. GBR-based growth kinetics was found to be matching well with the results of the conventional experimental approach while significantly reducing the experimental effort, time, and resources by âˆ¼ 50%. Further, the activity and enzyme kinetics of lipase produced in this study was investigated on hydrolysis of p-nitrophenyl butyrate resulting in a maximum lipase activity of 24.07 U at 44 h. The robustness and significance of developed kinetic models were ensured through detailed statistical analysis. The application of the proposed hybrid approach can be extended to any other microbial process.