Estimation of Delafloxacin Using Derivative Spectrophotometry and Area Under Curve in Bulk Material and in Laboratory Mixture

Simple, specific, rapid and accurate UV-spectrophotometric methods have been developed using a solvent acetonitrile (50 %) to determine delafloxacin in bulk material and in laboratory mixture. “Method A” is zero order derivative UV- spectrophotometry using absorbance, “Method B” is zero order derivative UV-spectrophotometry using Area Under Curve (AUC) technique, “Method C” is first order derivative UV-spectrophotometry using amplitude, “Method D” is First Order Derivative UV-spectrophotometry- AUC,“Method E” is Second Order Derivative UV-spectrophotometry using amplitude and “Method F” is second order derivative UV- spectrophotometry using (AUC) technique. The developed methods have shown excellent results in terms of linearity and range, accuracy, precision and Limit of Detection (LOD) and Limit of Quantification (LOQ). In all Methods, delafloxacin obeyed linearity in the concentration range of 2 - 12 μg/mL with (r2 > 0.999). All these methods were applied for estimation of delafloxacin in laboratory mixture. All the above mentioned methods were validated considering linearity and range, accuracy, precision, ruggedness and sensitivity

A REVIEW ON CUSTOMER CHURN PREDICTION USING MACHINE LEARNING APPROACH

Customer churn is a problem that affects firms in a variety of industries. Every time a client departs, there will be significant loss of a firm. Churn prediction refers to determining which consumers are most likely to cancel a service subscription based on how they use it. It's a crucial prediction for many firms because getting new customers is sometimes more expensive than keeping old ones. There are six faces to our suggested methodology. Data pre-processing and exploratory data analysis are performed in the first two faces. In the third phase, feature selection is considered; after that, the data is divided into two portions, train and testset, in a ratio of 80% and 20%, respectively. The most prominent prediction models, such as logistic regression, naive bayes, support vector machine, and random forests, were used on the train set, and ensemble approaches were used to evaluate how they impacted model accuracy. In addition, for hyperparameter tuning and to avoid overfitting of models, k-fold cross validation was applied. Finally, the AUC/RUC curve was used to analyse the findings obtained on the test set. Random Forest and SVM were shown to have the highest accuracy of 87 percent and 84 percent, respectively. Random Forest achieves the greatest AUC score of 94.5 percent, while SVM classifiers obtain 92.1 percent, outperforming others

In search of selective 11β-HSD type 1 inhibitors without nephrotoxicity: An approach to resolve the metabolic syndrome by virtual based screening

Over expression of 11β-HSD 1 in key metabolic tissues is related to the development of type 2 diabetes, obesity, hypertension and metabolic syndrome. Nephrotoxicity of corosolic acid (selective inhibitor of 11β-HSD 1) is recently reported, which is one of the major drawback. Therefore, it is of great interest to find out the selective 11β-HSD 1 inhibitors without nephrotoxicity. Using crystal structures of 11β-HSD 1 in complex with inhibitors as a source of structural information, a combined structure-based virtual screening approach followed by PASS toxicity prediction, Lipinski’s rule and ADME prediction was implemented to find out the potent and selective 11 β-HSD 1 analog of corosolic acid without nephrotoxicity. Two compounds with NCBI compound identification number CID59752459 (Genins of Asiatic acid) and CID 119034 (Asiatic acid) were found to be selective for the 11β-HSD 1 enzyme without nephrotoxicity which comply with Lipinski’s rule and ADME parameter defined for human use. However, none of the hits inhibited 11β-HSD 2 at 100 μM indicating their selectivity against 11β-HSD 1

In search of selective 11β-HSD type 1 inhibitors without nephrotoxicity: An approach to resolve the metabolic syndrome by virtual based screening

AbstractOver expression of 11β-HSD 1 in key metabolic tissues is related to the development of type 2 diabetes, obesity, hypertension and metabolic syndrome. Nephrotoxicity of corosolic acid (selective inhibitor of 11β-HSD 1) is recently reported, which is one of the major drawback. Therefore, it is of great interest to find out the selective 11β-HSD 1 inhibitors without nephrotoxicity. Using crystal structures of 11β-HSD 1 in complex with inhibitors as a source of structural information, a combined structure-based virtual screening approach followed by PASS toxicity prediction, Lipinski’s rule and ADME prediction was implemented to find out the potent and selective 11 β-HSD 1 analog of corosolic acid without nephrotoxicity. Two compounds with NCBI compound identification number CID59752459 (Genins of Asiatic acid) and CID 119034 (Asiatic acid) were found to be selective for the 11β-HSD 1 enzyme without nephrotoxicity which comply with Lipinski’s rule and ADME parameter defined for human use. However, none of the hits inhibited 11β-HSD 2 at 100μM indicating their selectivity against 11β-HSD 1

Antidrug Resistance in the Indian Ambient Waters of Ahmedabad during the COVID-19 Pandemic

The ongoing COVID-19 pandemic increases the consumption of antimicrobial substances (ABS) due to the unavailability of approved vaccine(s). To assess the effect of imprudent consumption of ABS during the COVID-19 pandemic, we compare the 2020 prevalence of antidrug resistance (ADR) of Escherichia coli (E. coli) with a similar survey carried out in 2018 in Ahmedabad, India using SARS-CoV-2 gene detection as a marker of ABS usage. We found a significant ADR increase for in 2020 compared to 2018 in ambient water bodies, harbouring a higher incidence of ADR E.Coli towards non-fluoroquinolone drugs. Effective SARS-CoV-2 genome copies were found to be associated with the ADR prevalence. The prevalence of ADR depends on the efficiency of WWTPs (Wastewater Treatment Plants) and the catchment area in its vicinity. In year 2018 study, prevalence of ADR was discretely distributed, and the maximum ADR prevalence recorded was ~60%; against the current homogenous ADR increase, and up to 85% of maximum ADR among the incubated E.coli isolated from the river (Sabarmati) and lake (Chandola and Kankaria) samples. Furthermore, wastewater treatment plants showed less increase in comparison to the ambient waters, which eventually imply that although SARS-CoV-2 genes and faecal pollution may be diluted in the ambient waters, as indicated by low Ct-value and E.coli count, the danger of related aftermath like ADR increase cannot be nullified. Also, Non-fluoroquinolone drugs exhibited overall more resistance than the quinolone drugs. Overall, this is probably the first ever study that traces the COVID-19 pandemic imprints on the prevalence of antidrug resistance (ADR) through wastewater surveillance and hints at monitoring escalation of other environmental health parameters. This study will make public and policyholders concerned about the optimum use of antibiotics during any kind of treatment.This work is funded by Kiran C Patel Centre for Sustainable Development at IIT Gandhinagar, UNICEF, Gujarat and UKIERI. We also acknowledge the help received from Dr. Vaibhav Srivastava, Dr. Arbind K Patel, and other GBRC staffs who contributed towards sample and data analyses.Peer reviewe