219 research outputs found
RESVERATROL INHIBITS EXPRESSION OF CANCER-SPECIFIC PENTOSE PHOSPHATE PATHWAY ENZYME TKTL1
No full textObjective: The objective of this study was to identify cancer-specific metabolic enzyme Transketolase-like 1 (TKTL1) from pentose phosphate pathway as target of resveratrol, a naturally occurring nutraceutical.Methods: Methylthiazolyldiphenyl-tetrazolium bromide assay and trypan blue assay were used for the estimation of growth and proliferation. Reactive oxygen species (ROS) was estimated using 2’-7’-Dichlorodihydrofluorescein diacetate while reduced glutathione (GSH) was estimated using commercially available kit. Promoter activity, reverse transcription polymerase chain reaction, and western blotting were used for expression analysis.Results: Resveratrol treatment in HeLa cells inhibited proliferation, promoted ROS, and reduced intracellular GSH levels. In TKTL1 promoter activity assay, we found that resveratrol treatment directly inhibited promoter activity of TKTL1. Resveratrol inhibited both mRNA and protein expression of TKTL1 in a dose-dependent manner.Conclusion: This is the first report where we show that resveratrol inhibits cancer-specific isoform TKTL1 as one of its targets to elicit its anticancer activity.</jats:p
Optimization of an M/M/1/N Feedback Queue with Retention of Reneged Customers
No full textCustomer impatience has become a threat to the business world. Firms employ various customer retention strategies to retain their impatient (or reneged) customers. Customer retention mechanisms may help to retain some or all impatient customers. Further, due to unsatisfactory service, customers may rejoin a queue immediately after departure. Such cases are referred to as feedback customers. Kumar and Sharma take this situation into account and study an M/M/1/N feedback queuing system with retention of reneged customers. They obtain only a steady-state solution for this model. In this paper, we extend the work of Kumar and Sharma by performing an economic analysis of the model. We develop a model for the costs incurred and perform the appropriate optimization. The optimum system capacity and optimum service rate are obtained. (original abstract
Stock market prediction, COVID-19 pandemic and neural networks: an SCG algorithm application
No full textPurpose: This study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011-February 2020) and during the COVID-19 (March 2020-June 2021). Design/methodology/approach: Secondary data on macroeconomic variables and Nifty 50 index spanning a period of last ten years starting from 2011 to 2021 have been from various government and regulatory websites. Also, an artificial neural network (ANN) model was trained with the scaled conjugate gradient algorithm for predicting the National Stock exchange's (NSE) flagship index Nifty 50. Findings: The findings of the study reveal that Scaled Conjugate Gradient (SCG) algorithm achieved 96.99% accuracy in predicting the Indian stock market in the pre-COVID-19 scenario. On the contrary, the proposed ANN model achieved 99.85% accuracy in during the COVID-19 period. The findings of this study have implications for investors, portfolio managers, domestic and foreign institution investors, etc. Originality/value: The novelty of this study lies in the fact that are hardly any studies that forecasts the Indian stock market using artificial neural networks in the pre and during COVID-19 periods
A Stochastic Feedback Queuing Model with Encouraged Arrivals and Retention of Impatient Customers
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High-mobility group box 1 protein (HMGB1) gene polymorphisms and cancer susceptibility: A comprehensive meta-analysis
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