ANTIDIABETIC ACTIVITY OF BAUHINIA VAHLII Wt. and Arn. (CAESALPINIACEAE) ROOT – A BOTANICAL SOURCE FOR THE AYURVEDA DRUG MURVA

Objective: The objective of this study was to evaluate the antidiabetic potential of Bauhinia vahlii Wt. and Arn. (Caesalpiniaceae) root, a botanical source for Murva (Ayurveda drug). Methods: Ethanol extract of B. vahlii root (EEBVR) and aqueous extract of B. vahlii root (AEBVR) prepared were subjected for acute toxicity study adopting Organisation for Economic Cooperation and Development guidelines. Antidiabetic property of EEBVR and AEBVR was screened against Streptozotocin-nicotinamide-induced Diabetes Mellitus (DM). The diabetic animals were administered with standard drug glibenclamide (0.5 mg/kg), EEBVR (200 and 400 mg/kg), and AEBVR (200 and 400 mg/kg) for 21 days. Fasting blood glucose, serum triglycerides, total cholesterol, liver malondialdehyde, reduced glutathione, and glycogen were estimated along with pancreatic histological analysis. Results: EEBVR (400 mg/kg) exerted a marked antidiabetic activity among the extracts at the tested doses, as evidenced by considerable reversal of biochemical parameters that were well supported by the histopathological interpretation of pancreas. Conclusion: This study confirms the antidiabetic potential of B. vahlii root and also its traditional claim in the use of DM

Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird’s eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies