Biochemical Assay for Measuring Diabetes Mellitus

The system of glucose-insulin control and associated problems in diabetes mellitus were studied by mathematical modeling. It is a helpful theoretical tool for understanding the basic concepts of numerous distinct medical and biological functions. It delves into the various risk factors contributing to the onset of diabetes, such as sedentary lifestyle, obesity, family history, viruses, and increasing age. The study emphasizes the importance of mathematical models in understanding the dynamic characteristics of biological systems. The study emphasizes the increasing prevalence of diabetes, especially in India, where urbanization and lifestyle changes contribute to the rising incidence. The present investigation describes the use of John Holland's evolutionary computing approach and the Genetic Algorithm (GA) in diabetes mellitus. The Genetic Algorithm is applied to address issues related to diabetes, offering a generic solution and utilizing MATLAB's Genetic Algorithm tool. The Mathematical Model provides differential equations representing glucose and insulin concentrations in the blood. The results represent testing outcomes for normal, prediabetic, and diabetic individuals, optimized with Genetic Algorithm showcased through fitness value plots. The conclusion highlights the effectiveness of Genetic Algorithm as an optimization tool in predicting optimal samples for diabetes diagnosis. The paper encourages the use of heuristic algorithms, such as Genetic Algorithms, to address complex challenges in the field of diabetes research. Future scope includes further exploration of biomathematics and Genetic Algorithm applications for enhanced understanding and management of diabetes mellitus. It is critical for people with diabetes to consistently check their blood glucose levels and follow their treatment plan.

Section: Figure 1 Causes Of Diabetes Mellitus [Source: Self]mentioning

confidence: 99%

Application of Genetic Algorithms for Medical Diagnosis of Diabetes Mellitus

Tyagi,

Kumar,

Gupta

2024

“…Diabetic kidney disease (DKD) affects about one in three people with diabetes. By 2035, about half of the world population will be affected by diabetes (Sur et al, 2023;Biswas et al, 2023;Roy et al, 2023;Medhi et al, 2023;Biswas et al, 2023), of which a sizeable proportion will develop diabetic kidney disease (Andersen et al, 1983;Price et al, 2012;Hallows et al, 2012). Sirtuin 1 Article History:…”

Section: Introductionmentioning

confidence: 99%

Effect of Borreria hispida Extract on SIRT1, HIF-1α, ET-1 and VEGFR-2 Gene Expression in NRK-52E Cells Subjected to Glucotoxicity

Markandeyan,

Ahmed,

Perumal

et al. 2024

This study aimed to investigate the impact of Borreria hispida extract on gene expression in NRK-52E cells under conditions of glucotoxicity. Gene expression analysis was conducted using RT-PCR following the exposure of cultured NRK-52E cells to glucotoxic conditions and varying concentrations of Borreria hispida extract. The results demonstrated a dose-dependent increase in SIRT1 gene expression and a concomitant decrease in HIF-1λ, ET-1, and VEGFR-2 gene expressions upon treatment with Borreria hispida extract. Additionally, molecular docking studies suggested the potential inhibition of Rho-kinase as a mechanistic explanation for these effects. Borreria hispida extract may confer renoprotective benefits against glucotoxicity-induced cellular damage. The potential therapeutic utility of Borreria hispida extracts in managing renal complications associated with conditions such as diabetes. Furthermore, our docking studies shed light on the potential molecular mechanisms underlying the observed effects, suggesting interactions between phytochemicals present in Borreria hispida extract and the JNK-1 protein. These interactions may contribute to the augmentation of SIRT1 activity, further bolstering the extract's therapeutic utility in DKD.

“…As per current reports of the World Health Organization (WHO) and the International Diabetic Federation (IDF), the global T2DM cases will cross 560 million by 2045 (WHO, 2022;Cloete et al, 2022;Sarkar et al, 2023;Lancet, 2023;Tyagi et al, 2024). A variety of medications preferable to insulin are available, but the rapid increase in T2DM cases indicates newer potential drugs with new strategies of treatment modalities are urgently required (Grover and Utreja, 2014;Padhi et al, 2020;Sur et al, 2023;Biswas et al, 2023). As a polygenic metabolic disorder, the anti-diabetic efficacy of available medicines varies according to disease condition, patient immunity, and daily habits, including their environmental factors (Pramanik, 2018;Jaiswal and Gupta, 2023).…”

Section: Introductionmentioning

confidence: 99%

Antidiabetic Potency of Flavonoids Using a Systematic Computer-Aided Drug Design Platform

Rath,

Pattnaik,

Kar

et al. 2024

Diabetic mellitus (DM) is a chronic metabolic disorder, with type 2 diabetes (T2DM) being the most prevalent type globally. Despite the availability of several target-specific drugs, the prevalence rate has remained uncontrollable, prompting a systematic exploration of plant secondary metabolites or phytochemicals for mainstream use. Among all natural resources, citrus fruits like oranges, lemons, grapefruits and limes are rich sources of flavonoids and get more attention due to their higher antioxidant, anti-inflammatory and immunomodulatory effects. Additionally, researchers have employed various strategies to locate the most bioactive and drug-able flavonoids from these herbal extracts for use in managing diabetes. Therefore, the present study selected nine citrus-fruit-derived flavonoids and tested their antidiabetic potency using four target enzymes: α-amylase, AKT Serine/Threonine Kinase 1 (AKT1), dipeptidyl peptidase-4 (DPP-IV), and glucose transporter 1 (GLU1) through molecular docking studies. In addition, we have predicted the physiochemical profile, toxicity, bioavailability, lead-likeness, drug-likeness, and lethal dose of flavonoids, along with five standard antidiabetic drugs, to select the most potential candidates. We used AutoDock 4.2 for the docking study, BIOVID-Discovery Studio for the protein-ligand interaction study, SwissADME, ProTox 3.0 and Molsot tools to predict the drug-likeness profile. Individual and average docking scores indicated that naringin (-11.2 and -10.40 kcal/mol) was the most potent flavonoid, and glimepiride (-11.1 and -10.1 kcal/mol) against AKT1 had the most potential among the five antidiabetic drugs. Naringin had non-toxic profiles, a positive drug-likeness score, and ideal physicochemical profiles, which suggested that it might be the best candidate for further testing. To sum up, the computer-aided drug design platform is an important part of the current drug discovery module to accelerate phyto-based drug discovery within limited time and resources.