CRP Gene Polymorphism and Their Risk Association With Type 2 Diabetes Mellitus

BACKGROUND: C-reactive protein (CRP) is an inflammatory marker associated with T2DM, obesity, insulin resistance, and cardiovascular disease. AIM: The present study evaluates the association of CRP +1059 G/C polymorphism of the CRP gene in 100 T2D cases and 100 healthy controls. METHODS: Present study was done by allele specific PCR method to study the CRP gene polymorphism in study subjects. RESULTS: Study found that CRP (+1059 G/C) genotype distribution among case and controls was found to be significant (p=0.001), Higher CRP C allele frequency (0.16) was observed compared to controls (0.04). CRP +1059 GC and CC had 2.72 (1.12-6.61), 20.56 (1.16-362.1) risk for T2D. It has been observed, HTN, Obesity, Smoking and alcoholism was found to be associated with increased risk of T2D, and a significant difference was observed in biochemical parameters. CONCLUSION: Study concluded that CRP gene polymorphism was found to be associated with risk of Type 2 Diabetes and risk was linked with heterozygosity and mutant homozygosity. Hypertension, Obesity, Smoking and alcoholism increases the risk of occurrence of Type 2 Diabetes

On the Effectiveness of Self-Training in MOOC Dropout Prediction

Massive open online courses (MOOCs) have gained enormous popularity in recent years and have attracted learners worldwide. However, MOOCs face a crucial challenge in the high dropout rate, which varies between 91%-93%. An interplay between different learning analytics strategies and MOOCs have emerged as a research area to reduce dropout rate. Most existing studies use click-stream features as engagement patterns to predict at-risk students. However, this study uses a combination of click-stream features and the influence of the learner’s friends based on their demographics to identify potential dropouts. Existing predictive models are based on supervised learning techniques that require the bulk of hand-labelled data to train models. In practice, however, scarcity of massive labelled data makes training difficult. Therefore, this study uses self-training, a semi-supervised learning model, to develop predictive models. Experimental results on a public data set demonstrate that semi-supervised models attain comparable results to state-ofthe-art approaches, while also having the flexibility of utilizing a small quantity of labelled data. This study deploys seven well-known optimizers to train the self-training classifiers, out of which, Stochastic Gradient Descent (SGD) outperformed others with the value of F1 score at 94.29%, affirming the relevance of this exposition

Autophagy Paradox of Cancer: Role, Regulation, and Duality

Autophagy, a catabolic process, degrades damaged and defective cellular materials through lysosomes, thus working as a recycling mechanism of the cell. It is an evolutionarily conserved and highly regulated process that plays an important role in maintaining cellular homeostasis. Autophagy is constitutively active at the basal level; however, it gets enhanced to meet cellular needs in various stress conditions. The process involves various autophagy-related genes that ultimately lead to the degradation of targeted cytosolic substrates. Many factors modulate both upstream and downstream autophagy pathways like nutritional status, energy level, growth factors, hypoxic conditions, and localization of p53. Any problem in executing autophagy can lead to various pathological conditions including neurodegeneration, aging, and cancer. In cancer, autophagy plays a contradictory role; it inhibits the formation of tumors, whereas, during advanced stages, autophagy promotes tumor progression. Besides, autophagy protects the tumor from various therapies by providing recycled nutrition and energy to the tumor cells. Autophagy is stimulated by tumor suppressor proteins, whereas it gets inhibited by oncogenes. Due to its dynamic and dual role in the pathogenesis of cancer, autophagy provides promising opportunities in developing novel and effective cancer therapies along with managing chemoresistant cancers. In this article, we summarize different strategies that can modulate autophagy in cancer to overcome the major obstacle, i.e., resistance developed in cancer to anticancer therapies

Association of Genetic Variants of KCNJ11 and KCNQ1 Genes with Risk of Type 2 Diabetes Mellitus (T2DM) in the Indian Population: A Case-Control Study

Type 2 diabetes mellitus (T2DM) is a polygenic metabolic disease described by hyperglycemia, which is caused by insulin resistance or reduced insulin secretion. The interaction between various genetic variants and environmental factors triggers T2DM. The aim of this study was to find risk associated with genetic variants rs5210 and rs2237895 of KCNJ11 and KCNQ1 genes, respectively, in the development of T2DM in the Indian population. A total number of 300 cases of T2DM and 100 control samples were studied to find the polymorphism in KCNJ11 and KCNQ1 through PCR-RFLP. The genotype and allele frequencies in T2DM cases were significantly different compared to the control population. KCNJ11 rs5210 and KCNQ1 rs2237895 variants were found to be significantly associated with risk of T2DM in dominant (KCNJ11: OR, 2.07; 95% CI, 1.30–3.27; p−0.001; KCNQ1: OR, 2.33; 95% CI, 1.46–3.70; p−0.0003) and codominant models (KCNJ11: OR, 1.76; 95% CI, 1.09–2.84; p−0.020; KCNQ1: OR, 1.85; 95% CI, 1.16–2.95; p−0.009). We also compared clinicopathological characteristics between cases and control and observed a significant difference in all the parameters except HDL, gender, and family history. In this study, clinicopathological data with a carrier of a variant allele of both KCNJ11 and KCNQ1 genes were also analysed, and a significant association was found between the carrier of a variant allele with gender and PPG in KCNJ11 and with triglyceride in KCNQ1. We confirm the significant association of KCNJ11 (rs5210) and KCNQ1 (rs2237895) gene polymorphism with T2DM, indicating the role of these variants in developing risk for T2DM in Indian population

BCL-2 (-938C>A), BAX (-248G>A), and HER2 Ile655Val Polymorphisms and Breast Cancer Risk in Indian Population

Breast cancer is the most common carcinoma in women worldwide. The present case-control study was aimed to examine the association of BCL-2 (-938C> A), BAX (-248G > A), and HER2 (I655V i.e. A > G) polymorphisms with breast cancer risk in Indian population. This study enrolled 117 breast cancer cases and 104 controls. BCL-2 (-938C > A), BAX (-248G > A), and HER2 Ile655Val polymorphisms were screened by PCR-RFLP method. There was no significance difference in the allelic and genotype frequency of the BCL-2 (-938C > A) and BAX (-248G > A) polymorphisms between cases and controls. In relation to HER2 Ile655Val polymorphism, the statistical analysis of observed genotypic frequencies showed significant association (p-0.0059). Compared to Ile/Ile (A/A) genotype, frequency of Ile/Val (A/G) genotype was significantly higher among cases than in control group and observed to increase the breast cancer risk (OR, 2.43; 95%CI, 1.32–4.46; p-0.004). The frequency of Val (G) allele was significantly higher in cases as compared to controls (6.83% vs 2.88%, resp.). Compared to Ile (A) allele, significant increase in the risk of breast cancer was observed with Val (G) allele (OR, 2.21; 95% CI, 1.35–3.63; p-0.0016). We observed significant association between HER2 Ile655Val polymorphism and breast cancer risk under the dominant (OR = 2.52; 95% CI: 1.41–4.51; p-0.001) and codominant (OR, 2.24; 95% CI: 1.23–4.09; p-0.008) model. In our study, BCL-2 (-938C > A) and BAX (-248G > A) polymorphism were not found to be associated with breast cancer risk. This present study for the first time shows significant association of HER2 Ile655Val polymorphism with risk of breast cancer in Indian population. Therefore, we suggest that each population need to evaluate its own genetic profile for breast cancer risk that may be helpful for better understanding the racial and geographic differences reported for breast cancer incidence and mortality