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Breast Cancer (BC) is a genetically and clinically heterogeneous disease including complex interactions between gene-gene and gene-environment components. This study aimed, to explore whether the Glutathione S- transferase (GSTs) gene polymorphism has role in BC susceptibility. We further evaluated the frequency of four subtypes of BC based on molecular classification followed by microscopic histological analysis to study the grades of invasive ductal carcinoma (IDC).Polymorphism in GST genes in North-Indian BC patients was assessed by multiplex-PCR and PCR-RFLP methods. 105 BC patients and 145 healthy controls were enrolled for this study. Data was analyzed by calculating the odds ratio (OR) and 95% CI from logistic regression analyses.Our findings revealed that GSTM1 null genotype (OR = 2.231; 95% CI = 1.332-3.737; p-value= 0.002) is significantly associated to BC risk in ethnic North- Indian population. However, the risk for BC susceptibility in North-Indians does not appear to be associated with GSTT1 null genotype. The GSTP1 (Val/Val) genotype (OR=1.545; CI=0.663-3.605; p-value= 0.314) was also found to be susceptible for BC risk. Combination of three high risk GST genotypes association exhibiting gene-gene interaction further confirmed the increased risk to BC in this region.The results of present study indicated that polymorphism in GSTM1 and rs1695 of GSTP1 genes may influence BC development among North-Indian women. Thus, the screening of GSTM1 and GSTP1 gene should be recommended for the earlier investigation for BC as a precautionary measure.

Combination of beneficiary and supply side factors for determining what influences Postpartum family planning use in Sindh and Punjab provinces of Pakistan.A facility-based survey was conducted among 1690 married women of reproductive age (MWRA - i.e. married women between the ages of 15 and 49 years) visiting public facilities in six districts across Sindh and Punjab provinces of Pakistan.Half (53%) of the interviewed women used postpartum contraceptives. Participants who used PPFP were: 55% more likely to be from Punjab than Sindh, 39% more likely to be between 25 to 34 year of age than under 25 years. After adjusting for all variables in the model, women who delivered in the private sector remained to have lower odds of PPFP use compared to those who delivered in public facilities. Women who had four or more sons were nearly 20 time more likely to use PPFP compared to women with no sons, and the PPFP use increases with the number of sons. On the contrary, women who had four or more daughters were 16 times less likely to use PPFP compared with women with no daughters.Postpartum women are among those with the greatest unmet need as a result of a combination of beneficiary and supplier side factors. The health system's response to meet the contraceptive needs of postpartum women should work along the continuum of care from pregnancy to postpartum, with expanded method choice and alternative service delivery outlets.

Diabetes mellitus (DM), a disease of the endocrine pancreas in which either pancreatic islets secrete insufficient insulin, or there is inefficient utilization of insulin; thus, producing hyperglycemic effects and other metabolic complications.

Speaker Recognition is one of the principle problems in Speech processing. The performance of speaker recognition systems can be improved by carefully choosing and calculating suitable features, which is an arduous task. Therefore, the learning based approach has been found to be simpler, more general and with the rapid growth in Artificial Intelligence, more accurate. This paper is a comparative study of the performance of different neural networks in speaker recognition. The focus of this work is to find which of these learning algorithms is more accurate, less complex, and more generic when it comes to speaker recognition. A database of 5000 utterances, 100 for each of the 50 different speakers, in both clean and noisy environment, with varying levels of noise was used. The MFCC (Mel Frequency Cepstral Coefficients) of these utterances were used as features to train and evaluate the neural networks. Accuracy of all neural networks was expectedly very high (>90%) for clean data, large variations coming in with introduction and change in the level of noise. RBFNN has been shown to consistently perform well under all conditions. DNN was the other consistent performer and has the potential to outperform other techniques, if trained on more data.