A Bayesian Semiparametric Approach to Learning About Gene-Gene Interactions in Case-Control Studies

Gene-gene interactions are often regarded as playing significant roles in influencing variabilities of complex traits. Although much research has been devoted to this area, to date a comprehensive statistical model that addresses the various sources of uncertainties, seem to be lacking. In this paper, we propose and develop a novel Bayesian semiparametric approach composed of finite mixtures based on Dirichlet processes and a hierarchical matrix-normal distribution that can comprehensively account for the unknown number of sub-populations and gene-gene interactions. Then, by formulating novel and suitable Bayesian tests of hypotheses we attempt to single out the roles of the genes, individually, and in interaction with other genes, in case-control studies. We also attempt to identify the significant loci associated with the disease. Our model facilitates a highly efficient parallel computing methodology, combining Gibbs sampling and Transformation based MCMC (TMCMC). Application of our ideas to biologically realistic data sets revealed quite encouraging performance. We also applied our ideas to a real, myocardial infarction dataset, and obtained interesting results that partly agree with, and also complement, the existing works in this area, to reveal the importance of sophisticated and realistic modeling of gene-gene interactions.Comment: To appear in Journal of Applied Statistic

Gender-Based Comparative Study of Type 2 Diabetes Risk Factors in Kolkata, India: A Machine Learning Approach

Type 2 diabetes mellitus represents a prevalent and widespread global health concern, necessitating a comprehensive assessment of its risk factors. This study aimed towards learning whether there is any differential impact of age, Lifestyle, BMI and Waist to height ratio on the risk of Type 2 diabetes mellitus in males and females in Kolkata, West Bengal, India based on a sample observed from the out-patient consultation department of Belle Vue Clinic in Kolkata. Various machine learning models like Logistic Regression, Random Forest, and Support Vector Classifier, were used to predict the risk of diabetes, and performance was compared based on different predictors. Our findings indicate a significant age-related increase in risk of diabetes for both males and females. Although exercising and BMI was found to have significant impact on the risk of Type 2 diabetes in males, in females both turned out to be statistically insignificant. For both males and females, predictive models based on WhtR demonstrated superior performance in risk assessment compared to those based on BMI. This study sheds light on the gender-specific differences in the risk factors for Type 2 diabetes, offering valuable insights that can be used towards more targeted healthcare interventions and public health strategies.Comment: 10 pages, 7 tables,3 figures, submitted to a conferenc

A successful pregnancy occurred after isolating the offending antibody(s) and choosing appropriate sperm donor of similar phenotype

Sensitization against Rh(D )is the most common cause of haemolytic disease of fetus and newborn (HDFN). Now a days, a widespread use of antenatal and postnatal Rh immunoglobulin has resulted in marked decrease in prevalence of Rh(D) alloimmunization. Fetal loss due to other red cell antigens gain importance as there are no prophylactic immunoglobulin are available. Here, we present a case of primary infertility associated with non Rh(D) alloimmunization which was detected in a 30 year old housewife during her ongoing infertility treatment. The antibody identification workup showed patient is having multiple alloantibodies , probable anti-c, and anti-Fya. The extended phenotype shows that the husband is mismatched with the wife's phenotype in “c” and Fya. Also the probable antibody in the mother's serum are anti-c and anti-Fya which are noted to cause HDFN as per literature

Fetuin-A acts as an endogenous ligand of TLR4 to promote lipid-induced insulin resistance

Toll-like receptor 4 (TLR4) has a key role in innate immunity by activating an inflammatory signaling pathway. Free fatty acids (FFAs) stimulate adipose tissue inflammation through the TLR4 pathway, resulting in insulin resistance 1, 2, 3, 4, 5, 6, 7. However, current evidence suggests that FFAs do not directly bind to TLR4 8, 9, but an endogenous ligand for TLR4 remains to be identified. Here we show that fetuin-A (FetA) could be this endogenous ligand and that it has a crucial role in regulating insulin sensitivity via Tlr4 signaling in mice. FetA (officially known as Ahsg) knockdown in mice with insulin resistance caused by a high-fat diet (HFD) resulted in downregulation of Tlr4-mediated inflammatory signaling in adipose tissue, whereas selective administration of FetA induced inflammatory signaling and insulin resistance. FFA-induced proinflammatory cytokine expression in adipocytes occurred only in the presence of both FetA and Tlr4; removing either of them prevented FFA-induced insulin resistance. We further found that FetA, through its terminal galactoside moiety, directly binds the residues of Leu100–Gly123 and Thr493–Thr516 in Tlr4. FFAs did not produce insulin resistance in adipocytes with mutated Tlr4 or galactoside-cleaved FetA. Taken together, our results suggest that FetA fulfills the requirement of an endogenous ligand for TLR4 through which lipids induce insulin resistance. This may position FetA as a new therapeutic target for managing insulin resistance and type 2 diabetes

Vapor of Volatile Oils from <em>Litsea cubeba</em> Seed Induces Apoptosis and Causes Cell Cycle Arrest in Lung Cancer Cells

<div><p>Non-small cell lung carcinoma (NSCLC) is a major killer in cancer related human death. Its therapeutic intervention requires superior efficient molecule(s) as it often becomes resistant to present chemotherapy options. Here we report that vapor of volatile oil compounds obtained from <em>Litsea cubeba</em> seeds killed human NSCLC cells, A549, through the induction of apoptosis and cell cycle arrest. Vapor generated from the combined oils (VCO) deactivated Akt, a key player in cancer cell survival and proliferation. Interestingly VCO dephosphorylated Akt at both Ser⁴⁷³ and Thr³⁰⁸; through the suppression of mTOR and pPDK1 respectively. As a consequence of this, diminished phosphorylation of Bad occurred along with the decreased Bcl-xL expression. This subsequently enhanced Bax levels permitting the release of mitochondrial cytochrome c into the cytosol which concomitantly activated caspase 9 and caspase 3 resulting apoptotic cell death. Impairment of Akt activation by VCO also deactivated Mdm2 that effected overexpression of p53 which in turn upregulated p21 expression. This causes enhanced p21 binding to cyclin D1 that halted G1 to S phase progression. Taken together, VCO produces two prong effects on lung cancer cells, it induces apoptosis and blocked cancer cell proliferation, both occurred due to the deactivation of Akt. In addition, it has another crucial advantage: VCO could be directly delivered to lung cancer tissue through inhalation.</p> </div

VCO induces apoptosis in A549 lung cancer cells.

<p>(<b>A</b>) Annexin-Cy3 (red) and 6-CFDA (green) double staining of apoptotic cells was examined by fluorescence microscopy where VCO treated A549 cells showed both green and red stains and control (untreated) cells stained green only. (<b>B</b>) Percentage of apoptotic A549 cells was measured at different time points (0 h, 12 h, 24 h, 36 h) with VCO treatments. (<b>C</b>) Mitochondrial membrane potential was observed in control and VCO exposed (36 h) A549 lung cancer cells by JC-1 staining assay. (<b>D</b>) Apoptotic DNA fragmentation was observed by VCO treated A-549 cells on 1.5% agarose gel electrophoresis. Data are presented as means ± SEM of three independent experiments. *p<0.05, **p<0.01 versus control (0 h). Bar represents 20 µm.</p