Objective ANGPTL3 and ANGPTL4 are secreted proteins that inhibit lipoprotein lipase (LPL) in vitro. Genetic variants at the ANGPTL3 and ANGPTL4 gene loci are significantly associated with plasma lipid traits. The aim of this study was to evaluate the association of plasma angiopoietin-like protein 3 (ANGPTL3) and 4 (ANGPTL4) concentrations with lipid and metabolic traits in a large community-based sample. Approach and Results Plasma ANGPTL3 and ANGPTL4 levels were measured in 1770 subjects using a validated ELISA assay. A Pearson unadjusted correlation analysis and a linear regression analysis adjusting for age, gender and race were performed. ANGPTL3 levels were significantly positively associated with low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) levels (both P < 2 ×10−5) but not triglycerides. In contrast, ANGPTL4 levels were significantly negatively associated with LDL-C and HDL-C (both P < 2 × 10−5) and positively associated with triglycerides (P=0.003). In addition, ANGPTL4, but not ANGPTL3, levels were significantly positively associated with fasting blood glucose and metabolic syndrome. Conclusions Despite having similar biochemical effects in vitro, plasma ANGPTL3 and ANGPTL4 concentrations have nearly opposite relationships with plasma lipids. ANGPTL4 is strongly negatively associated with LDL-C and HDL-C and positively with multiple features of the metabolic syndrome including triglycerides, whereas ANGPTL3 is positively associated with LDL-C and HDL-C and not with metabolic syndrome traits including triglycerides. While ANGPTL3 and ANGPTL4 both inhibit LPL in vitro and influence lipoprotein metabolism in vivo, the physiology of these related proteins and their effects on lipoproteins is clearly divergent and complex.

Post partum depression (PPD) is an important complication of child-bearing. It requires urgent interventions as it can have long-term adverse consequences if ignored, for both mother and child. If PPD has to be prevented by a public health intervention, the recognition and timely identification of its risk factors is must. We in this review have tried to synthesize the results of Asian studies examining the risk factors of PPD. Some risk factors, which are unique to Asian culture, have also been identified and discussed. We emphasize on early identification of these risk factors as most of these are modifiable and this can have significant implications in prevention of emergence of post partum depression, a serious health issue of Asian women.

Background and Purpose-Platelets bearing complement C4d were recently reported to be 99% specific for a diagnosis of systemic lupus erythematosus (SLE) and associated with neuropsychiatric lupus. We compared the prevalence of platelet C4d and investigated the clinical associations of platelet C4d in patients with acute ischemic stroke. Methods-We recruited 80 patients hospitalized for acute ischemic stroke. Stroke severity was measured by the National Institutes of Health Stroke Scale (NIH-SS). Infarct volume was determined by MRI. Platelet C4d was measured by flow cytometry. Results-Mean age was 57.9 years (range: 24.6 to 86.8 years), 58% were male, and 91% were white. Eight patients (10%) with acute ischemic stroke were platelet C4d-positive, which was significantly higher in prevalence compared to healthy controls (0%, PϽ0.0001) and non-SLE patients with immune/inflammatory disease (2%, Pϭ0.004). The median NIH-SS score and infarct volume for acute stroke patients were 6 (interquartile range [IQR]: 2 to 13) and 3.4 cc (IQR: 1.1 to 16.6), respectively. Platelet C4d-positive patients were more likely to have a severe stroke compared to those with negative platelet C4d (NIH-SS median: 17.5 versus 5, Pϭ0.003). Positive platelet C4d was independently associated with stroke severity (Pϭ0.03) after controlling for age, anticardiolipin antibody (aCL) status, and total anterior circulation of stroke involvement, and also with infarct volume (Pϭ0.005) after controlling for age, aCL status, and old stroke by MRI. Conclusions-Platelet

The structural simplicity and ability to capture serial correlations make Markov models a popular modeling choice in several genomic analyses, such as identification of motifs, genes and regulatory elements. A critical, yet relatively unexplored, issue is the determination of the order of the Markov model. Most biological applications use a predetermined order for all data sets indiscriminately. Here, we show the vast variation in the performance of such applications with the order. To identify the ‘optimal’ order, we investigated two model selection criteria: Akaike information criterion and Bayesian information criterion (BIC). The BIC optimal order delivers the best performance for mammalian phylogeny reconstruction and motif discovery. Importantly, this order is different from orders typically used by many tools, suggesting that a simple additional step determining this order can significantly improve results. Further, we describe a novel classification approach based on BIC optimal Markov models to predict functionality of tissue-specific promoters. Our classifier discriminates between promoters active across 12 different tissues with remarkable accuracy, yielding 3 times the precision expected by chance. Application to the metagenomics problem of identifying the taxum from a short DNA fragment yields accuracies at least as high as the more complex mainstream methodologies, while retaining conceptual and computational simplicity.