The role of metformin response in lipid metabolism in patients with recent-onset type 2 diabetes: HbA1c level as a criterion for designating patients as responders or nonresponders to metformin

Background: In this study, we investigated whether response to metformin, the most frequently drug for diabetes treatment, influences the therapeutic effects of antilipidemic medication in newly diagnosed patients with type 2 diabetes mellitus (T2DM). Methods: A total of 150 patients with T2DM were classified into two groups following 3 months of metformin therapy (1000mg twice daily): responders (patients showing >1% reduction in HbA1c from baseline) and nonresponders (patients showing <1% reduction in HbA1c from baseline). The patients received atorvastatin 20 mg, gemfibrozil 300 mg, or atorvastatin 20 mg and gemfibrozil 300 mg daily. Principal Findings: HbA1c and fasting glucose levels were significantly different between baseline and 3 months among responders receiving atorvastatin; however, these differences were not statistically significant in nonresponders. Atherogenic ratios of low-density lipoprotein cholesterol to high-density lipoprotein cholesterol (LDL-C/HDL-C; p = 0.002), total cholesterol to HDL-C (TC/HDL-C; p<0.001) and AIP (the atherogenic index of plasma; p = 0.004) decreased significantly in responders receiving atorvastatin than in nonresponders. Moreover, responders receiving atorvastatin showed a significant increase in HDL-C levels but nonresponders receiving atorvastatin did not (p = 0.007). The multivariate model identified a significant association between metformin response (as the independent variable) and TG, TC, HDL-C and LDL-C (dependent variables; Wilk's λ = 0.927, p = 0.036). Conclusions: Metformin response affects therapeutic outcomes of atorvastatin on atherogenic lipid markers in patients newly diagnosed with T2DM. Metformin has a greater impact on BMI in responders of metformin compared to nonresponders. Adoption of better therapeutic strategies for reducing atherogenic lipid markers may be necessary for metformin nonresponders. © 2016 Kashi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Strong duality in conic linear programming: facial reduction and extended duals

The facial reduction algorithm of Borwein and Wolkowicz and the extended dual of Ramana provide a strong dual for the conic linear program $$(P) \sup {<c, x> | Ax \leq_K b}$$ in the absence of any constraint qualification. The facial reduction algorithm solves a sequence of auxiliary optimization problems to obtain such a dual. Ramana's dual is applicable when (P) is a semidefinite program (SDP) and is an explicit SDP itself. Ramana, Tuncel, and Wolkowicz showed that these approaches are closely related; in particular, they proved the correctness of Ramana's dual using certificates from a facial reduction algorithm. Here we give a clear and self-contained exposition of facial reduction, of extended duals, and generalize Ramana's dual: -- we state a simple facial reduction algorithm and prove its correctness; and -- building on this algorithm we construct a family of extended duals when $K$ is a {\em nice} cone. This class of cones includes the semidefinite cone and other important cones.Comment: A previous version of this paper appeared as "A simple derivation of a facial reduction algorithm and extended dual systems", technical report, Columbia University, 2000, available from http://www.unc.edu/~pataki/papers/fr.pdf Jonfest, a conference in honor of Jonathan Borwein's 60th birthday, 201

Semi-quantitative analysis of endometrial receptivity marker mRNA expression in the mid-secretory endometrium of patients with uterine fibromas

In fertile women, expression of molecular marker of endometrial receptivity, HOXA11, leukemia inhibitory factor (LIF) and basic transcriptional element binding protein 1 (BTEB1), rises during the luteal phase with the peak occurring during the implantation window. We evaluated the transcript levels of HOXA-11, LIF and BTEB1 in the mid-secretory endometrium of infertile patients with uterine fibroid infertility (n = 8) and from normal fertile women (n = 8). Expression levels of HOXA11, LIF and BTEB1 mRNA were measured in endometrium during the mid-secretory phase using semi-quantitative reverse transcriptase-polymerase chain reaction (RT-PCR). Endometrial HOXA11, LIF and BTEB1 mRNA expression levels (normalized to ß-actin expression) were significantly decreased in endometrium of infertile patients with uterine fibroid as compared with healthy fertile controls at the time of implantation (P&lt;0.05). The results suggest that the alteration in expression pattern of some genes could account for some aspects of infertility in patients with uterine fibroma.Key words: Myoma, fibromas, implantation, HOXA11, leukemia inhibitory factor, basic transcriptional element binding protein 1

Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

Role of circ-FOXO3 and miR-23a in radiosensitivity of breast cancer

Identifying the radiosensitivity of cells before radiotherapy (RT) in breast cancer (BC) patients allows appropriate switching between routinely used treatment regimens and reduces adverse side effects in exposed patients. In this study, blood was collected from 60 women diagnosed with Invasive Ductal Carcinoma (IDC) BC and 20 healthy women. To predict cellular radiosensitivity, a standard G2-chromosomal assay was performed. From these 60 samples, 20 BC patients were found to be radiosensitive based on the G2 assay. Therefore, molecular studies were finally performed on two equal groups (20 samples each) of patients with and without cellular radiosensitivity. QPCR was performed to examine the expression levels of circ-FOXO3 and miR-23a in peripheral blood mononuclear cells (PBMCs) and RNA sensitivity and specificity were determined by plotting Receiver Operating Characteristic (ROC) curves. Binary logistic regression was performed to identify RNA involvement in BC and cellular radiosensitivity (CR) in BC patients. Meanwhile, qPCR was used to compare differential RNA expression in the radiosensitive MCF-7 and radioresistant MDA-MB-231 cell lines. An annexin -V FITC/PI binding assay was used to measure cell apoptosis 24 and 48 h after 2 Gy, 4 Gy, and 8 Gy gamma-irradiation. Results indicated that circ-FOXO3 was downregulated and miR-23a was upregulated in BC patients. RNA expression levels were directly associated with CR. Cell line results showed that circ-FOXO3 overexpression induced apoptosis in the MCF-7 cell line and miR-23a overexpression inhibited apoptosis in the MDA-MB-231 cell line. Evaluation of the ROC curves revealed that both RNAs had acceptable specificity and sensitivity in predicting CR in BC patients. Binary logistic regression showed that both RNAs were also successful in predicting breast cancer. Although only circ-FOXO3 has been shown to predict CR in BC patients, circ-FOXO3 may function as a tumor suppressor and miR-23a may function as oncomiR in BC. Circ-FOXO3 and miR-23a may be promising potential biomarkers for BC prediction. Furthermore, Circ-FOXO3 could be a potential biomarker for predicting CR in BC patients.</p

On energy consumption of switch-centric data center networks

Data center network (DCN) is the core of cloud computing and accounts for 40% energy spend when compared to cooling system, power distribution and conversion of the whole data center (DC) facility. It is essential to reduce the energy consumption of DCN to esnure energy-efficient (green) data center can be achieved. An analysis of DC performance and efficiency emphasizing the effect of bandwidth provisioning and throughput on energy proportionality of two most common switch-centric DCN topologies: three-tier (3T) and fat tree (FT) based on the amount of actual energy that is turned into computing power are presented. Energy consumption of switch-centric DCNs by realistic simulations is analyzed using GreenCloud simulator. Power related metrics were derived and adapted for the information technology equipment (ITE) processes within the DCN. These metrics are acknowledged as subset of the major metrics of power usage effectiveness (PUE) and data center infrastructure efficiency (DCIE), known to DCs. This study suggests that despite in overall FT consumes more energy, it spends less energy for transmission of a single bit of information, outperforming 3T