Vitamin K, as a cofactor in the gamma carboxylation of certain glutamic acid (Gla) residues, has been related to glucose metabolism and insulin sensitivity

Background: Sesamin is one of the most abundant lignans in sesame and has multiple functions and high values such as antioxidative effect and promoting-immunity function. The aim of this study was to evaluate the effects of sesamin supplementation on glycemic status, serum levels of inflammatory markers and adiponectinin patients with type 2 diabetes mellitus. Material and Methods: Forty-eight patients with type 2 diabetes participated in this double-blind, randomized, placebo-controlled clinical trial. Subjects were randomly assigned to two groups: intervention group (sesamin capsules; 200 mg/day, n = 24) and control group (placebo capsules; 200 mg/day starch, n = 24). Anthropometric indices and serum levels of fasting blood sugar (FBS), glycated hemoglobin (HbA1c), insulin, tumor necrosis factor-alpha (TNF-alpha), interleukin-6 (IL-6), high-sensitivity C-reactive protein (hs-CRP) and adiponectin were assessed at the baseline and after 8 weeks. Result: Sesamin supplementation caused a significant reduction in FBS, HbA1C and TNF-α levels compared with the placebo (p < 0.05). Waist circumference, hip circumference, body adiposity index (BAI), FBS, HbA1c, TNF-α, and IL-6 levels were decreased, and adiponectin levels were increased significantly in the intervention group at the end of study (p < 0.05). Conclusion: According to the beneficial effects of sesamin supplement on glycemic status and inflammatory factors, it may be considered as a supplementary therapeutic approach for diabetic patients. However, future investigations are needed in this field.

(1) Background: Coronary angiography is considered to be the most reliable method for the diagnosis of cardiovascular disease. However, angiography is an invasive procedure that carries a risk of complications; hence, it would be preferable for an appropriate method to be applied to determine the necessity for angiography. The objective of this study was to compare support vector machine, naïve Bayes and logistic regressions to determine the diagnostic factors that can predict the need for coronary angiography. These models are machine learning algorithms. Machine learning is considered to be a branch of artificial intelligence. Its aims are to design and develop algorithms that allow computers to improve their performance on data analysis and decision making. The process involves the analysis of past experiences to find practical and helpful regularities and patterns, which may also be overlooked by a human. (2) Materials and Methods: This cross-sectional study was performed on 1187 candidates for angiography referred to Ghaem Hospital, Mashhad, Iran from 2011 to 2012. A logistic regression, naive Bayes and support vector machine were applied to determine whether they could predict the results of angiography. Afterwards, the sensitivity, specificity, positive and negative predictive values, AUC (area under the curve) and accuracy of all three models were computed in order to compare them. All analyses were performed using R 3.4.3 software (R Core Team; Auckland, New Zealand) with the help of other software packages including receiver operating characteristic (ROC), caret, e1071 and rminer. (3) Results: The area under the curve for logistic regression, naïve Bayes and support vector machine were similar—0.76, 0.74 and 0.75, respectively. Thus, in terms of the model parsimony and simplicity of application, the naïve Bayes model with three variables had the best performance in comparison with the logistic regression model with seven variables and support vector machine with six variables. (4) Conclusions: Gender, age and fasting blood glucose (FBG) were found to be the most important factors to predict the result of coronary angiography. The naïve Bayes model performed well using these three variables alone, and they are considered important variables for the other two models as well. According to an acceptable prediction of the models, they can be used as pragmatic, cost-effective and valuable methods that support physicians in decision making.

Background: Carbapenems are important drugs used for the treatment of Pseudomonas aeruginosa infections, however metallo-β-lactamases (MBL) are able to efficiently hydrolyze these classes of drugs. Immediate detection of the MBL-producing P. aeruginosa is necessary in order to accurately treat infections caused by this organism. Objectives: To determine the prevalence of MBL producing P. aeruginosa in burn and non-burn patients by two phenotypic tests and polymerase chain reaction (PCR) and to compare phenotypic tests with PCR. Materials and Methods: A total of 223 non-duplicate strains of P. aeruginosa were collected from three teaching hospitals of Ahvaz, Iran. Antimicrobial susceptibility and minimum inhibitory concentrations (MICs) of carbapenems (imipenem, meropenem, doripenem and ertapenem) were determined by the Kirby-Bauer and E-test methods. Combined disk (CD) test, MBL E-test and PCR were performed for carbapenem-resistant P. aeruginosa isolates. Results: Amongst all the P. aeruginosa isolates, 58.7% were resistant to imipenem while 31.8%, 13.5% and 74.4% were resistant to meropenem, doripenem and ertapenem, respectively. Amongst all the P. aeruginosa isolates, 44.4% were multidrug resistant and 13.45% were resistant to all of the carbapenems. The CD test with doripenem disk / 750 μg ethylene diamine tetra acetic acid (EDTA) had the highest efficiency compared to the other phenotypic tests. bla_IMP and bla_VIM genes were detected in 11.7% and 0.4% of isolates, respectively. bla_SPM and bla_NDM genes were not observed. Conclusions: Epidemiological and regional evaluation of MBL-producing P. aeruginosa through simple and inexpensive methods should be considered for effective treatment of carbapenem-resistant P.aeruginosa infections. Keywords: Metallo-beta- lactamase; Carbapenemase; Pseudomonas aeruginosa

Background: In this exploratory study, the contribution of delivery type to the weight gain pattern for full-term infants with exclusive breastfeeding in the first month of infancy was determined. In addition, breastfeeding success among cesarean section (C-section) delivery mothers based on their neonate's weight gain at the end of the first month of infancy was evaluated.Methods: A cohort of 92 neonates born in Shiraz, from July 10 to August 10, 2007 was followed longitudinally. The data were collected during the first month postpartum at three occasions: 3 to 7 days postpartum, 10-21 days postpartum and 24-31 days postpartum.Results: Among 92 mothers in this study, 35 (38%) were delivered by C-section. Generalized estimating equation (GEE) showed that delivery type (p < 0.01), receipt of advice about breastfeeding (p = 0.03) and neonate's age (p < 0.01) significantly affected weight gain. GEE estimated the values of the parameters under study and the testing contribution of each factor to weight gain, leading to the conclusion that gender, parities and maternal education did not contribute to weight gain. The neonate's weight gain pattern for C-section deliveries lies below that of normal vaginal deliveries until 25 days postpartum, when weight gain for C-section deliveries became higher than that for normal vaginal deliveries.Conclusions: Type of delivery contributes strongly to the weight gain pattern in the first month of infancy. In spite of greater weight loss among C-section birth neonates in the first days of life, at the end of the first month neonates showed a similar weight gain. Consequently, mothers with C-section delivery can successfully exclusively breastfeed.