Comparison of serum adiponectin and osteopontin levels along with metabolic risk factors between obese and lean women with and without PCOS

Introduction: The objective of this study was to investigate the possible relation between serum adiponectin and osteopontin levels as metabolic risk markers among women with different polycystic ovary syndrome (PCOS) phenotypes. Material and methods: In a University Hospital setting PCOS patients diagnosed according to Rotterdam Consensus Conference criteria with body mass index (BMI) between 18 and 35 were recruited. Results: Overall, 57 PCOS patients and 57 age- and BMI-matched healthy controls were included in the study. Luteinising hormone (LH) to follicle-stimulating hormone FSH ratio (LH/FSH), free androgen index (FAI), and dehydroepiandrosterone sulphate (DHEAS-S) was found to be significantly higher in women with PCOS. There was significant interaction between PCOS status and obesity for serum adiponectin levels. Although mean adiponectin and osteopontin levels were similar among cases and controls, a further two-way ANOVA comparison within lean and obese subgroups revealed adiponectin to be significantly lower in lean PCOS women than in lean controls. LH/FSH ratio and adiponectin levels were all found to differ between lean counterparts; however, they did not show any correlation with metabolic markers [cholesterol, homeostatic model assessment (HOMA) or C-reactive protein (CRP) levels] in overall lean women or in the lean PCOS subgroup. Conclusion: Serum adiponectin levels in lean PCOS women were significantly lower than those in lean controls. On the other hand, mean adiponectin and osteopontin levels were similar in PCOS cases and controls overall. © 2020 Via Medica. All rights reserved

In vivo evaluation of teicoplanin- and calcium sulfate-loaded PMMA bone cement in preventing implant-related osteomyelitis in rats

alper, murat/0000-0001-7069-0623WOS: 000243185500008PubMed: 17267341The objective of this study was to evaluate the efficacy of teicoplanin- and calcium sulphate-loaded polymethylmethacrylate (PMMA) bone cements in preventing experimental implant-related osteomyelitis in rats. Four groups of antibiotic-loaded rods were prepared and were implanted into the lateral condylus of the rat femur after inoculation of Staphylococcus aureus. The effectiveness of these were assessed microbiologically, radiographically, and histopathologically. Radiographic evaluation revealed a significant reduction of periostal reaction and osteolysis in rats that received calcium sulphate- and teicoplanin-loaded rods. Histopathological evaluation confirmed these results. Acute infection and bone necrosis were found to be significantly lower in rats that had received calcium sulphate- and teicoplanin-loaded rods. The addition of calcium sulfate to teicoplanin-loaded PMMA bone cement appeared satisfactory as an antibiotic-carrying system for prophylaxis of experimental implant-related osteomyelitis, but further investigations are needed to reach definitive statements for clinical applications

Challenges In Identifying Asthma Subgroups Using Unsupervised Statistical Learning Techniques

Rationale: Unsupervised statistical learning techniques, such as exploratory factor analysis (EFA) and hierarchical clustering (HC), have been used to identify asthma phenotypes, with partly consistent results. Some of the inconsistency is caused by the variable selection and demographic and clinical differences among study populations. Objectives: To investigate the effects of the choice of statistical method and different preparations of data on the clustering results; and to relate these to disease severity. Methods: Several variants of EFA and HC were applied and compared using various sets of variables and different encodings and transformations within a dataset of 383 children with asthma. Variables included lung function, inflammatory and allergy markers, family history, environmental exposures, and medications. Clusters and original variables were related to asthma severity (logistic regression and Bayesian network analysis). Measurements and Main Results: EFA identified five components (eigenvalues >= 1) explaining 35% of the overall variance. Variations of the HC (as linkage-distance functions) did not affect the cluster inference; however, using different variable encodings and transformations did. The derived clusters predicted asthma severity less than the original variables. Prognostic factors of severity were medication usage, current symptoms, lung function, paternal asthma, body mass index, and age of asthma onset. Bayesian networks indicated conditional dependence among variables. Conclusions: The use of different unsupervised statistical learning methods and different variable sets and encodings can lead to multiple and inconsistent subgroupings of asthma, not necessarily correlated with severity. The search for asthma phenotypes needs more careful selection of markers, consistent across different study populations, and more cautious interpretation of results from unsupervised learning.Wo