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Introduction: Puerperal infection remains a significant cause of maternal morbidity and mortality. Those infections occur more likely after cesarean delivery (CD). Prophylactic antibiotics are administered at the time of CD to prevent complications. In addition to intraoperative prophylaxis; prescription of antibiotics during hospital discharge to prevent surgical site infections (SSI) is quite common. Purpose of this study is to determine the utility of prophylactic oral antibiotic prescription in a cohort of low-risk women undergoing CD.Materials and Methods: A prospective observational study was conducted between 2014 and 2018 at Ufuk University School of Medicine, Department of Obstetrics and Gynaecology. Total of 389 low risk elective cesarean deliveries were selected. All cases received intraoperative prophylaxis. In group I (157 subjects), no further antibiotics were given and in group II (232 cases), oral cephuroxime 500 mg was given during hospital discharge. Primary outcome was SSI. Secondary outcomes were endometritis and other infectious conditions.Results: Overall SSI rate was 2.5%. Only 2 SSIs were noted in group 1 (1.2%) compared to eight in group II (3.4%). There was no statistical difference in SSI rate between two groups. Secondary outcomes were also comparable.Conclusion: In this study, we failed to reveal any beneficial effect of oral antibiotic prescription during hospital discharge in low risk elective CDs. Therefore, use of oral antibiotics in addition to intraoperative prophylaxis should be questioned in terms of increased costs, emergence of bacterial resistance and long term effects on new born as a consequence of changes in gut microbiome. [ABSTRACT FROM AUTHOR]

Background: There is a growing interest in noninvasively defining molecular subsets of hemispheric diffuse gliomas based on the isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutation status, which correspond to distinct tumor entities, and differ in demographics, natural history, treatment response, recurrence, and survival patterns.Purpose: To investigate whether metabolite levels detected with short echo time (TE) proton MR spectroscopy (1 H-MRS) at 3T can be used for noninvasive molecular classification of IDH and TERTp mutation-based subsets of gliomas.Study Type: Retrospective.Subjects: In all, 112 hemispheric diffuse gliomas (70 males/42 females, mean age: 42.1 ± 13.9 years).Field Strength/sequence: Short-TE 1 H-MRS (repetition time (TR) = 2000 msec, TE = 30 msec, number of signal averages = 192) and routine clinical brain tumor MR protocols were acquired at 3T.Assessment: 1 H-MRS data were quantified using LCModel software. TERTp and IDH1 or IDH2 (IDH1/2) mutations in the tissue were determined by either minisequencing or Sanger sequencing.Statistical Tests: Metabolic differences between IDH mutant and IDH wildtype gliomas were assessed by a Mann-Whitney U-test. A Kruskal-Wallis test followed by a Tukey-Kramer test was used to analyze metabolic differences between IDH and TERTp mutational molecular subsets of gliomas. A Spearman rank correlation coefficient was used to assess the correlations of metabolite intensities with the Ki-67 index. Furthermore, machine learning was employed to classify the IDH and TERTp mutational status of gliomas, and the accuracy, sensitivity, and specificity values were estimated.Results: Short-TE 1 H-MRS classified the presence of an IDH mutation with 88.39% accuracy, 76.92% sensitivity, and 94.52% specificity, and a TERTp mutation within primary IDH wildtype gliomas with 92.59% accuracy, 83.33% sensitivity, and 95.24% specificity.Data Conclusion: Short-TE 1 H-MRS could be used to identify molecular subsets of hemispheric diffuse gliomas corresponding to IDH and TERTp mutations.Level Of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1799-1809. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) shares some or most components of metabolic cardiovascular syndrome, manifested by abdominal obesity, insulin resistance, dyslipidaemia and atherosclerosis. It has been previously demonstrated that folate and vitamin B12 treatment improved insulin resistance in patients with metabolic syndrome. This study first investigated whether PCOS patients have lower or higher vitamin B12, folate and homocysteine concentrations when compared with healthy, age and body mass index matched controls, and, then examined associations between vitamin B12, folate, homocysteine and insulin resistance and obesity in PCOS patients. Homocysteine concentrations and homeostasis model assessment index were higher, whereas concentrations of vitamin B12 were lower in PCOS patients with insulin resistance compared with those without insulin resistance. Serum vitamin B12 concentrations were significantly lower in obese PCOS women in comparison with obese control women (P < 0.05). Fasting insulin, insulin resistance and homocysteine are independent determinants of serum vitamin B12 concentrations in PCOS patients. Insulin resistance, obesity, and elevated homocysteine were associated with lower serum vitamin B12 concentrations in PCOS patients. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to classify Parkinson's disease mild cognitive impairment (PD-MCI), cognitively normal Parkinson's disease (PD-CN) and healthy control (HC) groups based on multimodal magnetic resonance imaging (MRI) using machine learning methods. Methods: 33 PD-MCI, 27 PD-CN and 17 HC participated in this study. The participants were diagnosed by neurologists according to the neuropsychological test and physical examination results. MRI data was obtained at a 3T Philips clinical MR system using a 32-channel head coil. Mean cerebral blood flow (CBF), arterial blood volume (aBV) and bolus arrival time (BAT) maps obtained from arterial spin labeling MRI (ASL-MRI), fractional anisotropy (FA) and mean diffusivity (MD) maps obtained from diffusion tensor imaging (DTI), and metabolite peak ratios obtained from proton MR spectroscopic imaging (1H-MRSI) at various brain regions were used as features. Various machine learning methods were employed with appropriate hyperparameters. In addition, feature selection algorithms and dimension reduction techniques such as principal component analysis (PCA) and non-negative matrix factorization (NNMF) were assessed. Features having high correlation with each other were eliminated. Results: Removing highly correlated features increased the model performance. The subset of features selected by the randomized logistic regression and leave one out cross-validation (RLR-LOOCV) method contained 10% of all features from all the MRI modalities. The classification accuracies were 77% for PD-MCI versus HC, 80% for PD-MCI versus PD-CN, and 71% for PD-CN versus HC. Conclusion: Machine learning based on multimodal MRI might be helpful in early diagnosis of PD-MCI. Future studies aim to improve the classification of PD-MCI in a larger patient cohort. This study has been supported by TÜBITAK #115S219 and Ministry of Development #2010K120330. [ABSTRACT FROM AUTHOR]

Objective: In this study, cerebral blood flow (CBF), arterial blood volume (aBV) and bolus arrival time (BAT) maps of Parkinson's disease patients with mild cognitive impairment (PD-MCI) or normal cognition (PD-CN) and healthy controls (HC) were calculated using arterial spin labeling MRI (ASLMRI) technique, and the aim was to define biomarkers, based on perfusion, that might indicate cognitive impairment. Methods: Twenty-nine PD-MCI, twenty-four PD-CN and sixteen HC were scanned on a 3 Tesla clinical MR system (Philips Medical Systems, Best, The Netherlands). ASL-MR images were acquired by using STAR labeling with Look Locker sequence at eight different inversion times (TIs) (TR/TE=250 ms/16ms, number of acquisitions=30). A program was written in MATLAB (The Mathworks Inc., Natick, MA) for calculating the CBF, aBV and BAT maps. Then, mean CBF, aBV and BAT values were estimated in several different brain regions of Montreal Neurological Institute (MNI) structural, and Harvard- Oxford cortical and subcortical structural atlases. A Kruskal- Wallis test followed by Dunn's post-hoc test including multiple comparison correction, was applied for comparing the perfusion values between PD-MCI, PD-CN and HC. Results: We observed lower mean CBF in thalamus (p= 0.005) and insular cortex (p=0.014) in PD-MCI than HC. PD-MCI had lower aBV in cuneal cortex (p=0.016) than HC. There was a trend for decreased perfusion in cingulate gyrus (p=0.025) and left hippocampus (p=0.026) in PD-CN patients in comparison to HC, and in cuneal cortex (p=0.026), occipital pole (p=0.023), and precuneus (p=0.045) in PD-MCI with respect to HC. Also, we observed a trend for lower aBV in lingual gyrus (p=0.026) and left hippocampus (p=0.040) in PD-MCI than HC. Conclusion: ASL-MRI technique might be useful for defining biomarkers for detecting cognitive decline in Parkinson's disease at an early phase and following-up the disease. This study was supported by TÜBITAK project #115S219 and the Ministry of Development project #2010K120330. [ABSTRACT FROM AUTHOR]

Objective: Parkinson's disease-mild cognitive impairment (PD-MCI) has a higher risk of developing PD dementia (PDD) and a reliable biomarker for the diagnosis of PD-MCI has not developed yet. In this study, in order to develop discriminative neurobiological parameters for PD-MCI, functional and structural differences among PD-MCI, cognitively normal PD (CN-PD) and healthy controls (HC) were investigated using resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI) modalities. Methods: 60 PD (27 PH-CN, 33 PD-MCI) patients and 17 HC were included in this study. MR imaging was performed on 3T Phillips MRI scanner. Resting state networks (RSN) were obtained by using independent component analysis (ICA) in Group-ICA fMRI Toolbox. Expression scores specific for each subject were fed into logistic regression to obtain RSNs discriminating three groups from each other. ROI based approach was used for analysis of DTI data and fractional anisotropy (FA) values of each subject were compared with one-way ANOVA. Results: Logistic regression analysis yielded maximum separation of between PD-MCI and PD-CN groups with default mode network (DMN) - posterior cingulate component with an overall accuracy of 63,3% (χ2=6.945, df=1, p=0.008). Sensorymotor and visual network provided maximum separation between PDMCI and HC groups with an overall accuracy of 80% (χ2=9.514, df=2, p=0.009). FA values differed among three groups in the superior longitudinal fasciculus (SLF) (p=0.028) and SLF-temporal part (p=0.026) and FA values in these ROIs significantly decreased in PD-MCI compared to HC. Conclusion: In previous studies, lower connectivity in DMN and decreased FA values in SLF have been demonstrated in patients with PDD. In this regard, our similar results in PDMCI may be an indicator for cognitive decline in PD. Additionally, lower connectivity in visual network could be associated with visuospatial impairment in PD-MCI. Supported by TUBITAK #115S219 and IU-BAP #21336. [ABSTRACT FROM AUTHOR]