Browsing by Subject "Case-control studies"

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Open Access
BRAPH: A graph theory software for the analysis of brain connectivity
(Public Library of Science, 2017) Mijalkov, M.; Kakaei, E.; Pereira, J. B.; Westman, E.; Volpe, G.
The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment. © 2017 Mijalkov 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.
Open Access
Intestinal microbiota in patients with spinal cord injury
(Public Library of Science, 2016) Gungor, B.; Adiguzel, E.; Gursel, I.; Yilmaz, B.; Gursel, M.
Human intestinal flora comprises thousands of bacterial species. Growth and composition of intestinal microbiota is dependent on various parameters, including immune mechanisms, dietary factors and intestinal motility. Patients with spinal cord injury (SCI) frequently display neurogenic bowel dysfunction due to the absence of central nervous system control over the gastrointestinal system. Considering the bowel dysfunction and altered colonic transit time in patients with SCI, we hypothesized the presence of a significant change in the composition of their gut microbiome. The objective of this study was to characterize the gut microbiota in adult SCI patients with different types of bowel dysfunction. We tested our hypothesis on 30 SCI patients (15 upper motor neuron [UMN] bowel syndrome, 15 lower motor neuron [LMN] bowel syndrome) and 10 healthy controls using the 16S rRNA sequencing. Gut microbial patterns were sampled from feces. Independent of study groups, gut microbiota of the participants were dominated by Blautia, Bifidobacterium, Faecalibacterium and Ruminococcus. When we compared all study groups, Roseburia, Pseudobutyrivibrio, Dialister, Marvinbryantia and Megamonas appeared as the genera that were statistically different between groups. In comparison to the healthy group, total bacterial counts of Pseudobutyrivibrio, Dialister and Megamonas genera were significantly lower in UMN bowel dysfunction group. The total bacterial count of Marvinbryantia genus was significantly lower in UMN bowel dysfunction group when compared to the LMN group. Total bacterial counts of Roseburia, Pseudobutyrivibrio and Megamonas genera were significantly lower in LMN bowel dysfunction group when compared to healthy groups. Our results demonstrate for the first time that butyrate-producing members are specifically reduced in SCI patients when compared to healthy subjects. The results of this study would be of interest since to our knowledge, microbiome-associated studies targeting SCI patients are non-existent and the results might help explain possible implications of gut microbiome in SCI. Copyright © 2016 Gungor 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.
Open Access
Progesterone change in the late follicular phase affects pregnancy rates both agonist and antagonist protocols in normoresponders: a case-controlled study in ICSI cycles
(Taylor & Francis, 2016) Demir, B.; Kahyaoglu, I.; Guvenir, A.; Yerebasmaz, N.; Altinbas, S.; Dilbaz, B.; Dilbaz, S.; Mollamahmutoglu, L.
Objective: The aim of the presented study is to investigate the impact of progesterone change in the late follicular phase on the pregnancy rates of both agonist and antagonist protocols in normoresponders.Study design: A total of 201 normoresponder patients, who underwent embryo transfer were consecutively selected. 118 patients were stimulated using a long luteal GnRH agonist protocol and 83 using a flexible antagonist protocol. The level of change in late follicular phase progesterone was calculated according to the progesterone levels on the hCG day and pre-hCG day (1 or 2 days prior to hCG day) measurement.Results: Clinical pregnancy rates were comparable between long luteal and antagonist group (35.6 and 41%, respectively). The incidence of progesterone elevation on the hCG day was 11% in long luteal and 18% in antagonist group (p = 0.16). In pregnant cycles, p levels both on the hCG day and pre-hCG day measurement were significantly higher in antagonist than agonist cycles (p = 0.029, p = 0.038, respectively). The change of p level was statistically significant in non-pregnant cycles both for the agonist (-0.17 ± 0.07; 95% CI: -0.29 to -0.37) and antagonist groups (-0.18 ± 0.07; 95%CI: -0.31 to -0.04).Conclusions: Late follicular phase progesterone levels were stable during the cycles of pregnant patients irrespective of the protocols and were shown to be higher in pregnant patients in antagonist cycles when compared to agonist cycles.