Background: Vitamin D and insulin play an important role in susceptibility to polycystic ovary syndrome (PCOS), and therefore vitamin D receptor (VDR), parathyroid hormone (PTH), and insulin receptor (INSR) gene variants might be involved in the pathogenesis of PCOS. Objective: The present study was designed to investigate the possible associations between polymorphisms in VDR, PTH, and INSR genes and the risk of PCOS. Materials and Methods: VDR, PTH, and INSR gene variants were genotyped in 35 women with PCOS and 35 controls using Polymerase chain reaction -Restriction fragment length polymorphism method. Furthermore, serum levels of glucose and insulin were measured in all participants. Results: No significant differences were observed for the VDR FokI, VDR Tru9I, VDR TaqI, PTH DraII, INSR NsiI, and INSR PmlI gene polymorphisms between the women with PCOS and controls. However, after adjustment for confounding factors, the VDR BsmI "Bb" genotype and the VDR ApaI "Aa" genotype were significantly under transmitted to the patients (p= 0.016; OR= 0.250; 95% CI= 0.081-0.769, and p= 0.017; OR= 0.260; 95% CI= 0.086-0.788, respectively). Furthermore, in the women with PCOS, insulin levels were lower in the participants with the INSR NsiI "NN" genotype compared with those with the "Nn + nn" genotypes (P= 0.045). Conclusion:The results showed an association between the VDR gene BsmI and ApaI polymorphisms and PCOS risk. These data also indicated that the INSR "NN" genotype was a marker of decreased insulin in women with PCOS. Our findings, however, do not lend support to the hypothesis that PTH gene DraII variant plays a role in susceptibility to PCOS.

Multidrug resistance protein 2 (MRP2), encoded by the ATP-binding cassette C2 (ABCC2) gene, is an efflux pump located on the apical membrane of many polarized cells, which transports conjugate compounds by an ATP-dependent mechanism. The correlation of G1249A ABCC2 polymorphism with the development of colorectal cancer (CRC) and poor prognosis was evaluated in patients who were treated with fluorouracil/-leucovorin (FL) plus oxaliplatin (FOLFOX-4). A total of 50 paraffin‑embedded tissue samples collected from CRC patients were analyzed to identify the polymorphism. Patients were in stage II/III and received postoperative FOLFOX-4 chemotherapy. As a control group, an equal number of unrelated healthy subjects were enrolled in the study. The polymorphism was genotyped by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method, and results were compared with clinicopathological markers, early relapse and survival rates. During the 12 months of follow-up, local and distant recurrences were observed in 15 (30%) patients. No significant difference in the distribution of wild-type and polymorphic genotypes was observed between the patient and control groups and between the patients who experienced recurrence within 1 year and those who did not (all P>0.05). In conclusion, the G1249A polymorphism is not associated with CRC risk and early recurrence. However, significant correlation was observed between G1249A polymorphism and the overall survival and disease-free survival of the patients.

To our knowledge, this is the first study suggesting that the RETN -420 C>G "CC" genotype is a marker of decreased CRC susceptibility. This observation is relevant from a scientific perspective and deserves further investigations.

No abstract

Functional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.