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In this study, we present a novel chitosan-intercalated montmorillonite/poly(vinyl alcohol) (OMMT/PVA) nanofibrous mesh as a microenvironment for guiding differentiation of human dental pulp stem cells (hDPSCs) toward neuronlike cells. The OMMT was prepared through ion exchange reaction between the montmorillonite (MMT) and chitosan. The PVA solutions containing various concentrations of OMMT were electrospun to form 3D OMMT-PVA nanofibrous meshes. The biomechanical and biological characteristics of the nanofibrous meshes were evaluated by ATR-FTIR, XRD, SEM, MTT, and LDH specific activity, contact angle, and DAPI staining. They were carried out for mechanical properties, overall viability, and toxicity of the cells. The hDPSCs were seeded on the prepared scaffolds and induced with neuronal specific differentiation media at two differentiation stages (2 days at preinduction stage and 6 days at induction stage). The neural differentiation of the cells cultured on the meshes was evaluated by determining the expression of Oct-4, Nestin, NF-M, NF-H, MAP2, and βIII-tubulin in the cells after preinduction, at induction stages by real-time PCR (RT-PCR) and immunostaining. All the synthesized nanofibers exhibited a homogeneous morphology with a favorable mechanical behavior. The population of the cells differentiated into neuronlike cells in all the experimental groups was significantly higher than that in control group. The expression level of the neuronal specific markers in the cells cultured on 5% OMMT/PVA meshes was significantly higher than the other groups. This study demonstrates the feasibility of the OMMT/PVA artificial nerve graft cultured with hDPSCs for regeneration of damaged neural tissues. These fabricated matrices may have a potential in neural tissue engineering applications.

Chronic granulomatous disease (CGD) is an inherited disorder of pathogen killing by phagocytic leukocytes caused by mutations in NADPH oxidase subunits. Patients with CGD have life-threatening bacterial and fungal infections. Children's Medical Center at Tehran University is the referral center for immunodeficiency in Iran. During 2 years of study, 11 non-consanguineous families with clinically diagnosed CGD were referred to this center. In functional assays performed on neutrophils from affected children and their mothers; no activity or strongly decreased oxidase activity was detected in the patients' cells. In oxidase tests that scored this activity on a per-cell basis, a mosaic pattern was detected in the neutrophils from all 11 mothers. Western blot analysis revealed an X91 degrees phenotype in all patients. Mutation screening in the CYBB gene encoding gp91(phox) by SSCP analysis followed by sequencing showed nine different mutations, including two novel mutations. The present survey is the first study aimed to analyze the clinical features and the molecular diagnosis of X-CGD in Iranian patients.

Long non‐coding RNAs (lncRNAs) are a subclass of non‐protein coding transcripts that are involved in several regulatory processes and are considered as potential biomarkers for almost all cancer types. This study aims to investigate the prognostic value of lncRNAs for lung adenocarcinoma (LUAD), the most prevalent subtype of lung cancer. To this end, the processed data of The Cancer Genome Atlas LUAD were retrieved from GEPIA and circlncRNAnet databases, matched with each other and integrated with the analysis results of a non‐small cell lung cancer plasma RNA‐Seq study. Then, the data were filtered in order to separate the differentially expressed lncRNAs that have a prognostic value for LUAD. Finally, the selected lncRNAs were functionally annotated using a bioinformatic and systems biology approach. Accordingly, we identified 19 lncRNAs as the novel LUAD prognostic lncRNAs. Also, based on our results, all 19 lncRNAs might be involved in lung cancer‐related biological processes. Overall, we suggested several novel biomarkers and drug targets which could help early diagnosis, prognosis and treatment of LUAD patients.

More than 60% of patients screening positive for depression on self-report were not recognized by neurologists on the UPDRS. A patient-reported screening tool for depression may improve recognition and management of dPD.

16Machine learning can be used to find meaningful patterns characterizing individual 17 differences. Deploying a machine learning classifier fed by local features derived from graph 18 analysis of electroencephalographic (EEG) data, we aimed at designing a neurobiologically-19 based classifier to differentiate two groups of children, one group with and the other group 20 without dyslexia, in a robust way. We used EEG resting-state data of 29 dyslexics and 15 typical 21 readers in grade 3, and calculated weighted connectivity matrices for multiple frequency bands 22 using the phase lag index (PLI). From the connectivity matrices, we derived weighted 23 connectivity graphs. A number of local network measures were computed from those graphs, and 24 37 False Discovery Rate (FDR) corrected features were selected as input to a Support Vector 25 Machine (SVM) and a common K Nearest Neighbors (KNN) classifier. Cross validation was 26 employed to assess the machine-learning performance and random shuffling to assure the 27 performance appropriateness of the classifier and avoid features overfitting. The best 28 performance was for the SVM using a polynomial kernel. Children were classified with 95% 29 accuracy based on local network features from different frequency bands. The automatic 30 classification techniques applied to EEG graph measures showed to be both robust and reliable in 31 distinguishing between typical and dyslexic readers. 32