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Mary Angelin,1 Padmavathi Gopinath,1 Vijaya Raghavan,1,2 Rangaswamy Thara,2 Faraz Ahmad,3 Arasamabattu Kannan Munirajan,1 Ravi Sudesh4 1Department of Genetics, University of Madras, Dr ALM PG Institute of Basic Medical Sciences, Taramani Campus, Chennai, Tamil Nadu, 600 113, India; 2Schizophrenia Research Foundation, Chennai, Tamil Nadu, 600 101, India; 3Department of Biotechnology, School of Bioscience and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India; 4Department of Biomedical Sciences, School of Bioscience and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, IndiaCorrespondence: Ravi Sudesh, Department of Biomedical Sciences, School of Bioscience and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India, Tel +91-9176028134, Email sudesh.ravi@vit.ac.in; sudesh.genetics@gmail.com Arasamabattu Kannan Munirajan, Department of Genetics, Dr. ALM PG Institute Basic Medical Sciences, University of Madras, Taramani, Chennai, Tamil Nadu, 600 113, India, Tel +91-44-24547064 ; +91-9444460136, Email akmunirajan@gmail.com; akmunirajan@unom.ac.inBackground: Schizophrenia is a heterogeneous chronic psychiatric disorder influenced by genetic and environmental factors. Environmental factors can alter epigenetic marks, which regulate gene expression and cause an array of systemic changes. Several studies have demonstrated the association of epigenetic modulations in schizophrenia, which can influence clinical course, symptoms, and even treatment. Based on this, we have examined the global DNA methylation patterns, namely the 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC); and the global RNA modification N6-methyladenosine (m6A) RNA methylation status in peripheral blood cells. First-Episode Psychosis (FEP) patients who were diagnosed with Schizophrenia (SCZ) and undergoing treatment were stratified as Treatment-Responsive (TR) and Treatment-Non-Responsive (TNR). Age- and sex-matched healthy subjects served as controls.Results: The methylation pattern of 5mC and 5hmC showed significant increases in patients in comparison to controls. Further, when patients were classified based on their response to treatment, there was a statistically significant increase in methylation patterns in the treatment non-responder group. 5fC and m6A levels did not show any statistical significance across the groups. Further, gender-based stratification did not yield any significant difference for the markers.Conclusion: The study highlights the increased global methylation pattern in SCZ patients and a significant difference between the TR versus TNR groups. Global 5mC and 5hmC epigenetic marks suggest their potential roles in schizophrenia pathology, and also in the treatment response to antipsychotics. Since not many studies were available on the treatment response, further validation and the use of more sensitive techniques to study methylation status could unravel the potential of these epigenetic modifications as biomarkers for SCZ as well as distinguishing the antipsychotic treatment response in patients.Keywords: schizophrenia, global methylation, treatment response, 5mC, 5hmC, m6A

Background: Type 1 diabetes mellitus is known to be associated with autoimmunity and hence autoantibodies. The primary objective of this study was to determine the prevalence of glutamic acid decarboxylase 65 (GAD) antibody in patients with newly diagnosed type 1 diabetes mellitus. Materials and Methods: This cross-sectional study was done in a government-run pediatric tertiary care center in South India from 2015 to 2017 and included 125 patients with newly diagnosed type 1 diabetes mellitus. Demographic and clinical data were captured and GAD antibody was detected by the ELISA technique and a titer of above 1.0 IU/mL was considered positive. Results: The prevalence (95% confidence interval) of GAD 65 antibody was found to be 79.2% (71%–86%). Glycated hemoglobin level was significantly higher in GAD negative when compared to GAD positive patients (P < 0.05). Conclusion: Type 1 diabetes mellitus is associated with high prevalence of autoantibodies in the current era.

Background: The first national-level multicentric clinic-based registry of youth-onset diabetes from India was started in the year 2006 by the Indian Council of Medical Research (ICMR). Objective: In this study, we present the data collected from one of the Regional Collaborating Centre, Chennai (RCC03) of the ICMR Young Diabetes Registry (YDR). Materials and Methods: YDR recruited young diabetes participants reporting on/after January 1, 2000, with age onset ≤25 years at the time of diagnosis of diabetes, and residing within the Chennai Metropolitan Area. The reporting centers (RCs) that were willing to participate in the registry were included, and their staff was trained to fill-in the baseline and follow-up proforma. Results: Overall, 29 RCs participated, which includes six government hospitals, and remaining are private speciality hospitals or single-physician clinics. So far, RCC03 had contributed 4194 young diabetes participants to ICMR-YDR from the Chennai region. Among the registered 48.1% (n = 2020) were type 1 diabetes mellitus (T1DM), 43.4% (n = 1821) were type 2 diabetes mellitus (T2DM), 6.4% (n = 269) were gestational diabetes mellitus, and remaining 2.0% (n = 84) had secondary diabetes. Among T1DM, 58% of them had age onset of

The texture is identifiable in optical and easy ways. Texture classification is an imperative region in texture analysis, where it gives descriptors for classifying the images. The categorization of normal and abnormal matter by magnetic resonance (MR), computed tomography (CT), and texture images has made noteworthy evolution in modern years. Recently, different novel robust classification techniques have been introduced to classify the different kinds of images for prediction. However, the accuracy of classification was not improved with lesser time. To address these issues, the edge‐preserved Tversky indexive Hellinger and deep perceptive Czekanowski classifier (ETIH‐DPCC) technique is introduced to segment and classify the images with more accuracy. The ETIH‐DPCC technique includes diverse processes namely preprocessing, segmentation, feature extraction, as well as classification. At first, different types of images, such as magnetic resonance imaging, CT, and texture, are used as input. With the acquired input, edge‐preserving normalized adaptive bilateral filtering is employed to carry the image preprocessing. In this stage, the noisy pixels are removed and edges are preserved. Then, the Tversky‐indexed quantile regression is applied to segment the images into diverse texture regions. After that, the feature extraction is performed on the segmented region using Hellinger kernel feature extraction, where a more informative feature for image prediction is extracted. During this process, the irrelevant features are avoided to decrease the dimensionality and feature extraction time. These extracted features are finally classified into positive and negative classes for disease prediction using DPCC. DPCC comprises multiple layers to deeply analyze the association between training and testing features. With this, the prediction accuracy is improved. Experimental outcomes show that the ETIH‐DPCC technique efficiently enhances prediction accuracy and less time compared to conventional methods. [ABSTRACT FROM AUTHOR]

The compressibility features (bulk density, tapped bulk density, porosity, coefficient of compressibility and Hauser ratio) of Canarium schweinfurthii engl. nutshell powder as it affects flowability during densification process were investigated. Three different moisture contents (10.13, 15.07 and 20.11% wet basis; w.b.) and particle sizes of 0.659 7, 1.26 and 2.05 mm were considered at pressure range of 2 to 10 MPa. The compressibility relationship with the factors were modelled and the optimum flow conditions were also determined. The obtained results showed that particle size and moisture content had incremental influence on the compression features studied except moisture content on bulk density. The compressibility of the nut shell powder increased from 17.44 to 28.18% and decreased from 29.41%to 18.79% as moisture content and particle size increased respectively. Medium particle size had the least Hausner ratio (1.16) and the best flow behaviour than other sizes for all the studied moisture contents. The linear model developed and its features had significant relationship with compressibility. The optimum values of pressure, moisture content and particle size required to achieve 17.45% compressibility for good flow are 4.88 MPa, 10.91% and 0.863 8 mm respectively.