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Vipul Vilas Wagh,1 Tanvi Kottat,1 Suchita Agrawal,2 Shruti Purohit,2 Tejaswini Arun Pachpor,3,4 Leelavati Narlikar,5 Vasudeo Paralikar,2 Satyajeet Pramod Khare1 1Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, MH, India; 2Psychiatry Unit, KEM Hospital Research Centre, Pune, MH, India; 3Department of Biosciences and Technology, School of Science and Environment Studies, Dr. Vishwanath Karad MIT World Peace University, Pune, MH, India; 4Department of Biotechnology, MES Abasaheb Garware College, Pune, MH, India; 5Department of Data Science, Indian Institute of Science Education and Research, Pune, MH, IndiaCorrespondence: Vasudeo Paralikar; Satyajeet Pramod Khare, Email paralikarv2010@gmail.com; satyajeetkhare@gmail.comIntroduction: Stigma contributes to a significant part of the burden of schizophrenia (SCZ), therefore reducing false positives from the diagnosis would be liberating for the individuals with SCZ and desirable for the clinicians. The stigmatization associated with schizophrenia advocates the need for high-precision diagnosis. In this study, we present an ensemble learning-based approach for high-precision diagnosis of SCZ using peripheral blood gene expression profiles.Methodology: The machine learning (ML) models, support vector machines (SVM), and prediction analysis for microarrays (PAM) were developed using differentially expressed genes (DEGs) as features. The SCZ samples were classified based on a voting ensemble classifier of SVM and PAM. Further, microarray-based learning was used to classify RNA sequencing (RNA-Seq) samples from our case-control study (Pune-SCZ) to assess cross-platform compatibility.Results: Ensemble learning using ML models resulted in a significantly higher precision of 80.41% (SD: 0.04) when compared to the individual models (SVM-radial: 71.69%, SD: 0.04 and PAM 77.20%, SD: 0.02). The RNA sequencing samples from our case-control study (Pune-SCZ) resulted in a moderate precision (59.92%, SD: 0.05). The feature genes used for model building were enriched for biological processes such as response to stress, regulation of the immune system, and metabolism of organic nitrogen compounds. The network analysis identified RBX1, CUL4B, DDB1, PRPF19, and COPS4 as hub genes.Conclusion: In summary, this study developed robust models for higher diagnostic precision in psychiatric disorders. Future efforts will be directed towards multi-omic integration and developing “explainable” diagnostic models. Keywords: Schizophrenia, peripheral blood, gene expression, machine learning, ensemble learning

In The Pune Maternal Nutrition Study, vitamin B12 deficiency was seen in 65% of pregnant women, folate deficiency was rare. Maternal total homocysteine concentrations were inversely associated with offspring birthweight, and low vitamin B12 and high folate concentrations predicted higher offspring adiposity and insulin resistance. These findings guided a nested pre-conceptional randomised controlled trial 'Pune Rural Intervention in Young Adolescents'. The interventions included: (1) vitamin B12+multi-micronutrients as per the United Nations International Multiple Micronutrient Antenatal Preparation, and proteins (B12+MMN), (2) vitamin B12 (B12 alone), and (3) placebo. Intervention improved maternal pre-conceptional and in-pregnancy micronutrient nutrition. Gene expression analysis in cord blood mononuclear cells in 88 pregnancies revealed 75 differentially expressed genes between the B12+MMN and placebo groups. The enriched biological processes included G2/M phase transition, chromosome segregation, and nuclear division. Enriched pathways included, mitotic spindle checkpoint and DNA damage response while enriched human phenotypes were sloping forehead and decreased head circumference. Fructose-bisphosphatase 2 ( FBP2) and Cell Division Cycle Associated 2 ( CDCA2) genes were under-expressed in the B12 alone group. The latter, involved in chromosome segregation was under-expressed in both intervention groups. Based on the role of B-complex vitamins in the synthesis of nucleotides and S-adenosyl methionine, and the roles of vitamins A and D on gene expression, we propose that the multi-micronutrient intervention epigenetically affected cell cycle dynamics. Neonates in the B12+MMN group had the highest ponderal index. Follow-up studies will reveal if the intervention and the altered biological processes influence offspring diabesity.

Out of 318 rural females studied, 17.3% were found to be obese. Most of them are aware of their obese status except for 10.9% who were unaware. Majority of the females considered eating more, childbirth and reduced activity as the possible cause of their obesity and also obesity in other persons. Though 48.1% and 33.6% of the females mentioned dieting and exercise/walking respectively as the method of prevention still 52.5% mentioned that medicine is the only method for prevention of obesity. Regarding remedy of obesity again 48.4% females mentioned medicine as the method.