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Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by deficiencies in social interactions and communication, combined with restricted and repetitive behavioral issues.As little is known about the etiopathophysiology of ASD and early diagnosis is relatively subjective, we aim to employ a targeted, fully quantitative metabolomics approach to biochemically profile post-mortem human brain with the overall goal of identifying metabolic pathways that may have been perturbed as a result of the disease while uncovering potential central diagnostic biomarkers.Using a combination ofWe accurately identified and quantified 203 metabolites in post-mortem brain extracts and performed a metabolite set enrichment analyses identifying 3 metabolic pathways as significantly perturbed (p 0.05). These include Pyrimidine, Ubiquinone and Vitamin K metabolism. Further, using a variety of machine-based learning algorithms, we identified a panel of central biomarkers (9-hexadecenoylcarnitine (C16:1) and the phosphatidylcholine PC ae C36:1) capable of discriminating between ASD and controls with an AUC = 0.855 with a sensitivity and specificity equal to 0.80 and 0.818, respectively.For the first time, we report the use of a multi-platform metabolomics approach to biochemically profile brain from people with ASD and report several metabolic pathways which are perturbed in the diseased brain of ASD sufferers. Further, we identified a panel of biomarkers capable of distinguishing ASD from control brains. We believe that these central biomarkers may be useful for diagnosing ASD in more accessible biomatrices.

In India, groundwater is very crucial natural resources that are extensively used in both urban and rural regions for irrigation and drinking purpose. In the present research work, the potential manganese contamination zones (PMCZ) within Burdwan district was investigated using GIS approach by considering various controlling factors, i.e., geology, soil, rainfall and land use land cover. Frequency ratio modeling was implemented to assign the scores to various input factors and their sub-classes. Model output based on PMCZ is classified into two broad classes, i.e., ‘suitable’ and ‘unsuitable’ where, 63% (4432 km2) and 37% (2607 km2) of the study area account for suitable and unsuitable category, respectively. The PMCZ model output was further validated with 654 reported manganese (Mn) occurrence in groundwater from different location in Burdwan district and it is observed that the model achieved an accuracy of about 75%. Success and prediction rate curve also show an accuracy of 83 and 77%, respectively which indicates that the prediction rate and accuracy rate of model in the prediction of PMCZ is quite high. The ground-truth verification of predicted zones shows an accuracy of 80% in prediction which was carried out by means of groundwater sampling in the study area followed by the Mn estimation in groundwater samples. Majority of high Mn contaminated area fall along the flood plain (Neogene–Pleistocene sediment) of Burdwan district. The outcome of the research work can be helpful in better planning and management of groundwater resources in future.

The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB’s chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC–MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.