Browsing by Author "Jac Fredo Agastinose Ronickom"

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A Framework to Diagnose Autism Spectrum Disorder Using Morphological Connectivity of sMRI and XGBoost
(2023) Vaibhavi Gupta; Gokul Manoj; Aditi Bhattacharya; Sandeep Singh Sengar; Rakesh Mishra; Bhoomika R. Kar; Chhitij Srivastava; Jac Fredo Agastinose Ronickom
In this study, we automated the diagnostic procedure of autism spectrum disorder (ASD) with the help of anatomical alterations found in structural magnetic resonance imaging (sMRI) data of the ASD brain and machine learning tools. Initially, the sMRI data was preprocessed using the FreeSurfer toolbox. Further, the brain regions were segmented into 148 regions of interest using the Destrieux atlas. Features such as volume, thickness, surface area, and mean curvature were extracted for each brain region, and the morphological connectivity was computed using Pearson correlation. These morphological connections were fed to XGBoost for feature reduction and to build the diagnostic model. The results showed an average accuracy of 94.16% for the top 18 features. The frontal and limbic regions contributed more features to the classification model. Our proposed method is thus effective for the classification of ASD and can also be useful for the screening of other similar neurological disorders.
Comparative evaluation of geometrical, Zernike moments, and volumetric features of the corpus callosum for discrimination of ASD using machine learning algorithms
(Inderscience Publishers, 2023) Aditi Bhattacharya; Gokul Manoj; Vaibhavi Gupta; Abdul Aleem Shaik Gadda; Dhanvi Vedantham; A. Amalin Prince; Priya Rani; Anandh Kilpattu Ramaniharan; Jac Fredo Agastinose Ronickom
In this study, we compared the performance of geometrical, Zernike moments, and volumetric features of the corpus callosum (CC) to diagnose autism spectrum disorder (ASD). Initially, the CC was segmented from the midsagittal view of 2D structural magnetic resonance images using the distance regularised level set evolution (DRLSE). The segmented images were validated with the ground truth using similarity measures. The geometrical and Zernike moments were extracted from the 2D segmented region, and the volumetric features were extracted from 3D images of CC. The features extracted were then used to train classifiers. The segmented images were highly matched with the ground truth with mean similarity measure values of Sokal and Sneath-II = 0.9928 and Pearson and Heron-II = 0.9924. We achieved the highest site-specific classification accuracy of 72.69% using the random forest (RF) classifier. The pipeline followed in this study can be used for mass screening of ASD-like neurodevelopmental disorders. Copyright © 2023 Inderscience Enterprises Ltd.
Prioritization before dereplication, an effective strategy to target new metabolites in whole extracts: ghosalin from Murraya paniculata root
(Royal Society of Chemistry, 2024) Sanju Kumari; Sanheeta Chakrabarty; Sanjay Kumar; Sanjeev Kumar; Jac Fredo Agastinose Ronickom; Shreyans K. Jain
Re-discovery of known metabolites is a common challenge in natural product-based drug discovery, and to avoid re-discovery, dereplication has been proposed for identifying known metabolites at the early stage of isolation. A majority of methods use LCMS to profile the extract and ignore the known mass. LC-HRMS profiling may generate a long mass list of metabolites. The identification of a new metabolite is difficult within the mass list. To overcome this, it was hypothesized that identifying a ‘new metabolite’ in the whole metabolome is more difficult than identifying it within the class of metabolites. A prioritization strategy was proposed to focus on the elimination of unknown and uncommon metabolites first using the designed bias filters and to prioritize the known secondary metabolites. The study employed Murraya paniculata root for the identification of new metabolites. The LC-HRMS-generated mass list of 509 metabolites was subjected to various filters, which resulted in 93 metabolites. Subsequently, it was subjected to regular dereplication, resulting in 10 coumarins, among which 3 were identified as new. Further, chromatographic efforts led to the isolation of a new coumarin, named ghosalin (1). The structure of the new compound was established through 2D NMR and X-ray crystallography. Cytotoxicity studies revealed that ghosalin has significant cytotoxicity against cancer cell lines. The proposed prioritization strategy demonstrates an alternative way for the rapid annotation of a particular set of metabolites to isolate a new metabolite from the whole metabolome of a plant extract. © 2024 The Royal Society of Chemistry.