Browsing by Author "Sachit Ganapathy"

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Classification Accuracy of Linear Discriminant Function using Principal Components with Multiple Correlated Variables: A Simulation based Exploration
(Wolters Kluwer Medknow Publications, 2025) Akash Mishra; Sachit Ganapathy; Narayanapillai Sreekumaran Nair; Durgesh Shukla; Ashish Kumar Yadav; Rajaat Vohra; Kuldeep Soni
Background: Linear Discriminant Analysis (LDA) is a powerful and widely used technique for classification with correlated variables. Principal Components (PCs) group these variables into linear combinations and produce independent variables. The LDA on these PC’s may provide better classification accuracy in clinical diagnostics than on usual measurements. Methodology: Two datasets were utilized for demonstration: one from a Sudden Sensorineural Hearing Loss (SSNHL) case-control study and the other from a Gall Bladder (GB) case-control study. Linear Discriminant Analysis (LDA) was conducted on the actual correlated measured variables for group classification, as well as on the derived principal component variables, to compare their classification accuracies. Performance metrics including Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Classification Accuracy, and F1 Score were assessed. For validation, a third simulated dataset was employed. Additionally, LDA was performed on each dataset using eigenvectors of the control group applied to the cases and vice versa, revealing a strong agreement in classification as measured by the kappa statistic. Results: When LDA was applied to the actual lipid measurements in the SSNHL dataset, the classification accuracy was 57.2%, and the F1 score was 39.7%. However, when LDA was performed using principal components (PCs), the classification accuracy markedly improved to 99.2%, with an F1 score of 98.5%. Similarly, for the GB cancer dataset, the classification accuracy and F1 score were initially 77.2% and 77.3%, respectively. Upon applying LDA with the PCs, these metrics were significantly enhanced to 98.4% and 98.3%, respectively. For the simulated dataset, both the classification accuracy and F1 score were 99.1%. The study also demonstrated that the classification accuracy and F1 score remained consistent regardless of whether the eigenvectors from the cases or controls were used to classify new subjects (Kappa Statistic = 0.962, P < 0.001). Conclusion: In group separation, utilizing principal components significantly improves classification accuracy and overall performance metrics, outperforming the use of the original correlated predictors. © 2025 Medical Journal of Dr. D.Y. Patil Vidyapeeth.
Molecular detection of Listeria monocytogenes from different dairy and street food sources in North Karnataka, India
(Elsevier Ltd, 2024) Roshan Kumar Sharma; Sunil S. Jalalpure; Swati Pathak; Sachit Ganapathy; Mickaël Desvaux; Subarna Roy; Satisha Hegde
Background: Food-borne pathogen Listeria monocytogenes is abundantly present in nature and accountable for sporadic and epidemic cases of listeriosis in humans. The objective of this study was to screen common food sources for L. monocytogenes using biochemical and molecular methods to detect and characterise its toxin genes as well as for biofilm formation. Methods: A total of 92 samples, comprising dairy and street food products, were randomly collected from various sources for this investigation. The collected samples were processed for biochemical and molecular methods to detect L. monocytogenes. Additionally, virulence factors associated genes, antibiogram profiles and biofilm formation related assays were determined. Results: L. monocytogenes presence was confirmed using molecular detection methods targeting prs and lmo1030 genes, along with MALDI-TOF MS. Following 16 S rRNA sequencing, the identified Listeria species were further categorised into two groups. L. monocytogenes was detected in two (2.17%) food samples tested (L-23 and L-74). Multiplex PCR indicated the presence of seven virulence-related genes in L. monocytogenes isolates, i.e., inlA, inlB, prfA, iap, actA, plcB, and hlyA. In addition, 17 antibiotics were tested, whereby two isolates showed resistance to clindamycin and azithromycin, while one isolate (L-74) was also resistant to nalidixic acid, co-trimoxazole, ampicillin, norfloxacin, and cefotaxime. L-23 and L-74 isolates showed biofilm formation, especially at pH 8.6 and 37°C. Conclusions: Besides the demonstration of the presence of L. monocytogenes in some dairy and street food products, this study underscores the need to increase the standards of hygiene on the one hand and the importance of the surveillance of food-borne pathogens on the other. © 2024 The Authors