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Devi, Kangjam Rekha; Pradhan, Jagat; Bhutia, Rinchenla; Dadul, Peggy; Sarkar, Atanu; Gohain, Nitumoni; Narain, Kanwar
Scientific reports, 04/2021, Letnik: 11, Številka: 1Journal Article
In India, tuberculosis is an enormous public health problem. This study provides the first description of molecular diversity of the Mycobacterium tuberculosis complex (MTBC) from Sikkim, India. A total of 399 Acid Fast Bacilli sputum positive samples were cultured on Lőwenstein-Jensen media and genetic characterisation was done by spoligotyping and 24-loci MIRU-VNTR typing. Spoligotyping revealed the occurrence of 58 different spoligotypes. Beijing spoligotype was the most dominant type constituting 62.41% of the total isolates and was associated with Multiple Drug Resistance. Minimum Spanning tree analysis of 249 Beijing strains based on 24-loci MIRU-VNTR analysis identified 12 clonal complexes (Single Locus Variants). The principal component analysis was used to visualise possible grouping of MTBC isolates from Sikkim belonging to major spoligotypes using 24-MIRU VNTR profiles. Artificial intelligence-based machine learning (ML) methods such as Random Forests (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used to predict dominant spoligotypes of MTBC using MIRU-VNTR data. K-fold cross-validation and validation using unseen testing data set revealed high accuracy of ANN, RF, and SVM for predicting Beijing, CAS1_Delhi, and T1 Spoligotypes (93-99%). However, prediction using the external new validation data set revealed that the RF model was more accurate than SVM and ANN.
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Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
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Vir: Osebne bibliografije
in: SICRIS
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