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Background: Bacterial vaginosis (BV) is a common clinical manifestation of a perturbed vaginal ecology associated with adverse sexual and reproductive health outcomes if left untreated. The existing diagnostic modalities are either cumbersome or require skilled expertise, warranting alternate tests. Application of machine‐learning tools to heterogeneous and high‐dimensional multi‐omics datasets finds promising potential in data integration and may aid biomarker discovery. Objectives: The present study aimed to evaluate the potential of the microbiome and metabolome‐derived biomarkers in BV diagnosis. Interpretable machine‐learning algorithms were used to evaluate the utility of an integrated‐omics‐derived classification model. Methods: Vaginal samples obtained from reproductive‐age group women with (n = 40) and without BV (n = 40) were subjected to 16S rRNA amplicon sequencing and LC–MS‐based metabolomics. The vaginal microbiome and metabolome were characterized, and machine‐learning analysis was performed to build a classification model using biomarkers with the highest diagnostic accuracy. Results: Microbiome‐based diagnostic model exhibited a ROC‐AUC (10‐fold CV) of 0.84 ± 0.21 and accuracy of 0.79 ± 0.18, and important features were Aerococcus spp., Mycoplasma hominis, Sneathia spp., Lactobacillus spp., Prevotella spp., Gardnerella spp. and Fannyhessea vaginae. The metabolome‐derived model displayed superior performance with a ROC‐AUC of 0.97 ± 0.07 and an accuracy of 0.92 ± 0.08. Beta‐leucine, methylimidazole acetaldehyde, dimethylethanolamine, L‐arginine and beta cortol were among key predictive metabolites for BV. A predictive model combining both microbial and metabolite features exhibited a high ROC‐AUC of 0.97 ± 0.07 and accuracy of 0.94 ± 0.08 with diagnostic performance only slightly superior to the metabolite‐based model. Conclusion: Application of machine‐learning tools to multi‐omics datasets aid biomarker discovery with high predictive performance. Metabolome‐derived classification models were observed to have superior diagnostic performance in predicting BV than microbiome‐based biomarkers. [ABSTRACT FROM AUTHOR]

D-Galactosamine (800 mg/kg, intraperitoneally) caused significant decrease in the activities of 5′-nucleotidase, glucose-6-phosphatase and cytochrome P450 and increase in activities of γ-glutamyl transpeptidase, succinate dehydrogenase, acid phosphatase and acid ribonuclease in liver after 24 hr. The levels of RNA, protein and glycogen decreased while total lipids, phospholipids, cholesterol and lipid peroxides increased. It also increased the serum levels of transaminases, alkaline phosphatase and bilirubin while protein concentration decreased significantly. Oral administration of Picroliv (12 mg/kg/day for 7 days), a standardised iridoid glycoside fraction of Picrorhiza kurroa, significantly prevented the biochemical changes in liver and serum of galactosamine-toxicated rats. Kutkoside (12 mg/kg/day for 7 days) also protected against changes in most of the hepatic and serum constituents studied. Another iridoid glycoside from Picroliv, Picroside I, at the same dose level could only prevent toxicant-induced changes in acid phosphatase, phospholipids and lipid peroxides in liver and alkaline phosphatase in serum. Mixture of Picroside I and Kutkoside in the ratio of 1:1.5 at 12 mg/kg dose elicited lesser response than Picroliv. [ABSTRACT FROM AUTHOR]