ABSTRACT
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.
Subject(s)
Biomarkers , Machine Learning , Microbiota , Vaginosis, Bacterial , Humans , Female , Vaginosis, Bacterial/diagnosis , Vaginosis, Bacterial/microbiology , Biomarkers/metabolism , Adult , Metabolomics/methods , Young Adult , Vagina/microbiology , Metabolome , RNA, Ribosomal, 16S/genetics , MultiomicsSubject(s)
Bladder Exstrophy/surgery , Cesarean Section/methods , Postoperative Complications , Pregnancy Complications , Urinary Incontinence/etiology , Urologic Surgical Procedures/adverse effects , Female , Humans , Postoperative Complications/diagnosis , Postoperative Complications/surgery , Pregnancy , Pregnancy Complications/diagnosis , Pregnancy Complications/etiology , Pregnancy Complications/physiopathology , Pregnancy Outcome , Premature Birth/surgery , Urinary Incontinence/diagnosis , Urinary Incontinence/physiopathology , Urologic Surgical Procedures/methods , Young AdultABSTRACT
Strumal carcinoid develops in a germ cell tumor characterized by an intimate mixture of thyroid and carcinoid. Unlike other carcinoid tumors, most patients with strumal carcinoid have no symptoms of carcinoid syndrome; few are reported to cause severe constipation. We report a case of a 60-year-old female patient presenting with severe progressive constipation and painful defecation for last few years. A right ovarian tumor was discovered during clinical examination. CT scan revealed a large lobulated solid cystic right adenexal mass suggestive of monomorphic teratoma, which was successfully removed surgically. The histopathology confirmed the diagnosis of strumal carcinoid tumor. Her constipation completely disappeared post surgery. These tumors are known to produce a biologically active substance like peptide YY, which has a pharmacologic inhibitory action on intestinal motility. This was presumably the cause of the constipation in this patient rather than a mechanical effect of the tumor.
