Resolution and co-occurrence patterns of <i>Gardnerella leopoldii</i> , <i>Gardnerella swidsinskii</i> , <i>Gardnerella piotii</i> and <i>Gardnerella vaginalis</i> within the vaginal microbiome
Bibliographic record
Abstract
Abstract Background Gardnerella vaginalis is a hallmark of vaginal dysbiosis, but is found in the microbiomes of women with and without vaginal symptoms. G. vaginalis encompasses diverse taxa differing in attributes that are potentially important for virulence, and there is evidence that ‘clades’ or ‘subgroups’ within the species are differentially associated with clinical outcomes. The G. vaginalis species description was recently emended, and three new species within the genus were defined ( leopoldii , swidsinskii , piotii ). 16S rRNA sequences for the four Gardnerella species are all >98.5% identical and no signature sequences differentiate them. Results We demonstrated that Gardnerella species can be resolved using partial chaperonin-60 (cpn60) sequences, with pairwise percent identities of 87.1-97.8% among the type strains. Pairwise co-occurrence patterns of Gardnerella spp. in the vaginal microbiomes of 413 reproductive aged Canadian women were investigated, and several significant co-occurrences of species were identified. Abundance of G. vaginalis , and swidsinskii was associated with vaginal symptoms of abnormal odour and discharge. Conclusions cpn60 barcode sequencing can provide a rapid assessment of the relative abundance of Gardnerella spp. in microbiome samples, providing a powerful method of elucidating associations between these diverse organisms and clinical outcomes. Researchers should consider using cpn60 in place of 16S RNA for better resolution of these important organisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".