The vaginal microbiota, symptoms, and local immune correlates in transmasculine individuals using sustained testosterone therapy
Bibliographic record
Abstract
Transmasculine individuals (assigned female at birth, masculine gender identity, TM) may use gender-affirming testosterone therapy, and some report adverse genital symptoms during treatment. In cis women, the vaginal microbiota is central to reproductive and sexual health. Lactobacillus-dominant communities are considered optimal, while diverse, Lactobacillus-depleted microbiota are non-optimal. Prior studies suggest Lactobacillus deficiency in TM vaginal microbiota, but associations with symptoms and immune markers remain unclear. We launched the TransBiota study to characterize the TM vaginal microbiota, soluble mediators of local inflammation, and self-reported symptoms over three weeks. Fewer than 10% of TM possess Lactobacillus-dominant microbiota, and most exhibit diverse, Lactobacillus-depleted microbiota. We identify 11 vaginal microbiota community state types (tmCSTs), with Lactobacillus-dominant tmCSTs unexpectedly linked to abnormal odor and elevated interleukin-1α. However, Lactobacillus dominance is not associated with other key symptoms, such as dyspareunia and vaginal dryness, underscoring that microbiome-symptom relationships in TM are more complex and warrant further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".