Enhanced IgA coating of bacteria in women with Lactobacillus crispatus-dominated vaginal microbiota
Bibliographic record
Abstract
BACKGROUND: Immunoglobulin A (IgA) plays an important role in maintaining a healthy intestinal microbiome, but little is known about the interaction between local immunoglobulins and the vaginal microbiome. We assessed immunoglobulins (unbound and bound to bacteria), their association with vaginal microbiota composition and the changes over time in 25 healthy women of reproductive age. RESULTS: In both Lactobacillus crispatus-dominated and non-L. crispatus-dominated microbiota, IgA and IgG (unbound and bound to bacteria) were higher during menses (T = 1) compared to day 7‑11 (T = 2) and day 17‑25 (T = 3) after menses onset. The majority of vaginal bacteria are coated with IgA and/or IgG. Women with L. crispatus-dominated microbiota have increased IgA coating of vaginal bacteria compared to women with other microbiota compositions, but contained less IgA per bacterium. Presence of a dominantly IgA-coated population at T = 2 and/or T = 3 was also strongly associated with L. crispatus-dominated microbiota. In women with non-L. crispatus-dominated microbiota, more bacteria were uncoated. Unbound IgA, unbound IgG, and bound IgG levels were not associated with microbiota composition. CONCLUSIONS: In conclusion, L. crispatus-dominated vaginal microbiota have higher levels of bacterial IgA coating compared to non-L. crispatus-dominated vaginal microbiota. Similar to its regulating function in the intestinal tract, we hypothesize that IgA is involved in maintaining L. crispatus-dominated microbiota in the female genital tract. This may play a role in L. crispatus-associated health benefits. Video abstract.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".