Distinct Effects of the Cervicovaginal Microbiota and Herpes Simplex Type 2 Infection on Female Genital Tract Immunology
Bibliographic record
Abstract
Background: Genital inflammation is a key determinant of human immunodeficiency virus (HIV) transmission, and may increase HIV-susceptible target cells and alter epithelial integrity. Several genital conditions that increase HIV risk are more prevalent in African, Caribbean, and other black (ACB) women, including bacterial vaginosis and herpes simplex virus type-2 (HSV-2) infection. Therefore, we assessed the impact of the genital microbiota on mucosal immunology in ACB women and microbiome-HSV-2 interactions. Methods: Cervicovaginal secretions and endocervical cells were collected by cytobrush and Instead Softcup, respectively. T cells and dendritic cells were assessed by flow cytometry, cytokines by multiplex enzyme-linked immunosorbent assay (ELISA), and the microbiota by 16S ribosomal ribonucleic acid gene sequencing. Results: The cervicovaginal microbiota of 51 participants were composed of community state types (CSTs) showing diversity (20/51; 39%) or predominated by Lactobacillus iners (22/51; 42%), L. crispatus (7/51; 14%), or L. gasseri (2/51; 4%). High-diversity CSTs and specific bacterial phyla (Gardnerella vaginalis and Prevotella bivia) were strongly associated with cervicovaginal inflammatory cytokines, but not with altered endocervical immune cells. However, cervical CD4+ T-cell number was associated with HSV-2 infection and a distinct cytokine profile. Conclusions: This suggests that the genital microbiota and HSV-2 infection may influence HIV susceptibility through independent biological mechanisms.
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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 | 0.002 |
| 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 | 0.001 |
| 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".