Foreskin surface area is not associated with sub-preputial microbiome composition or penile cytokines
Bibliographic record
Abstract
OBJECTIVE: Male circumcision (MC) reduces acquisition of HIV-1 in heterosexual men by at least 60%, but the biological mechanism for this protection is incompletely understood. Previous studies have shown that a larger foreskin size, increased abundance of anaerobic bacteria in the sub-preputial space, and higher levels of pro-inflammatory cytokines on the penis are all prospectively associated with risk of HIV-1 acquisition. Since coverage of the glans on the non-erect penis is dependent on foreskin size, a larger foreskin could result in a less aerobic environment that might preferentially support anaerobic bacterial growth and induce inflammation. We therefore assessed the relationship between foreskin size, penile microbiome composition and local inflammation. METHODS: This is a retrospective, cross-sectional analysis of 82 HIV-uninfected men who participated in a randomized trial of MC for HIV-1 prevention in Rakai, Uganda between 2003-2006. Sub-preputial swabs were collected prior to MC and assessed for cytokines (multiplexed immunosorbent assay) and bacterial load (qPCR) and taxon abundance (sequencing). Foreskin size was measured immediately after MC. RESULTS: Foreskin surface area did not correlate with total bacterial load (rho = 0.05) nor the abundance of key taxa of bacteria previously associated with HIV-1 risk (rho = 0.04-0.25). Foreskin surface area also did not correlate with sub-preputial cytokine concentrations previously associated with HIV-1 risk (IL-8 rho = 0.05). CONCLUSIONS: Larger foreskin size is not associated with either increased penile anaerobes or pro-inflammatory cytokines. These data suggest that foreskin size does not increase HIV-1 risk through changes in penile microbiome composition or penile inflammation.
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) | 0.004 | 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".