P3-S5.01 Microbial diversity of genital ulcer disease in men enrolled in a randomised trial of male circumcision in Kisumu, Kenya
Bibliographic record
Abstract
Background Medical male circumcision (MMC) reduces the risk of HIV acquisition by ∼60%, in part by reducing genital ulcer disease (GUD) and HSV-2. In the Ugandan and Kenyan trials, PCR did not find a sexually transmissible agent in 50–60% of GUD specimens, and MMC did not protect against HSV-2 in the Kenyan trial. We sought to better define the aetiology of GUD in the Kenyan trial and examine how MMC affects GUD aetiology. Methods We defined GUD of unknown aetiology as negative for HSV type 1 and type 2,T pallidum, andH ducreyiby PCR, and negative for HSV-2 andT pallidumby serology. In 59 GUD specimens from 59 men, 23 (39%) had unknown aetiology. We identified bacterial microbiota in all 59 specimens using multitag pyrosequencing of the 16S rRNA gene, and compared results by unknown vs STI aetiology. Statistical analysis employed exact methods. Results Overall, 83 distinct genera were detected. Prevotella spp. was most abundant, accounting for 18% of microbiota on average, and present in 75% of specimens. Bacterial diversity was greater in GUD of unknown aetiology than STI associated GUD (median number of genera 13 [range 7–20] vs 11 [range 3–20], p=0.06).Fusobacterium spp., Sneathia spp. and Anaerococcus spp.were more abundant in GUD of unknown aetiology (7.7%, 7.6%, and 7.3%, respectively) than in GUD of STI aetiology (4.6%, 5.0%, and 5.4%). Fusobacteriales (Fusobacterium spp.orSneathia spp.) [OR=4.7; 95% CI: 1.3 to 19.9] andAnaerococcus spp.[OR=4.6; 95% CI: 1.2 to 22.5] were more likely to be recovered in GUD of unknown aetiology than STI associated GUD. Fusobacteriales were more often recovered from uncircumcised men than circumcised men (62% vs 22%, p=0.04), and Anaerococcus spp. was present in 22% of circumcised vs 70% of uncircumcised men (p=0.010). Reported penile coital injuries were more common among men withAnaerococcus spp.(85% vs 57%, p=0.01), and condom use was less common (50% vs 71%, p=0.11). There was no difference in these bacteria by ulcer location. Conclusions Fusobacteriales andAnaerococcus spp.may colonise genital ulcers that develop from a mechanism related to circumcision status. Many such “ulcers” may be epithelial disruptions that are traumatic in origin. These bacteria have cytotoxic properties that may ulcerate or exacerbate pre-existing minor epithelial disruptions. MMC may reduce GUD through a reduction in these anaerobic bacteria.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".