The High Prevalence of Oncogenic HPV Genotypes Targeted by the Nonavalent HPV Vaccine in HIV-Infected Women Urgently Reinforces the Need for Prophylactic Vaccination in Key Female Populations Living in Gabon
Bibliographic record
Abstract
Background/Objectives. Women living with human immunodeficiency virus (WLWH) have a six-fold higher risk of developing cervical cancer associated with high-risk human Papillomavirus (HR-HPV) than HIV-negative women. We herein assessed HR-HPV genotype distribution and plasma levels of the cancer antigen 125 (CA-125) in WLWH in a rural town in Gabon, in Central Africa. Methods. Adult WLWH attending the local HIV outpatient center were prospectively enrolled and underwent cervical visual inspection and cervicovaginal and blood sampling. HIV RNA load and CA-125 levels were measured from plasma using the Cepheid® Xpert® HIV-1 Viral Load kit and BioMérieux VIDAS® CA-125 II assay, respectively. HPV detection and genotyping were performed via a nested polymerase chain reaction (MY09/11 and GP5+/6+), followed by sequencing. Results. Fifty-eight WLWH (median age: 52 years) were enrolled. Median CD4 count was 547 cells/µL (IQR: 412.5–737.5) and HIV RNA load 4.88 Log10 copies/mL (IQR: 3.79–5.49). HPV prevalence was 68.96%, with HR-HPV detected in 41.37% of women. Among HR-HPV-positive samples, 87.5% (21/24) were genotypes targeted by the Gardasil vaccine, while 12.5% (3/24) were non-vaccine types. Predominant HR-HPV types included HPV-16 (13.8%), HPV-33 (10.34%), HPV-35 (5.17%), HPV-31, and HPV-58 (3.45%). Most participants had normal cervical cytology (62.07%), and a minority (14.29%) had elevated CA-125 levels, with no correlation to cytological abnormalities. Conclusions. In the hinterland of Gabon, WLWH are facing an unsuspected yet substantial burden of cervical HR-HPV infection and a neglected risk for cervical cancer. Strengthening cervical cancer prevention through targeted HPV vaccination, sexual education, and accessible screening strategies will help in mitigating associated risk.
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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 | 0.000 | 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".