Human Papillomavirus Genotypes Distribution in High-Grade Cervical Lesions and Invasive Cervical Carcinoma in Women Living in Mauritania: Implications for Cervical Cancer Prevention and HPV Prophylactic Vaccination
Bibliographic record
Abstract
Cervical cancer related to high-risk human papillomavirus (HR-HPV) is the second female cancer in Mauritania (Northwest Sahelian Africa). We assessed the distribution of HPV genotypes in Mauritanian women with high-grade cervical intraepithelial neoplasia (CIN2/3) or invasive cervical cancer (ICC). A prospective study was conducted in the Centre Hospitalier National, Nouakchott, Mauritania, to collect cervical biopsies among women suspected of CIN2/3 or cancer. HPV DNA detection and genotyping were carried out from formalin-fixed, paraffin-embedded biopsies using multiplex PCR (Human Papillomavirus Genotyping Real-Time PCR Kit, Bioperfectus Technologies Co., Taizhou, China). Fifty biopsies were included from women (mean age: 56.7 years) suffering from CIN2/3 (28.0%) and ICC (72.0%) which corresponded to 32 (64.0%) squamous cell carcinomas (SCC) and 4 (8.0%) adenocarcinomas (ADC). HPV DNA detection was successful in 47 (94.0%) samples. The most prevalent HR-HPV genotypes were HPV-45 (40.4%), HPV-16 (38.3%), HPV-39 and HPV-52 (23.4%), HPV-33 (17.0%), HPV-18 (14.9%), HPV-35 (4.2%), and HPV-56 (2.1%). The majority (93.6%) of HPV-positive biopsies contained at least one HPV type covered by the 9-valent Gardasil-9® vaccine, and 40.9% were infected by multiple vaccine HPV genotypes. To eradicate cervical cancer in Mauritania, prophylactic HPV vaccination must be combined with primary molecular screening of cervical HR-HPV infection.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".