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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".