Genetic diversity of HPV16 and HPV18 in Brazilian patients with invasive cervical cancer
Bibliographic record
Abstract
Cervical cancer is the fourth most common cancer among women, and ∼70-80% of these cancers are associated with two human papillomavirus types: HPV16 and HPV18. Several studies have reported that intra-type diversity is associated with the progression of infection to invasive cancer. Herein, we report the genetic diversity of HPV16 and HPV18 in a cohort of 594 Brazilian women with invasive cervical cancer and describe the prevalence of lineages and intra-type diversity prior to the implementation of the public immunization program in Brazil. HPV detection and genotyping were performed using PCR, PGMY/GP primers, and DNA extracted from fresh tumors. The HPV16 (378 women) and HPV18 (80 women) lineages were identified by PCR and sequencing of the LCR and E6 fragments, followed by SNV comparison and phylogenetic analysis. In our cohort, was found a higher frequency of the lineage A (in 217 women), followed by lineage D (in 97 women) and lineages B and C (in 10 women each) for HPV16; and a higher frequency of lineage A (in 56 women) followed by lineage B (in 15 women) in HPV18. The genetic diversity of HPV16 indicated a recent expansion of specific variants or a selective advantage that is associated with invasive cancer; this pattern was not observed for HPV18.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".