Human papillomavirus genotype prevalence in the women of Shanghai, China and its association with the severity of cervical neoplasia.
Bibliographic record
Abstract
AIMS: Human papillomavirus (HPV) viral load and genotype are the primary determinants for the development of cervical neoplasia. We aim to identify the prevalent HPV genotypes in the women of Shanghai, China and investigate the association between the HPV viral load and the severity of cervical neoplasia. METHODS: Formalin-fixed, paraffin-embedded tissue samples were obtained from 20 cases of histologically normal cervix, 52 cases of low grade squamous intraepithelial lesion (LSIL), 46 cases of high grade squamous intraepithelial lesion (HSIL), and 29 cases of cervical squamous cervical cancer (SCC). A polymerase chain reaction reverse dot blot (PCR-RDB) genotyping chip was used to examine 23 HPV genotypes. Real-time quantitative PCR was used to detect the viral load of HPV in the fresh tissue of 80 cases. RESULTS: The HPV infection rate in the 147 cases of cervical biopsies was 73.5%. Fourteen HPV genotypes were detected, including 12 high risk (HR)-HPVs and 2 low-risk (LR)-HPVs. HPV-16 (33.3%), HPV-31 (6.1%), HPV-52 (6.1%), and HPV-58 (5.4%) were the most popular genotypes of HR-HPV. Significant differences were found in HPV viral load between histologically normal cervix and cervix tissues with epithelial dysplasia (P<0.05). There was a statistically insignificant trend of gradual increase of viral load as the epithelial lesion progresses from LSIL to HSIL and to SCC (P>0.05). CONCLUSIONS: HPV 16, 31, 52, and 58 are the most prevalent genotypes in women of Shanghai, China. HPV viral load is an indicator of the presence of cervical neoplasia but not an accurate predictor for the severity of cervical neoplasia.
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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 | 0.000 | 0.000 |
| 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".