Correlation of Human Papillomavirus Type 16 and Human Papillomavirus Type 18 E7 Messenger RNA Levels with Degree of Cervical Dysplasia
Bibliographic record
Abstract
Infection with certain types of human papillomavirus (HPV) is a necessary event in the development of cervical carcinoma; however, not all women who become infected with HPV will progress to cancer. Much is known about the molecular influence of HPV E6 and E7 proteins on the malignant transformation. Little is known about the additional factors needed to drive the process. Quantitative real-time PCR was used to quantitate mRNA expression of the E7 gene in women exhibiting normal epithelium, low-grade squamous intraepithelial lesions (LSIL), and high-grade squamous intraepithelial lesions (HSIL). Prevalence of mRNA transcripts was lower among normal women (27%) than for women with LSIL (40%) and HSIL (37%). Mean levels ranged from 2.0 (ln scale per 20 ng cDNA) among normal women to 4.2 among those with HSIL, with a significant trend (P=0.008). This trend was only significant for HPV 18 transcripts if separately analyzed by HPV type. The transcriptional activity of HPV 18 is higher than that of HPV 16 and increases with increasing level of dysplasia. This is in concert with the findings of other studies, and reinforces the notion that HPV 18 is a more aggressive viral type. Real-time PCR of viral transcripts could provide a more efficient method to analyze the oncogenic potential within cells from a cervical swab, thus providing a way to better screen women who may progress to higher grade lesions or invasive carcinoma from those who will spontaneously regress.
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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 | 0.000 |
| 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".