Detection of Human Papilloma Virus (HPV) Infection in Colorectal Cancer and Viral Genome Integration Status
Bibliographic record
Abstract
Background: Colorectal cancer (CRC) is the third common cause of malignancy in Iran, and its incidence rate has been raised due to an increase in individual and environmental risk factors. Objectives: The present study aimed to define the role of human papillomavirus (HPV) infection in CRC, then by drawing a phylogenetic tree, we aimed to analysis the sequences of isolated HPVs, and finally determine the viral genome physical status for HPV-16. Methods: A nested-PCR (nPCR), direct sequencing, and INNO-LiPA HPV genotyping assay were carried out for HPV detection, and a quantitative real-time PCR assay (qRT-PCR) was conducted for the physical status of the viral genome. Results: Among 157 subsets, there were 66 cases and 91 controls. Moreover, 5.7% (9/157) were HPV positive by nPCR, among whom 9.1% (6/66) were cases and 3.3% (3/91) were control groups (P = 0.750). The qRT-PCR analysis showed three forms of integrated, episomal, and mixed forms in our three HPV-16 strains. A higher rate of HPV infection was detected in the CRC cases; however, it was not statistically significant. Conclusions: Although there were limited positive results, higher ages (> 65) were related to HPV involvement and CRC outcome. Furthermore, 9.1% of the CRC cases were infected with HPVs. The HPV-16 genome integration is observed in high-grade cancer cases with severe malignancy in CRC patients.
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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 | 0.001 | 0.002 |
| 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".