Analysis of colorectal cancer and polyp for presence herpes simplex virus and cytomegalovirus DNA sequences by polymerase chain reaction
Bibliographic record
Abstract
Introduction:In recent years, it was demonstrated that there is a clear association between the complicated course of colorectal cancer (CRC) and the presence of herpes viruses.Despite a great number of published reports, the exact pathogenic role of herpes viruses remains unclear in these patients.The purpose of this study is to explore the prevalence of herpes simplex virus (HSV) and cytomegalovirus (CMV) in patients with CRC and polyp in comparison with healthy subjects using the polymerase chain reaction (PCR) method.Methods: In this case-control study, 15 biopsies of patients with CRC and 20 colorectal polyp sample were selected.From each patient, two tissue samples were obtained: one sample from malignant tissue, and the other from normal colorectal tissue in an area located 15 cm away from the malignant tissue.Furthermore, 35 samples from healthy people as controls were selected.After DNA extraction, PCR was used to determine HSV and CMV genomes by specific primers.A statistical analysis was performed using the chi-square test.Results: Five CRC patients (33.3%) had HSV DNA detected in both the malignant and the matched normal tissue.Five CRC patients (33.3%) and seven polyp patients (35.0%) had CMV DNA detected in both the malignant and the matched normal tissue.HSV DNA was found in 20% and CMV DNA in 37.1% of samples from healthy people as a control group.Thus, no significant association was observed between the prevalence of HSV and CMV, and an incidence of CRC and polyps according to the location of the samples as compared with the control group.Conclusion: The findings demonstrated that there is no direct molecular evidence to support the association between HSV and CMV and human colorectal malignancies.However, the results from this study do not exclude a possible oncogenic role of these viruses in the neoplastic development of colon cells.
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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 | 0.002 | 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) | 0.002 | 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".