Phylogenetic Analysis of Human Papillomavirus 16 and 52 L1 Gene from Cervical Cancer in Bandung, Indonesia
Bibliographic record
Abstract
BACKGROUND: Chronic infection with high-risk type of human papillomavirus (HPV) can cause cervical cancer. Previous studies showed that multiple infections of HPV are found in cervical cancer caused by multiple HPV infections and the most common are HPV-16 and HPV-52. The origin of HPV-16 circulating in Indonesia varies. The purpose of this study was to explore the origin of multiple infections of HPV-52 and HPV-16 in cervical cancer by using a phylogenetic tree.METHODS: During July-November 2010, 100 women were diagnosed with cervical cancer in the Department of Obstetrics and Gynecology, Dr. Hasan Sadikin General Hospital, Bandung, Indonesia. Only 96 patients were involved in this study. Ninety-six samples of HPV deoxyribonucleic acid (DNA) were isolated from biopsied tissue of cervical cancer. Multiple infections of HPV genotypes HPV-16 and HPV-52 were confirmed by using the linear assay for HPV genotyping test. Afterward,HPV-52L1 gene was amplified by using self-designed primer. L1 gene was also sequenced and analyzed using phylogenetic program (MEGA6.06).RESULTS: The result of phylogenetic tree construction showed that isolated HPV-52 originated from multiple infections of HPV-16 and HPV-52 from cervical cancer patients in Bandung were in a subgroup with isolates originating from EU077219 Canada (America) and KT799980 southwest China (Asia). Isolate HPV-16 in one subgroup with isolates originating from KU951191.1 (Southwest China).CONCLUSION: L1 gene sequence from multiple infections isolated from HPV-16 and HPV-52 from cervical cancer patients in Bandung refers to the variation of L1 gene reported from Canada and southwest China. This proves that Indonesia’s HPV clusters are located in the strains found in America and Asia.KEYWORDS: multiple infections, HPV-16, HPV-52, L1 gene, phylogenetic
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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 | 0.001 | 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".