Effect of simultaneous silencing of HPV-18 E6 and E7 on inducing apoptosis in HeLa cellsThis paper is one of a selection of papers published in this special issue entitled “Second International Symposium on Recent Advances in Basic, Clinical, and Social Medicine” and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
The objective of this investigation was to determine if simultaneous silencing of the human papillomavirus type 18 (HPV-18) E6 and E7 oncogenes using RNA interference (RNAi) would be a potential therapeutic approach against the carcinogenic activity of this virus. Two synthetic double-stranded oligonucleotides, encoding short hairpin transcripts corresponding to HPV-18 E6 and E7 genes, were cloned into pGenesilence (pGS) 1.0 vectors to produce pGS-E6, pGS-E7, and pGS-(E6+E7), respectively. Our results showed that the expression of HPV-18 E6 class 1 and HPV-18 E7 in HeLa cells was markedly decreased after being transfected with pGS-E6, pGS-E7, and pGS-(E6+E7) vectors. Of the three vectors, pGS-(E6+E7) had a greater ability to decrease the growth rate of HeLa cells, inhibit colony formation in soft agar, and significantly reduce tumor growth in nude mice. We also found that depletion of HPV-18 E6 and E7 in this manner promoted apoptosis of HeLa cells. Our data showed that simultaneously decreasing HPV-18 E6 and E7 gene expression in HeLa cells by RNAi could significantly inhibit tumor growth under in vitro conditions and in nude mice. These data suggest that gene therapy may be a possible therapeutic approach for HPV-positive cervical cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".