Molecular Mechanisms of Human Papillomavirus-Induced Carcinogenesis
Bibliographic record
Abstract
Approximately 20% of all cancers are associated with infectious agents. Among them, human papillomaviruses (HPVs) are very common and are now recognized as the etiological agent of cervical cancer, the second most common cancer in women worldwide, and they are increasingly linked with other forms of dysplasia. Carcinogenesis is a complex and multistep process requiring the acquisition of several genetic and/or epigenetic alterations. HPV-induced neoplasia, however, is in part mediated by the intrinsic functions of the viral proteins. In order to replicate its genome, HPV modulates the cell cycle, while deploying mechanisms to escape the host immune response, cellular senescence and apoptosis. As such, HPV infection leads directly and indirectly to genomic instability, further favouring transforming genetic events and progression to malignancy. This review aims to summarize our current understanding of the molecular mechanisms exploited by HPV to induce neoplasia, with an emphasis on the role of the 2 viral oncoproteins E6 and E7. Greater understanding of the role of HPV proteins in these processes will ultimately aid in the development of antiviral therapies, as well as unravel general mechanisms of oncogenesis.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".