Innovations in understanding the biology of cervical cancer
Bibliographic record
Abstract
Revelation of the connection between the human papillomavirus (HPV) and cervical neoplasia and invasive cervical cancer is prompting new investigations to expand that understanding and promote vaccines, gene therapy, and other interventions. At the Second International Conference on Cervical Cancer (Houston, TX, April 11-14, 2002), laboratory and clinical researchers reported advances in new studies meant to increase understanding of the natural history of HPV and cervical intraepithelial neoplasia, to evaluate new cervical cancer screening techniques, and to promote new therapies. Using K14-HPV type 16 transgenic mice, researchers are investigating the effects of estrogen on cervical cancer carcinogenesis, and results are lending support to epidemiological theories showing a difference in HPV infection rates and the development of cervical lesions in women using oral contraceptives. Other work involves investigating genes that are up-regulated by HPV infection and the role of the p53 homologue, p63, in cervical neoplasia evolution. Telomerase also is under investigation as a biomarker in high-risk populations. Gene therapy that replaced p53 in cervical cancer cell lines in vitro and a nude mouse model inhibited cell and tumor growth, confirming previous findings in squamous epithelial carcinomas of the head and neck. Furthermore, research in intracellular targeting of antigens to subcellular locations shows promise for treating cervical cancer preclinically. Identification of molecular changes in cervical cancer and knowledge about the importance of HPV infection in cervical cancer can lead to new therapies to treat existing cervical cancer and, in the long term, prevent the disease.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".