Novel Drug Delivery Platform for the Topical Treatment of Cervical Cancer
Bibliographic record
Abstract
Cervical cancer was ranked fourth of all cancer deaths among women globally in 2012. Although vaccines were developed as prophylaxis, they do not cure existing infection, nor do they provide protection against all types of the causative virus (Human Papillomavirus). The current treatments of cervical cancer, including surgery, radiotherapy, and chemotherapy, have not improved the 5-year survival rate over the last decades, and were associated with undesirable systemic side effects. Therefore, there is a pressing need of novel strategies for cervical cancer treatment. Aurora A Kinase was recently identified as critical for the survival of human-papillomavirus-transformed cervical cancer, which accounts for more than 99% of cervical cancer cases, and a complete regression of the disease was achieved in mouse models by inhibiting this enzyme using Alisertib (MLN8237) by Takeda (Japan). This effect was due to Alisertib sensitivity induced by the HPV oncogene, E7, providing a rationale for testing this drug to treat - HPV-driven 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".