Bibliographic record
Abstract
The importance of p53 in chemotherapy-induced apoptosis of cancer cells is well established. p53 plays a critical role in the cellular response to DNA damage by regulating genes involved in cell cycle progression, apoptosis, and genomic stability. As a result, p53 tumor status is a critical determinant of both responses to anti-cancer treatment and clinical prognosis. Interestingly, tumors expressing certain mutant forms of p53 ("gain of function") are particularly resistant to chemotherapy, even when compared to cells that lack any detectable p53. Until recently, the explanation for this enhanced chemoresistance was not clear. Recent studies have shown that the p53 homologues, p73 and p63, are also activated by chemotherapies, leading to tumor cell death. Now the discovery that mutant p53 interacts with p73, and that regulation of this interaction by a p53 polymorphism can modulate chemosensitvity provide a new model for how p53-family interactions can influence the response of tumors to anti-cancer therapies. Since p53 mutations are found in more than 50% of human tumors, strategies aimed at manipulating these interactions may prove useful in enhancing the chemotherapy response, and perhaps, overcoming chemoresistance.
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".