Review of mutant p53 protein and the p53 targeting therapy in cancer treatment
Bibliographic record
Abstract
Cancer in humans is a disease that has been difficult to treat due to properties it is able to obtain after being introduced to an organism and has been one of the most prominent points of research in drug development. Since cancers can take on a multitude of forms, a popular strategy employed to find therapies for it is by identifying common features among cancers. A well-known alteration in half of all human cancers is TP53 mutations, of which there are more than 500. This literary review first discusses the additional capabilities cancer cells obtain, then a discussion of the various functions of p53 and the mutations it can take on. The central focus of this review will be an elucidation of the major approaches attempted in the development of cancer treatment through p53: viruses, targeting gain-of-function mutant p53, structural reactivation of mutant p53 to restore wild type activity, the depletion of mutant p53, and targeting mutant p53 through synthetic lethal inhibitors. Through exploring the different therapies, it is a universal goal to elicit one single treatment for mutant p53 that can impact the greatest amount of p53 mutations while retaining the ability to suppress or even prevent and inhibit cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".