Activité dominante négative des protéines p53 mutées
Bibliographic record
Abstract
Tumor suppressor gene inactivation as proposed by the Knudson model implies a sequential inactivation of two alleles of a gene. For example, the first allele is inactivated by a missense mutation, and the second one is inactivated by a deletion or insertion. The alteration of the p53 tumor suppressor gene is far to correspond only to this model. In the great majority of cancers, the mutated allele of p53 coexists with the normal allele. It is well known that the transcriptional activity is one of the most important functions of p53. The p53 protein is active as a tetramer (this complex activates the expression of targeted genes by binding to its consensus DNA sequence called the p53 response element). Experimental evidence shows that wild-type p53 interacts with mutant proteins to form heterotetramers. In association with wild-type proteins, mutant proteins drive the wild-type subunits into a mutant conformation. This association leads to a loss of trans-activating function. The capacity of mutant subunits to form heterotetramers with wild-type subunits and to commit them into a mutant conformation is called << dominant negative effect >>. Many p53 mutant proteins possess this dominant negative activity. Recently, several factors, which are implicated in the control of the dominant negative activity of p53 mutants, have been identified. The elucidation of these complex molecular functions, which are implicated in the dominant negative activity of the p53 mutated protein represents an important aspect in the comprehension of the biological mechanisms involved in carcinogenesis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".