Transcriptional Activation of p53 during Cold Induced Torpor in the 13-Lined Ground Squirrel<i>Ictidomys tridecemlineatus</i>
Bibliographic record
Abstract
The transcription factor p53 is located at the centre of multiple pathways relating the cellular response to stress. Commonly known as a tumor suppressor, it is responsible for initiating diverse actions to protect the integrity of the genome, ranging from cell cycle arrest to apoptosis. This study investigated the regulation of p53 protein in hibernating 13-lined ground squirrel Ictidomys tridecemlineatus during multiple stages of the torpor-arousal cycle. Transcript and protein levels of p53 were both elevated in the skeletal muscle during early and late torpor stages of the hibernation cycle. Nuclear localization of p53 was also increased during late torpor, and this is associated with an increase in its DNA binding activity and expression of p53 transcriptional targets p21CIP, gadd45α, and 14-3-3σ. The increase in p53 transcriptional activity appears to be independent of its phosphorylation at Ser-15, Ser-46, and Ser-392, consistent with an absence of checkpoint kinase activation during torpor. Sequence analysis revealed unique amino acid substitutions in the ground squirrel p53 protein, which may contribute to an increase in protein stability compared to nonhibernators. Overall, the study results provided evidences for a potential role of p53 in the protection of the skeletal muscle during torpor.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".