Anti-apoptotic signaling as a cytoprotective mechanism in mammalian hibernation
Bibliographic record
Abstract
In the context of normal cell turnover, apoptosis is a natural phenomenon involved in making essential life and death decisions. Apoptotic pathways balance signals which promote cell death (pro-apoptotic pathways) or counteract these signals (anti-apoptotic pathways). We proposed that changes in anti-apoptotic proteins would occur during mammalian hibernation to aid cell preservation during prolonged torpor under cellular conditions that are highly injurious to most mammals (e.g. low body temperatures, ischemia). Immunoblotting was used to analyze the expression of proteins associated with pro-survival in six tissues of thirteen-lined ground squirrels, Ictidomys tridecemlineatus. The brain showed a concerted response to torpor with significant increases in the levels of all anti-apoptotic targets analyzed (Bcl-2, Bcl-xL, BI-1, Mcl-1, cIAP1/2, xIAP) as well as enhanced phosphorylation of Bcl-2 at S70 and T56. Heart responded similarly with most anti-apoptotic proteins elevated significantly during torpor except for Bcl-xL and xIAP that decreased and Mcl-1 that was unaltered. In liver, BI-1 increased whereas cIAP1/2 decreased. In kidney, there was an increase in BI-1, cIAP and xIAP but decreases in Bcl-xL and p-Bcl-2(T56) content. In brown adipose tissue, protein levels of BI-1, cIAP1/2, and xIAP decreased significantly during torpor (compared with euthermia) whereas Bcl-2, Bcl-xL, Mcl-1 were unaltered; however, Bcl-2 showed enhanced phosphorylation at Thr56 but not at Ser70. In skeletal muscle, only xIAP levels changed significantly during torpor (an increase). The data show that anti-apoptotic pathways have organ-specific responses in hibernators with a prominent potential role in heart and brain where coordinated enhancement of anti-apoptotic proteins occurred in response to 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".