Central nervous system regulation of mammalian hibernation: implications for metabolic suppression and ischemia tolerance
Bibliographic record
Abstract
Torpor during hibernation defines the nadir of mammalian metabolism where whole animal rates of metabolism are decreased to as low as 2% of basal metabolic rate. This capacity to decrease profoundly the metabolic demand of organs and tissues has the potential to translate into novel therapies for the treatment of ischemia associated with stroke, cardiac arrest or trauma where delivery of oxygen and nutrients fails to meet demand. If metabolic demand could be arrested in a regulated way, cell and tissue injury could be attenuated. Metabolic suppression achieved during hibernation is regulated, in part, by the central nervous system through indirect and possibly direct means. In this study, we review recent evidence for mechanisms of central nervous system control of torpor in hibernating rodents including evidence of a permissive, hibernation protein complex, a role for A1 adenosine receptors, mu opiate receptors, glutamate and thyrotropin-releasing hormone. Central sites for regulation of torpor include the hippocampus, hypothalamus and nuclei of the autonomic nervous system. In addition, we discuss evidence that hibernation phenotypes can be translated to non-hibernating species by H(2)S and 3-iodothyronamine with the caveat that the hypothermia, bradycardia, and metabolic suppression induced by these compounds may or may not be identical to mechanisms employed in true hibernation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".