Life in the cold: links between mammalian hibernation and longevity
Bibliographic record
Abstract
The biological process of aging is the primary determinant of lifespan, but the factors that influence the rate of aging are not yet clearly understood and remain a challenging question. Mammals are characterized by >100-fold differences in maximal lifespan, influenced by relative variances in body mass and metabolic rate. Recent discoveries have identified long-lived mammalian species that deviate from the expected longevity quotient. A commonality among many long-lived species is the capacity to undergo metabolic rate depression, effectively re-programming normal metabolism in response to extreme environmental stress and enter states of torpor or hibernation. This stress tolerant phenotype often involves a reduction in overall metabolic rate to just 1-5% of the normal basal rate as well as activation of cytoprotective responses. At the cellular level, major energy savings are achieved via coordinated suppression of many ATP-expensive cell functions; e.g. global rates of protein synthesis are strongly reduced via inhibition of the insulin signaling axis. At the same time, various studies have shown activation of stress survival signaling during hibernation including up-regulation of protein chaperones, increased antioxidant defenses, and transcriptional activation of pro-survival signaling such as the FOXO and p53 pathways. Many similarities and parallels exist between hibernation phenotypes and different long-lived models, e.g. signal transduction pathways found to be commonly regulated during hibernation are also known to induce lifespan extension in animals such as Drosophila melanogaster and Caenorhabditis elegans. In this review, we highlight some of the molecular mechanisms that promote longevity in classic aging models C. elegans, Drosophila, and mice, while providing a comparative analysis to how they are regulated during mammalian hibernation.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".