Hibernation-inspired innovations in biomedicine: addressing aging, chronic diseases, and critical care
Bibliographic record
Abstract
This review explores hibernation-inspired innovations to address major healthcare challenges, including aging and chronic diseases. The study of hibernating mammals offers unique insights into extreme metabolism that could revolutionize treatments for various conditions. Hibernation provides natural examples of reversible mass gain, insulin resistance, hypothermia, and metabolic suppression, which have direct applications to diabetes management and ischemia-reperfusion injury. The ability of hibernating animals to emerge without neuronal, muscle, or bone loss highlights potential avenues for treating neurodegenerative and age-related diseases. This review discusses the journey from discovery to translation, emphasizing the importance of understanding the phenology and physiological ecology of target species. It highlights the challenges and advancements in developing tools for non-model organisms, which have opened new opportunities for hibernation-inspired drug discovery. Hibernators' resistance to bone and muscle atrophy, combined with adaptive anorexia, offers insights for obesity. Nitrogen recycling provides strategies for treating sarcopenia and preventing ammonia toxicity. Applications of hibernation science in stroke and cardiac arrest include improvements in targeted temperature management and application of temperature-independent neuroprotection. Interbout arousals in hibernators offers insights into tolerance of rapid rewarming and reperfusion, which could inform treatments for ischemia-reperfusion injuries. This review synthesizes the potential of hibernation research in advancing biomedical innovations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".