What can naked mole‐rats teach us about ameliorating hypoxia‐related human diseases?
Bibliographic record
Abstract
Abstract Ameliorating the deleterious impact of systemic or tissue‐level hypoxia or ischemia is key to preventing or treating many human diseases and pathologies. Usefully, environmental hypoxia is also a common challenge in many natural habitats; animals that are native to such hypoxic niches often exhibit strategies that enable them to thrive with limited O 2 availability. Studying how such species have evolved to tolerate systemic hypoxia offers a promising avenue of discovery for novel strategies to mitigate the deleterious effects of hypoxia in human diseases and pathologies. Of particular interest are naked mole‐rats, which are among the most hypoxia‐tolerant mammals. Naked mole‐rats that tolerate severe hypoxia in a laboratory setting are also protected against clinically relevant mimics of heart attack and stroke. The mechanisms that support this tolerance are currently being elucidated but results to date suggest that metabolic rate suppression, reprogramming of metabolic pathways, and mechanisms that defend against deleterious perturbations of cellular signaling pathways all provide layers of protection. Herein, we synthesize and discuss what is known regarding adaptations to hypoxia in the naked mole‐rat cardiopulmonary system and brain, as these systems comprise both the primary means of delivering O 2 to tissues and the most hypoxia‐sensitive organs in mammals.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".