Tribute to P. L. Lutz: putting life on `pause' – molecular regulation of hypometabolism
Bibliographic record
Abstract
Entry into a hypometabolic state is an important survival strategy for many organisms when challenged by environmental stress, including low oxygen, cold temperatures and lack of food or water. The molecular mechanisms that regulate transitions to and from hypometabolic states, and stabilize long-term viability during dormancy, are proving to be highly conserved across phylogenic lines. A number of these mechanisms were identified and explored using anoxia-tolerant turtles as the model system, particularly from the research contributions made by Dr Peter L. Lutz in his explorations of the mechanisms of neuronal suppression in anoxic brain. Here we review some recent advances in understanding the biochemical mechanisms of metabolic arrest with a focus on ideas such as the strategies used to reorganize metabolic priorities for ATP expenditure, molecular controls that suppress cell functions (e.g. ion pumping, transcription, translation, cell cycle arrest), changes in gene expression that support hypometabolism, and enhancement of defense mechanisms (e.g. antioxidants, chaperone proteins, protease inhibitors) that stabilize macromolecules and promote long-term viability in the hypometabolic state.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".