Solving Donor Organ Shortage with Insights from Freeze Tolerance in Nature
Bibliographic record
Abstract
The North American wood frog, Rana sylvatica, endures seasonal whole-body freezing during the winter and thawing during the spring without sustaining any apparent damage from ice or oxidative stress. Strategies from these frogs may solve the shortage of human donor organs, which is a multidisciplinary problem that can be alleviated by eliminating geographical boundaries. Rana sylvatica deploys an array of molecular and physiological responses, such as glucose production and microRNA regulation, to help it survive the cold. These strategies have been adapted in the lab to impart cryotolerance in liver cells, and the non-freezing supercooled storage of transplantable rat livers - milestones that have advanced the field toward cryopreserving human donor organs in the clinic. In this review, a case is presented for the use of non-coding RNAs to decrease oxidative damage of donor organs by activating endogenous antioxidant systems prior to procurement.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".