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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".