Short-Term Storage of the Ischemically Damaged Human Pancreas by the Two-Layer Method Prior to Islet Isolation
Bibliographic record
Abstract
A two-layer cold storage method (TLM) allows sufficient oxygen delivery to pancreata during preservation and resuscitates the viability of ischemically damaged pancreata in the canine pancreas transplant model. In this study, we applied a short-term preservation of the TLM to human pancreata after prolonged cold ischemia prior to islet isolation, and investigated the mechanisms of resuscitation of the ischemically damaged human pancreas by the TLM. Human pancreata were procured from cadaveric donors and preserved by the TLM for 3.2 +/- 0.5 h after 11.1 +/- 0.9 h of cold storage in UW (TLM group), or by cold UW alone for 11.0 +/- 0.3 h (UW group). Islet isolations of all pancreata were performed using the Edmonton protocol. Islet recovery and in vitro functional viability of isolated islets were significantly increased in the TLM group compared with the UW group. According to the criteria of the Edmonton protocol, 10/14 cases (71%) in the TLM group were transplanted to patients with type I diabetes mellitus compared with only 5/21 cases (24%) in the UW group. In the metabolic assessment of human pancreata, levels of energetic parameters (ATP, total adenylates, and energy charge) were significantly increased, and malondialdehyde (MDA) levels were significantly decreased after the TLM preservation. There was no observable change in the incidence or degree of mitochondrial injury after the TLM preservation. Additional short-term storage by the TLM resuscitates the ischemically damaged human pancreas by regenerating the energetic status and prevents further damage by oxidative stress, ultimately leading to improvements of islet recovery and in vitro function. Use of the TLM following prolonged storage in UW provides an excellent adjunctive protocol for treating human pancreata for the rigors of the islet isolation process.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".