Ameliorating Injury during Preservation and Isolation of Human Islets Using the Two-Layer Method with Perfluorocarbon and UW Solution
Bibliographic record
Abstract
This study assessed the effects of a two-layer method (TLM), using perfluorocarbon and UW solution, on the quality of human pancreata following storage and islet yield/function after isolation. In part A, TLM was applied immediately after procurement and the energetic profile was compared to a group treated with UW solution only (control) throughout 24-h storage. In part B, cadaveric human pancreata were procured and subjected to a TLM after cold storage in UW solution (TLM group) or UW solution (control group). Energetics, lipid peroxidation, and islet recovery/function were assessed after preservation at 4 degrees C. In part A, after 9-h storage, the energetic profile (ATP, ATP/ADP, energy charge) for the TLM group was superior to controls. In part B, TLM treatment resulted in consistently greater ATP, ATP/ADP, and energy charge values than with storage in UW solution alone (p < 0.05). UW treatment resulted in 40% greater peroxidative damage than in the TLM group (p < 0.05). Islet recovery and functional viability were 30-40% higher following TLM treatment (p < 0.05). These data support the hypothesis that islet viability and yields can be significantly improved using a brief period of TLM treatment following conventional UW storage; reduced energetic and oxidative stress are implicated as potential mechanisms.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".