Influence of Pancreas Preservation on Human Islet Isolation Outcomes: Impact of the Two-Layer Method
Bibliographic record
Abstract
BACKGROUND: Human pancreas preservation for islet transplantation holds additional challenges and considerations compared with whole pancreas transplantation. The purpose of this study was to clarify the limitations of the University of Wisconsin (UW) solution and the potentials of the two-layer method (TLM) for pancreas preservation before human islet isolation. METHODS: We retrospectively evaluated human islet isolation records between January 2001 and February 2003. One hundred forty-two human pancreata were procured from cadaveric donors and preserved by means of the UW solution (n=112) or TLM (n=30). Human islet isolations were performed using a standard protocol and assessed by islet recovery and in vitro function of islets. RESULTS: Eight to ten hours of cold ischemia in the UW solution is a critical point for successful islet isolations. It is difficult to recover a sufficient number of viable islets for transplantation from human pancreata with more than 10 hours of cold storage in the UW solution. The overall islet recovery in the TLM group was significantly higher than in the UW group. With 10 to 16 hours of cold storage, the success rates of islet isolations remained at 62% in the TLM group but decreased to 22% in the UW group. Transplanted islets in the TLM group worked well in the recipients. CONCLUSIONS: There are time limitations for using the UW solution for pancreas preservation before human islet isolation. The TLM is a potential method to prolong the optimal cold storage time for successful islet isolations.
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 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".