TECHNICAL IMPROVEMENT OF HUMAN PANCREATIC ISLET ISOLATION
Bibliographic record
Abstract
P437 Aims: Despite the breakthrough introduced by ‘Edmonton protocol’, islet transplantation still has a major drawback, i.e., in most cases islets from one donor are insufficient to cure diabetic patients. A key factor for successful islet isolation is to place the optimal amount of enzyme into the pancreatic ducts prior to starting digestion of pancreatic glands. To improve this procedure, we introduced novel techniques to identify and repair tissue damages resulting in leakage of collagenase solution. Furthermore, 3 dissection procedures were also developed and compared to achieve complete digestion of glands. Methods: One hundred twelve standardized consecutive islet isolations were evaluated. Dye solutions (Metyltioninklorid®) and tissue glue (Indermil®) were applied to detect leakage and to repair the damaged parenchyma. The effects of dye and glue were evaluated in terms of islet yield, islet function using the perifusion assay, and the possibility to use the islet preparation for clinical transplantation. One group of pancreata (n=26) were obtained en bloc together with duodenum and carefully detached with ligation of accessory ducts in isolation unit (Whole pancreas with duodenum: WPD group), whereas the pancreata were dissected from the duodenum in the operation room in other 86 isolations. In 28 out of these 86 isolations, the whole glands were used (Whole pancreas: WP group), while only the body and tail area were applied in remaining 58 isolations (Partial pancreas: PP group). Results: Both dye and glue were effective to prevent leakage of collagenase from the gland. Despite having been applied to only damaged pancreata, both islet yield and success rate were higher when these tools were used (Dye: IEQs/g; 4,207±451 vs. 3,469±342 (p=0.02), success rate; 34.8% vs. 23.6% (p=0.27), Glue: IEQs/g; 4,479±809 vs. 3,579±299 (p=0.14), success rate; 50.0% vs. 22.3% (p=0.18)). No adverse effects on islet function or collagenase activity were observed. The success rate of isolations and islet yield were significantly higher in the WPD group (p=0.02 and 0.003, respectively). When WPD group was combined with the use of dye and glue, the success rate significantly increased (50.0% vs. 21.3%, p=0.02). Conclusions: Dye and glue could be applied to human islet isolation as useful tools to improve the islet isolation procedure. In addition, the use of the whole pancreas further improve the outcome.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".