Seven Consecutive Successful Clinical Islet Isolations with Pancreatic Ductal Injection
Bibliographic record
Abstract
Inconsistent islet isolation is one of the issues of clinical islet transplantation. In the current study, we applied ductal injection to improve the consistency of islet isolation. Seven islet isolations were performed with the ductal injection of ET-Kyoto solution (DI group) and eight islet isolations were performed without the ductal injection (standard group) using brain-dead donor pancreata. Isolated islets were evaluated based on the Edmonton protocol for transplantation. The DI group had significantly higher islet yields (588,566 +/- 64,319 vs. 354,836 +/- 89,649 IE, p < 0.01) and viability (97.3 +/- 1.2% vs. 92.6 +/- 1.2%, p < 0.02) compared with the standard group. All seven isolated islet preparations in the DI group (100%), versus only three out of eight isolated islet preparations (38%) in the standard group met transplantation criteria. The islets from the DI group were transplanted into three type 1 diabetic patients and all three patients became insulin independent. Ductal injection significantly improved quantity and quality of isolated islets and resulted in high success rate of clinical islet transplantation. This simple modification will reduce the risk of failure of clinical islet isolation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".