Allograft islets transplantation for type 1 diabetes mellitus and 3-year follow-up: 10 cases reportr
Bibliographic record
Abstract
Objective To evaluate the 3-year follow-up outcomes of allograft islet transplantation for type 1 diabetes mellitus (T1DM). Method Ten cases of T1DM were subjected to islet transplantations. The pancreases were digested by Liberase collagenase enzyme and islets with high activity were purified using continuous gradients of Ficoll-diatrizoic acid on a refrigerated COBE 2991 centrifuge. Cultured islets were infused by minimally invasive surgical approach to the liver via portal vasculature. After islet cell transplantation, a modified Edmonton immunosuppresion protocol containing antithymocyte globulin (ATG) and Etanercept, tacrolimus and mycophenolate mofetil was used, and the changes in blood glucose, C peptide and glycate hemoglobin were monitored regularly during a follow-up period of 3 years. Result The glucocorticoid-free immunosuppressive regimen was used. During the follow-up period of 36 months, 6 recipients remained insulin-independent. The dosage of insulin decreased by 60% in 4 patients. The levels of blood glucose and HbA1c were all within normal range and liver and renal functions were normal. C-peptide level was normal. No complications related to islet infusion were observed. Conclusion Medium-term clinical effect of islet transplantation is effective and safe for treating T1DM. Key words: Pancreatic islet transplantation; Diabetes Mellitus; Immunosuppression
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".