Islet Transplantation in Type 1 Diabetes Mellitus Using Cultured Islets and Steroid‐Free Immunosuppression: Miami Experience
Bibliographic record
Abstract
Following the success obtained with transplantation of fresh human islets under steroid-free immunosuppression, this trial evaluated the transplantation of islets that had undergone a period of in vitro culture and the potential of tumor necrosis factor (TNF-alpha) blockade to improve islet engraftment. Subjects included 16 patients with type 1 diabetes mellitus (T1DM); half were randomly assigned to receive Infliximab immediately preceding initial infusion. Immunosuppression consisted of daclizumab induction and sirolimus/tacrolimus maintenance. Out of 16 subjects 14 achieved insulin independence with one or two islet infusions; adverse events precluded completion in two. Without supplemental infusions, 11/14 (79%) subjects were insulin independent at 1 year, 6/14 (43%) at 18 months; these same subjects remain insulin independent at 33+/-6 months. While on immunosuppression, all patients maintained graft function. Out of 14 patients, 8 suffered chronic partial graft loss, likely immunological in nature, 5 of these received supplemental infusions. Currently, 11 subjects remain on immunosuppression, 8 (73%) are insulin independent, two with supplemental infusions. Insulin independent subjects demonstrated normalization of HbA1c, fructosamine and Mean Amplitude of Glycemic Excursions (MAGE) values. No clinical benefit of infliximab was identified. These results demonstrate that transplantation of cultured human islet allografts results in reproducible insulin independence in all subjects under this immunosuppressive regimen, comparable to that of freshly transplanted islets (Edmonton protocol).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".