Sequential Kidney/Islet Transplantation Using Prednisone-Free Immunosuppression
Bibliographic record
Abstract
Islet transplantation is becoming established as a treatment option for type I diabetes in select patients. Individuals with type I diabetes who have previously received a successful kidney allograft may be good candidates for islet transplantation. They have already assumed the risks of chronic immunosuppression, so the added procedural risk of a subsequent islet transplant would be minimal. Furthermore, because of the preimmunosuppressed state it is possible that islet-after-kidney transplantation may result in a more efficient early islet engraftment. Consequently, insulin independence might be achieved with significantly fewer islets than the approximately 8-10,000 islet equivalents/kg/b.w. currently required. A mass that usually demands two or more cadaveric donors. A case of successful islet-after-kidney transplantation is described using the steroid-free Edmonton immunosuppression protocol. Characteristics of the final islet product are: a) islet equivalents: 265,888 (4100 islet equivalents/kg/b.w.); b) islet purity: 75-80%; c) viability: >95% (trypan blue exclusion); and d) mean islet potency (static low-high glucose challenge): 4.16 +/- 1.91-fold increase. Post-transplant the patient's hypoglycemic episodes abated. Exogenous insulin requirements were eliminated at week 12 post-transplant as basal and Ensure (Abbott Laboratories, Abbott Park, IL, USA) oral glucose stimulated C-peptide levels peaked and stabilized. Twenty-four-hour continuous glucose monitoring confirmed moment-to-moment glycemic control, and periodic nonfasting finger stick glucose determinations over the next month confirmed glycemia was controlled. Hemoglobin A1c levels declined from a pretransplant level of 6.9% to 5.3%. Renal allograft function remained changed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".