Strategies toward single-donor islets of Langerhans transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: The current review addresses a critical need in clinical islet transplantation, namely the routine transition from the requirement of two to four donors down to one donor per recipient. The ability to achieve single-donor islet transplantation will provide many more islet grafts for treatment of an ever-expanding patient base with type 1 diabetes (T1DM) with poor glycemic control. Avoiding exposure of recipients to multiple different donor human leukocyte associated (HLA) antigens is critical if risk of donor sensitization is to be avoided. This point is important as further islet or pancreas transplants in the remote future or the potential future need for a solid organ kidney transplant may become prohibitive if the recipient is sensitized. RECENT FINDINGS: This review addresses systematically all areas that contribute to the success or failure of single-donor islet engraftment, beginning with donor-related factors, optimizing islet isolation and culture conditions, and describes a series of strategies in the treatment of the recipient to prevent inflammation, apoptosis, islet thrombosis, and improve metabolic functional outcome, all of which will lead to improved single-donor engraftment success. SUMMARY: If single-donor islet transplantation can be achieved routinely, therapy will become more widely available, more accepted by the transplant community (currently pancreas transplantation requires only a single donor), and this situation will have a major impact overall as an effective treatment option in T1DM.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".