Prospective and Challenges of Islet Transplantation for the Therapy of Autoimmune Diabetes
Bibliographic record
Abstract
Pancreatic islet cell transplantation is an attractive treatment of type 1 diabetes (T1D). The success enhanced by the Edmonton protocol has fostered phenomenal progress in the field of clinical islet transplantation in the past 5 years, with 1-year rates of insulin independence after transplantation near 80%. Long-term function of the transplanted islets, however, even under the Edmonton protocol, seems difficult to accomplish, with only 10% of patients maintaining insulin independence 5 years after transplantation. These results differ from the higher metabolic performance achieved by whole pancreas allotransplantation, and autologous islet cell transplantation, and form the basis for a limited applicability of islet allografts to selected adult patients. Candidate problems in islet allotransplantation deal with alloimmunity, autoimmunity, and the need for larger islet cell masses. Employment of animal islets and stem cells, as alternative sources of insulin production, will be considered to face the problem of human tissue shortage. Emerging evidence of the ability to reestablish endogenous insulin production in the pancreas even after the diabetic damage occurs envisions the exogenous supplementation of islets to patients also as a temporary therapeutic aid, useful to buy time toward a possible self-healing process of the pancreatic islets. All together, islet cell transplantation is moving forward.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".