β Cell transplantation and immunosuppression: can’t live with it, can’t live without it
Bibliographic record
Abstract
Since the first diabetic was treated with insulin in 1922, millions of patients have relied on frequent insulin injections and glucose monitoring to combat the disease and its complications. Improved immunosuppressive regimens in islet transplantation developed in the Edmonton protocol raised the hopes of diabetics worldwide for a complete cure and insulin independence. However, transplant success has proven to be short-lived and accompanied by significant side effects. Using a clever genetic model for conditional ablation of pancreatic beta cells in vivo, Nir and colleagues show in this issue of the JCI that the immunosuppressant drugs clinically inhibit beta cell proliferation in the diabetic setting (see the related article beginning on page 2553). They also demonstrate that beta cells have a remarkable regenerative capacity and that normal beta cell mass can recover even in the setting of hyperglycemia. Their new mouse model should aid in the development of improved immunoregulatory strategies and in the elucidation of the molecular pathways that govern beta cell regeneration.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".