Recovery from diabetes in mice by β cell regeneration
Bibliographic record
Abstract
The mechanisms that regulate pancreatic beta cell mass are poorly understood. While autoimmune and pharmacological destruction of insulin-producing beta cells is often irreversible, adult beta cell mass does fluctuate in response to physiological cues including pregnancy and insulin resistance. This plasticity points to the possibility of harnessing the regenerative capacity of the beta cell to treat diabetes. We developed a transgenic mouse model to study the dynamics of beta cell regeneration from a diabetic state. Following doxycycline administration, transgenic mice expressed diphtheria toxin in beta cells, resulting in apoptosis of 70%-80% of beta cells, destruction of islet architecture, and diabetes. Withdrawal of doxycycline resulted in a spontaneous normalization of blood glucose levels and islet architecture and a significant regeneration of beta cell mass with no apparent toxicity of transient hyperglycemia. Lineage tracing analysis indicated that enhanced proliferation of surviving beta cells played the major role in regeneration. Surprisingly, treatment with Sirolimus and Tacrolimus, immunosuppressants used in the Edmonton protocol for human islet transplantation, inhibited beta cell regeneration and prevented the normalization of glucose homeostasis. These results suggest that regenerative therapy for type 1 diabetes may be achieved if autoimmunity is halted using regeneration-compatible drugs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".