Neonatal beta-cell apoptosis: a trigger for autoimmune diabetes?
Bibliographic record
Abstract
In neonatal rodents, the beta-cell mass undergoes a phase of remodeling that includes a wave of apoptosis. Using both mathematical modeling and histochemical detection methods, we have demonstrated that beta-cell apoptosis is significantly increased in neonates as compared with adult rats, peaking at approximately 2 weeks of age. Other tissues, including the kidney and nervous system, also exhibit neonatal waves of apoptosis, suggesting that this is a normal developmental phenomenon. We have demonstrated that increased neonatal beta-cell apoptosis is also present in animal models of autoimmune diabetes, including both the BB rat and NOD mouse. Traditionally, apoptosis has been considered a process that does not induce an immune response. However, recent studies indicate that apoptotic cells can do the following: 1) display autoreactive antigen in their surface blebs; 2) preferentially activate dendritic cells capable of priming tissue-specific cytotoxic T-cells; and 3) induce the formation of autoantibodies. These findings suggest that in some circumstances physiological apoptosis may, in fact, initiate autoimmunity. Initiation of beta-cell-directed autoimmunity in murine models appears to be fixed at approximately 15 days of age, even when diabetes onset is dramatically accelerated. Taken together, these observations have led us to hypothesize that the neonatal wave of beta-cell apoptosis is a trigger for beta-cell-directed autoimmunity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".