Priming and effector dependence on insulin B:9–23 peptide in NOD islet autoimmunity
Bibliographic record
Abstract
NOD mice with knockout of both native insulin genes and a mutated proinsulin transgene, alanine at position B16 in preproinsulin (B16:A-dKO mice), do not develop diabetes. Transplantation of NOD islets, but not bone marrow, expressing native insulin sequences (tyrosine at position B16) into B16:A-dKO mice rapidly restored development of insulin autoantibodies (IAAs) and insulitis, despite the recipients' pancreatic islets lacking native insulin sequences. Splenocytes from B16:A-dKO mice that received native insulin-positive islets induced diabetes when transferred into wild-type NOD/SCID or B16:A-dKO NOD/SCID mice. Splenocytes from mice immunized with native insulin B chain amino acids 9-23 (insulin B:9-23) peptide in CFA induced rapid diabetes upon transfer only in recipients expressing the native insulin B:9-23 sequence in their pancreata. Additionally, CD4(+) T cells from B16:A-dKO mice immunized with native insulin B:9-23 peptide promoted IAAs in NOD/SCID mice. These results indicate that the provision of native insulin B:9-23 sequences is sufficient to prime anti-insulin autoimmunity and that subsequent transfer of diabetes following peptide immunization requires native insulin B:9-23 expression in islets. Our findings demonstrate dependence on B16 alanine versus tyrosine of insulin B:9-23 for both the initial priming and the effector phase of NOD anti-islet 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".