Inducing Specific Immune Tolerance to Prevent Type 1 Diabetes in NOD Mice
Bibliographic record
Abstract
OBJECTIVES: Proinsulin is the first autoantigen in type 1 diabetes (T1D). We reasoned that coupling hematopoietic stem cells (HSCs) transplantation with ex vivo transduction of syngeneic HSCs with lentiviral vectors to express proinsulin II could prevent T1D in nonobese diabetic (NOD) mice. METHODS: Hematopoietic stem cells were isolated from 6- to 8-week-old NOD female mice and transduced in vitro with lentiviral vectors encoding proinsulin II. Preconditioned 3- to 4-week-old female NOD mice were transplanted with transduced or nontransduced HSCs and compared with age-matched unmanipulated control. The insulitis, T1D development, and immune reconstitution were assessed. RESULTS: The mean (SD) insulitis score was significantly reduced (1.156 [0.575] vs 2.156 [0.892] or 3.043 [0.728], P = 0.009 or <0.001), and diabetes was nearly completely prevented (1/13 vs 5/12 or 4/9, P = 0.031 or 0.013) in recipients of transduced HSCs expressing proinsulin II as compared with recipients of nontransduced HSCs or unmanipulated control. Sialitis, reconstitution of peripheral blood leukocytes, and in vitro recall responses to ovalbumin were not different between 3 groups of mice. CONCLUSIONS: Syngeneic transplantation of HSCs transduced ex vivo with lentiviral vectors to encode proinsulin II is a novel strategy to prevent T1D.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".