Exendin-4 Improves Reversal of Diabetes in NOD Mice Treated with Anti-CD3 Monoclonal Antibody by Enhancing Recovery of β-Cells
Bibliographic record
Abstract
Immune modulators can arrest loss of insulin secretion in type 1 diabetes mellitus (T1DM), but they have not caused permanent disease remission or restored normal insulin secretion. We tested whether exendin-4, a glucagon-like peptide-1 receptor agonist, would enhance remission of T1DM in NOD mice treated with anti-CD3 monoclonal antibody (mAb) and studied the effects of exendin-4 treatment on cellular and metabolic responses of beta-cells. Diabetic NOD mice treated with anti-CD3 mAb and exendin-4 had a higher rate of remission (44%) than mice treated with anti-CD3 mAb alone (37%) or exendin-4 (0%) or insulin or IgG alone (0%) (P < 0.01). The effect of exendin-4 on reversal of diabetes after anti-CD3 mAb was greatest in mice with a glucose level of less than 350 mg/dl at diagnosis (63 vs. 39%, P < 0.05). Exendin-4 did not affect beta-cell area, replication, or apoptosis or reduce the frequency of diabetogenic or regulatory T cells or modulate the antigenicity of islet cells. Reversal of T1DM with anti-CD3 mAb was associated with recovery of insulin in glucose transporter-2(+)/insulin(-) islet cells that were identified at diagnosis. Glucose tolerance and insulin responses improved in mice treated with combination therapy, and exendin-4 increased insulin content and insulin release from beta-cells. We conclude that treatment with glucagon-like peptide-1 receptor agonist enhances remission of T1DM in NOD mice treated with anti-CD3 mAb by enhancing the recovery of the residual islets. This combinatorial approach may be useful in treatment of patients with new-onset T1DM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".