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 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 | 0.000 | 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 | 0.000 |
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 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".