Adenosine receptor activation ameliorates type 1 diabetes
Bibliographic record
Abstract
Growing evidence indicates that adenosine receptors could be promising therapeutic targets in autoimmune diseases. Here we studied the role of adenosine receptors in controlling the course of type 1 diabetes. Diabetes in CD-1 mice was induced by multiple-low-dose-streptozotocin (MLDS) treatment and in nonobese diabetic (NOD) mice by cyclophosphamide injection. The nonselective adenosine receptor agonist 5'-N-ethylcarboxamidoadenosine (NECA) prevented diabetes development in both MLDS-challenged mice and in cyclophosphamide-treated NOD mice. The effect of NECA was reversed by the selective A2B receptor antagonist N-(4-cyanophenyl)-2-[4-(2,3,6,7-tetrahydro-2,6-dioxo-1,3-dipropyl-1H-purin-8-yl)phenoxy]acetamide (MRS 1754). The selective A1 receptor agonist 2-chloro-N6-cyclopentyladenosine (CCPA) and A3 receptor agonist N6-(3-iodobenzyl)-adenosine-5'-N-methyluronamide (IB-MECA) were less efficacious in ameliorating the course of diabetes. NECA inhibited diabetes in A2A receptor KO mice and the selective A2A receptor agonist 2-p-(2-carboxyethyl)phenethyl-amino-5'-N-ethyl-carboxamidoadenosine (CGS21680) had no effect in normal mice, indicating a lack of role of A2A receptors. NECA failed to prevent cytokine-induced beta-cell death in vitro, but NECA strongly suppressed expression of the proinflammatory cytokines TNF-alpha, MIP-1alpha, IL-12, and IFN-gamma in pancreata, endotoxin, or anti-CD3-stimulated splenic cells, and T helper 1 lymphocytes, indicating that the beneficial effect of NECA was due to immunomodulation. These results demonstrate that adenosine receptor ligands are potential candidates for the treatment of type 1 diabetes.
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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) | 0.003 | 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".