Inosine Protects Against the Development of Diabetes in Multiple-Low-Dose Streptozotocin and Nonobese Diabetic Mouse Models of Type 1 Diabetes
Bibliographic record
Abstract
Inosine, a naturally occurring purine, was long considered to be an inactive metabolite of adenosine. However, recently inosine has been shown to be an immunomodulator and anti-inflammatory agent. The aim of this study was to determine whether inosine influences anti-inflammatory effects and affects the development of type 1 diabetes in murine models. Type 1 diabetes was induced either chemically by streptozotocin or genetically using the nonobese diabetic mouse (NOD) model. Mice were treated with inosine (100 or 200 mg kg(-1)d(-1)d) and diabetes incidence was monitored. The effect of inosine on pancreas immune cell infiltration, oxidative stress, and cytokine profile also was determined. For the transplantation model islets were placed under the renal capsule of NOD mice and inosine (200 mg kg(-1)d d(-1)d) treatment started the day of islet transplantation. Graft rejection was diagnosed by return of hyperglycemia accompanied by glucosuria and ketonuria. Inosine reduced the incidence of diabetes in both streptozotocin-induced diabetes and spontaneous diabetes in NOD mice. Inosine decreased pancreatic leukocyte infiltration and oxidative stress in addition to switching the cytokine profile from a Th1 to a Th2 profile. Inosine prolonged pancreatic islet graft survival, increased the number of surviving beta cells, and reduced the number of infiltrating leukocytes. Inosine protects against both the development of diabetes and against the rejection of transplanted islets. The purine exerts anti-inflammatory effects in the pancreas, which is its likely mode of action. The use of inosine should be considered as a potential preventative therapy in humans susceptible to developing Type 1 diabetes and as a possible antirejection therapy for islet transplant recipients.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".