Critical Role of Macrophage Migration Inhibitory Factor Activity in Experimental Autoimmune Diabetes
Bibliographic record
Abstract
Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine that plays a pivotal role in several immunoinflammatory and autoimmune diseases. In this study we examined the role of MIF in the development of immunoinflammatory diabetes induced in susceptible strains of mice by multiple low doses of streptozotocin. We found that MIF protein was significantly elevated in islet cells during the development of diabetes, and that targeting MIF activity with either neutralizing antibody or the pharmacological inhibitor (S,R)-3-(4-hydroxyphenyl)-4,5-dihydro-5-isoxazole acetic acid methyl ester, markedly reduced clinical and histopathological features of the disease, such as hyperglycemia and insulitis. Lymphocytes from mice treated with the MIF inhibitors exhibited reduction of both islet antigen-specific proliferative responses and adhesive cell-cell interactions. Neutralization of MIF also down-regulated the ex vivo secretion of the proinflammatory mediators, TNF-alpha, interferon-gamma, and nitric oxide, while augmenting that of the antiinflammatory cytokine, IL-10. This study provides the first in vivo evidence for a critical role for MIF in the immune-mediated beta-cell destruction in an animal model of human type 1 diabetes mellitus and identifies a new therapeutic strategy for the prevention and treatment of this disease in humans that is based on the selective inhibition of MIF activity.
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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 | 0.000 | 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".