The Role of Macrophage Migration Inhibitory Factor in Mouse Islet Transplantation
Bibliographic record
Abstract
BACKGROUND: Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine produced by many tissues including pancreatic beta-cells. METHODS: This study investigates the impact of MIF on islet transplantation using MIF knock-out (MIFko) mice. RESULTS: Early islet function, assessed with a syngeneic marginal islet mass transplant model, was enhanced when using MIFko islets (P<0.05 compared with wild-type [WT] controls). This result was supported by increased in vitro resistance of MIFko islets to apoptosis (terminal deoxynucleotide tranferase-mediated dUTP nick-end labeling assay), and by improved glucose metabolism (lower blood glucose levels, reduced glucose areas under curve and higher insulin release during intraperitoneal glucose challenges, and in vitro in the absence of MIF, P<0.01). The beneficial impact of MIFko islets was insufficient to delay allogeneic islet rejection. However, the rejection of WT islet allografts was marginally delayed in MIFko recipients by 6 days when compared with WT recipient (P<0.05). This effect is supported by the lower activity of MIF-deficient macrophages, assessed in vitro and in vivo by cotransplantation of islet/macrophages. Leukocyte infiltration of the graft and donor-specific lymphocyte activity (mixed lymphocyte reaction, interferon gamma ELISPOT) were similar in both groups. CONCLUSION: These data indicate that targeting MIF has the potential to improve early function after syngeneic islet transplantation, but has only a marginal impact on allogeneic rejection.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".