Induction of Indefinite Cardiac Allograft Survival Correlates With Toll-Like Receptor 2 and 4 Downregulation After Serine Protease Inhibitor-1 (Serp-1) Treatment
Bibliographic record
Abstract
BACKGROUND: Innate immunity provides obstacles to successful organ transplantation, which cannot be prevented by cyclosporine (CsA). Here we have determined the potential of a myxoma viral serpin, Serp-1, with proven anti-inflammatory and antiatherogenic actions, to modulate innate immunity and contribute synergistically with CsA in the prevention of acute cardiac allograft rejection. METHODS: Brown-Norway rat hearts were heterotopically transplanted into Lewis rats and given either a monotherapy treatment of Serp-1, a subtherapeutic dose of CsA, or the two drugs in combination. RESULTS: A brief treatment of Serp-1 alone, or a subtherapeutic dose of CsA, resulted in a marked decrease in intragraft macrophage infiltration and downregulation of toll-like receptor (TLR)-2, TLR4 and MyD88 at 48 hours posttransplantation, which was associated with significantly reduced numbers of mature dendritic cells. A significant reduction in intragraft T-lymphocyte infiltration was observed with both Serp-1 monotherapy and Serp-1 and CsA combination therapy, with the combination treatment achieving indefinite graft survival (>100 days) with normal histology. The CsA monotherapy group displayed partially reduced lymphocyte infiltration compared to the untreated controls, but failed to inhibit early innate immune graft recognition events such as macrophage infiltration and TLR 2, TLR4, and MyD88, and was ultimately unsuccessful in preventing rejection (36.3+/-7.8 days). CONCLUSION: Observed suppressive effects of Serp-1 on early innate immune response components such as TLR-2 and 4, and on adaptive responses such as T-cell intragraft infiltration suggests that Serp-1 may modulate the transition from innate to adaptive immunity, exhibiting a synergistic effect on allograft survival when used in combination with a subtherapeutic dose of CsA.
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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) | 0.000 | 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".