Inhibition of Transplant Vasculopathy in a Rat Aortic Allograft Model After Infusion of Anti-Inflammatory Viral Serpin
Bibliographic record
Abstract
BACKGROUND: Transplant vasculopathy remains a difficult therapeutic problem, resulting in the majority of late cardiac graft losses. This chronic vascular disease is thought to be triggered by alloantigen-dependent and alloantigen-independent inflammatory factors. Despite improved 1-year survival, the incidence of transplant vasculopathy has not improved with current immunosuppressive protocols. Highly effective strategies have evolved in the large DNA viruses that shield infecting viruses from host inflammatory responses. Serp-1 is a secreted myxoma virus anti-inflammatory serine proteinase inhibitor. Serp-1 inhibits plasminogen activators in a manner similar to plasminogen activator inhibitor (PAI-1), a vascular protein that plays a pivotal regulatory role in vascular wound healing. In this study, we tested the ability of purified Serp-1 protein to ameliorate posttransplant vasculopathy after rat aortic allograft surgery. METHODS AND RESULTS: Serp-1 protein or controls were infused into 98 rats immediately after segmental aortic allograft transplantation. After either late (28 days, 64 rats) or early (12 to 48 hours, 24 rats) follow-up, transplanted aortic segments were harvested for morphological and immunohistochemical analysis. Significant reductions in intimal plaque growth (P<0.002) and mononuclear cell invasion (P<0.033) were detected after Serp-1 infusion at nanogram doses. Serp-1 reduced early macrophage (P<0.0016) and nonspecific lymphocyte (P<0.0179) invasion into medial and adventitial layers and inhibited associated depletion of medial smooth muscle cells (P<0.0006). CONCLUSIONS: Infusion of a viral anti-inflammatory serpin, Serp-1, significantly reduces early inflammatory responses and later luminal occlusion in a rat aortic allograft model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".