Neuroserpin, a thrombolytic serine protease inhibitor (serpin), blocks transplant vasculopathy with associated modification of T-helper cell subsets
Bibliographic record
Abstract
Thrombolytic serine proteases not only initiate fibrinolysis, but also are up-regulated in vascular disease and acute inflammatory responses. Although the serine protease inhibitor (serpin) plasminogen activator inhibitor-1 (PAI-1) is considered a main regulator of thrombolysis, PAI-1 is also associated with vascular inflammation. The role of other serpins that target thrombolytic proteases, PAI-2, PAI-3, and neuroserpin (NSP), in vascular inflammation is, however, less well defined. NSP is a mammalian serpin that, similar to PAI-1, inhibits urokinase- and tissue-type plasminogen activators (uPA and tPA, respectively) and has been most closely associated with the nervous system, with a demonstrated protective role after cerebral infarction in mouse models. However, the role of NSP in systemic arterial inflammation and plaque growth is not known. Serp-1 is a myxoma viral serpin that also inhibits tPA and uPA, as well as additionally inhibiting plasmin and factor Xa (fXa). Serp-1 has proven highly potent anti-inflammatory and anti-atherogenic activity. Here we assess the effects of NSP treatment on plaque growth and T-helper (Th) lymphocyte activity in a mouse aortic allograft transplant model, with comparison to Serp-1. NSP and Serp-1 both significantly reduced plaque growth and T-cell invasion. T-bet (a Th1 differentiation marker) was significantly reduced in transplanted aorta with associated reductions in Th1 and Th17, but not Th2, in splenocytes. NSP had additional Th modifying activity in non-transplanted mice. In summary, this is the first report that NSP possesses anti-inflammatory activity in systemic arteries, modifying Th cell responses and significantly reducing plaque growth in mouse aortic allografts.
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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".