Bibliographic record
Abstract
Serine protease inhibitors, termed serpins, are key regulators of numerous biological pathways that initiate inflammation, coagulation, angiogenesis, apoptosis, extra-cellular matrix composition and complement activation responses. Viruses have encoded serpins to guard themselves from host immune attack. The myxoma virus which infects rabbits secretes a highly potent anti-inflammatory serpin, Serp-1, which targets thrombolytic and thrombotic proteases as a means to fend off coagulation and inflammatory reactions to viral infection. These reactions act as a defense, produced by the host, to counter viral infection and invasion. When infused in animals after vascular injury, Serp-1 elicits exceptional anti-inflammatory activity, whereas the mammalian serpin, plasminogen activator inhibitor-1 (PAI-1), which also targets thrombotic and thrombolytic proteases can induce a pro-thrombotic response. During arterial injury, PAI-1 is highly expressed and increased PAI-1 concentration can result in acute thrombosis after aortic transplant in mouse models. The reactive center loop amino acid sequence is a fingerprint for serpin function and this function is highly sequence specific such that modification in this sequence can markedly alter activity. For instance, the alteration of the serpin reactive site loop P1-P1I amino acid sequence nullified the anti-inflammatory activity of Serp-1 and modification of P2-P7 initiated a pro-inflammatory response with vascular remodeling with aneurysm formation. Furthermore Serp-1 has demonstrated the capacity to utilize a mammalian serine protease receptor, the urokinase-type plasminogen activator receptor (uPAR), to alter cellular signaling in part through the actin binding protein cytoskeletal system (via filamin B). In this review, the molecular mechanisms relating inflammation and coagulation pathways to atherosclerosis and how the viral serpin, Serp-1, modifies these pathways in order to exhibit this profound anti-inflammatory activity without associated adverse thrombosis are discussed. Viral and vascular serpins targeting the thrombolytic cascade represent a potential new and untapped therapeutic resource.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".