Virus-derived serpin reduces immuno-coagulopathic damage in murine colitis by targeting the urokinase-type plasminogen activator receptor (uPAR) and complement
Bibliographic record
Abstract
A virus-derived serpin, Serp-1, has proven efficacy in treating inflammatory and coagulation disorders in preclinical and clinical studies. Serp-1 evolved over millions of years to block host immune responses, targeting serine proteases in immune and coagulation pathways. Treatment with PEGylated Serp-1 (PEGSerp-1) protein reduced lung injury in both lupus lung hemorrhage and SARS-CoV-2 models. Here, PEGSerp-1 effects on immune-coagulopathic responses is examined in a mouse colitis model. Inflammatory bowel disease (IBD) is associated with life-threatening complications with severe inflammation, bleeding, vasculitis, cancer and toxic megacolon. Serine protease cascades activate coagulation and complement pathways throughout the human body and are regulated by inhibitors, termed serpins, that can reduce gut inflammation. Prophylactic PEGSerp-1 significantly improved survival in severe 5% Dextran sodium sulfate (DSS) colitis, reducing inflammation and crypt damage. Colon damage and inflammation were also reduced after either acute colitis induced by 5% DSS or repeat 2% DSS induced colitis. PEGSerp-1 reduced inflammatory M1 macrophage invasion, urokinase-type plasminogen activator receptor (uPAR), fibrinogen and complement on immunohistochemical analysis. PEGSerp-1 reduced uPAR expression in human macrophage, but not colon cells. Here we report analysis of PEGSerp-1 as a tissue and macrophage targeting therapeutic for colitis, reducing immune and coagulation induced damage in the colon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".