Endothelial anticoagulant heparan sulfate mediates anti‐inflammatory effects of antithrombin
Bibliographic record
Abstract
Anticoagulant heparan sulfate (HS AT+ ) is thought to catalyze antithrombin's (AT) anticoagulant activity; however, HS AT+ deficient mice ( Hs3st1 −/− ) show normal hemostasis. Perhaps endothelial HS AT+ mediates anti‐inflammatory effects of AT. We examined leukocyte‐endothelial interactions (LEIs) by intravital microscopy of cremaster muscle venules. Human AT, or vehicle, was given 2 h before exteriorization of the cremaster muscle, an intrascrotal injection of TNFα, or an intraperitoneal injection of lipopolysaccharide (LPS). In Hs3st1 +/+ mice, AT reduced select LEIs induced by cremaster exteriorization and LPS, but enhanced TNFα induced LEIs. LPS treated Hs3st1 −/− , vs. Hs3st1 +/+ , mice trended towards enhanced mortality (P=0.17) and select LEIs were enhanced (P<0.01). Most importantly, AT treatment produced opposite effects on LPS induced leukocyte adhesion efficiency; Hs3st1 +/+ mice exhibited a 7‐fold reduction (P<0.001) but Hs3st1 −/− mice had a 3.7‐fold elevation (P<0.02). In Hs3st1 −/− mice, HS AT+ levels were normal on leukocytes but undetectable on cremaster endothelial cells. We conclude that AT's inhibition of LEIs is context dependent, likely involving a balance of pro‐ and anti‐inflammatory activities. Endothelial HS AT+ mediates AT anti‐inflammatory activity. Supported in part by Bayer‐CIHR‐CBS‐HQ Partnership Fund to PLG; NIH R01 HL079104 , and AHA GIA 0250613N to NWS.
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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".