The Lectin-like Domain of Thrombomodulin Confers Protection from Neutrophil-mediated Tissue Damage by Suppressing Adhesion Molecule Expression via Nuclear Factor κB and Mitogen-activated Protein Kinase Pathways
Bibliographic record
Abstract
Thrombomodulin (TM) is a vascular endothelial cell (EC) receptor that is a cofactor for thrombin-mediated activation of the anticoagulant protein C. The extracellular NH(2)-terminal domain of TM has homology to C-type lectins that are involved in immune regulation. Using transgenic mice that lack this structure (TM(LeD/LeD)), we show that the lectin-like domain of TM interferes with polymorphonuclear leukocyte (PMN) adhesion to ECs by intercellular adhesion molecule 1-dependent and -independent pathways through the suppression of extracellular signal-regulated kinase (ERK)(1/2) activation. TM(LeD/LeD) mice have reduced survival after endotoxin exposure, accumulate more PMNs in their lungs, and develop larger infarcts after myocardial ischemia/reperfusion. The recombinant lectin-like domain of TM suppresses PMN adhesion to ECs, diminishes cytokine-induced increase in nuclear factor kappaB and activation of ERK(1/2), and rescues ECs from serum starvation, findings that may explain why plasma levels of soluble TM are inversely correlated with cardiovascular disease. These data suggest that TM has antiinflammatory properties in addition to its role in coagulation and fibrinolysis.
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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 | 0.000 |
| 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".