Activated Protein C Up-Regulates IL-10 and Inhibits Tissue Factor in Blood Monocytes
Bibliographic record
Abstract
The protective effect of recombinant activated protein C therapy in patients with severe sepsis likely reflects the ability of recombinant activated protein C to modulate multiple pathways implicated in sepsis pathophysiology. In this study, we examined the effects of recombinant activated protein C on the anti-inflammatory cytokine IL-10 and on the procoagulant molecule tissue factor (TF) in LPS-challenged blood monocytes. Treatment of LPS-stimulated monocytes with recombinant activated protein C resulted in an up-regulation of IL-10 protein production and mRNA synthesis. The up-regulation of IL-10 required the serine protease activity of recombinant activated protein C and was dependent on protease-activated receptor-1, but was independent of the endothelial protein C receptor. At the intracellular level, p38 MAPK activation was required for recombinant activated protein C-mediated up-regulation of IL-10. We further observed that incubation of LPS-stimulated monocytes with recombinant activated protein C down-regulated TF Ag and activity levels. This anticoagulant effect of recombinant activated protein C was dependent on IL-10 since neutralization of endogenously produced IL-10 abrogated the effect. In patients with severe sepsis, plasma IL-10 levels were markedly higher in those treated with recombinant activated protein C than in those who did not receive recombinant activated protein C. This study reveals novel regulatory functions of recombinant activated protein C, specifically the up-regulation of IL-10 and the inhibition of TF activity in monocytes. Our data further suggest that these activities of recombinant activated protein C are directly linked: the recombinant activated protein C-mediated up-regulation of IL-10 reduces TF in circulating monocytes.
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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".