Modulation of Monocyte Function by Activated Protein C, a Natural Anticoagulant
Bibliographic record
Abstract
Activated protein C is the first effective biological therapy for the treatment of severe sepsis. Although activated protein C is well established as a physiological anticoagulant, emerging data suggest that it also exerts anti-inflammatory and antiapoptotic effects. In this study, we investigated the ability of activated protein C to modulate monocyte apoptosis, inflammation, phagocytosis, and adhesion. Using the immortalized human monocytic cell line THP-1, we demonstrated that activated protein C inhibited camptothecin-induced apoptosis in a dose-dependent manner. The antiapoptotic effect of activated protein C requires its serine protease domain and is dependent on the endothelial cell protein C receptor and protease-activated receptor-1. In primary blood monocytes from healthy individuals, activated protein C inhibited spontaneous apoptosis. With respect to inflammation, activated protein C inhibited the production of TNF, IL-1beta, IL-6, and IL-8 by LPS-stimulated THP-1 cells. Activated protein C did not influence the phagocytic internalization of Gram-negative and Gram-positive bioparticles by THP-1 cells or by primary blood monocytes. Activated protein C also did not affect the expression of adhesion molecules by LPS-stimulated blood monocytes nor the ability of monocytes to adhere to LPS-stimulated endothelial cells. We hypothesize that the protective effect of activated protein C in sepsis reflects, in part, its ability to prolong monocyte survival in a manner that selectively inhibits inflammatory cytokine production while maintaining phagocytosis and adherence capabilities, thereby promoting antimicrobial properties while limiting tissue damage.
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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) | 0.000 | 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".