Material‐induced tissue factor expression but not CD11b upregulation depends on the presence of platelets
Bibliographic record
Abstract
Biomaterials activate leukocytes as well as platelets when exposed to blood. One feature of leukocyte activation at least at times beyond a few hours is tissue factor expression, contributing to a procoagulant state. We show here that platelet activation and specifically platelet-monocyte aggregate formation appears to be a precondition for tissue factor expression. Material-induced Tissue Factor (TF) expression by isolated leukocytes (6 x 10(6) cells/mL) resuspended in increasing concentrations of platelets in plasma was elevated when the platelet concentration was 50 x 10(6) platelets/mL or more; at lower platelet concentrations (1-25 x 10(6). cells/mL) the TF expression remained at background levels. On the other hand, significant CD11b upregulation was observed on leukocytes, in bulk and adherent to beads, at all platelet concentrations. This platelet effect on material-induced TF expression appeared to be mediated by the formation of platelet-monocyte aggregates. Anti-P-selectin, which blocked the association between platelets and leukocytes, reduced monocyte adhesion and material-induced TF expression for bulk monocytes. Anti-GPIIb/IIIa, a GPIIb/IIIa platelet antagonist, also reduced monocyte adhesion and material-induced TF expression in the bulk, most likely due to its inhibiting effect on the formation of platelet-monocyte aggregates, secondary to platelet activation. However, the antibody-associated reductions for bulk leukocytes (mainly neutrophils) were small and incomplete. Similar levels of TF expression, in the bulk, were observed with both polystyrene (PS), a strong platelet activator, and polyethylene glycol-modified PEG (PS-PEG), a mild platelet activator. The role of platelets in material-induced TF expression appears to be mediated in part via the formation of platelet-monocyte aggregates, although other mechanisms are likely also involved.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".