Flow cytometric evaluation of material-induced platelet and complement activation
Bibliographic record
Abstract
Flow cytometry is used to characterize the activation state of platelets and leukocytes within whole blood after contact for 4 h at 37 degrees C with various materials under conditions of low shear. The contact involved adding heparinized whole blood to small diameter tubes that were connected to two arms extending from a rocking platform. For all surfaces (polyethylene, polypropylene, Silastic, PVA hydrogel) tested there was strong evidence of platelet activation in the bulk blood: platelet-derived microparticles. P-selectin expression and platelet-leukocyte aggregates. Only contact with PVA hydrogel surfaces led to dramatic increases in CD11b up-regulation on monocytes and neutrophils that was inhibited by complement inhibition (sCRI). Flow cytometry was also used to evaluate the effectiveness of various agents to inhibit material-induced complement activation. The assay involved incubating 10 microm polystyrene beads for 1 h with serum at 37 degrees C before isolating the beads so as to label them with a monoclonal antibody against a neoantigen on SC5b-9. The beads were then identified by flow cytometry and the fluorescence associated with their SC5b-9 level recorded. The ability of C1-INH, pentamidine and benzamidine to moderately inhibit SC5b-9 levels suggests a role for classical complement activation in material-induced complement activation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".