Microparticle content of platelet concentrates is predicted by donor microparticles and is altered by production methods and stress
Bibliographic record
Abstract
In circulation, shedding of microparticles from a variety of viable cells can be triggered by pathological activation of inflammatory processes, by activation of coagulation or complement systems, or by physical stress. Elevated microparticle content (MPC) in donor blood might therefore indicate a clinical condition of the donor which, upon transfusion, might affect the recipient. In blood products, elevated MPC might also represent product stress. Surprisingly, the MPC in blood collected from normal blood donors is highly variable, which raises the question whether donor microparticles are present in-vivo and transfer into the final blood component, and how production methods and post-production processing might affect the MPC. We measured MPC using ThromboLUX in (a) platelet-rich plasma (PRP) of 54 apheresis donors and the corresponding apheresis products, (b) 651 apheresis and 646 pooled platelet concentrates (PCs) with plasma and 414 apheresis PCs in platelet additive solution (PAS), and (c) apheresis PCs before and after transportation, gamma irradiation, and pathogen inactivation (N = 8, 7, and 12 respectively). ThromboLUX-measured MPC in donor PRP and their corresponding apheresis PC samples were highly correlated (r = 0.82, P = .001). The average MPC in pooled PC was slightly lower than that in apheresis PC and substantially lower in apheresis PC stored with PAS rather than plasma. Mirasol Pathogen Reduction treatment significantly increased MPC with age. Thus, MPC measured in donor samples might be a useful predictor of product stability, especially if post-production processes are necessary.
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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 | 0.001 |
| 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) | 0.001 | 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".