Platelet‐enhanced plasma: Characterization of a novel candidate resuscitation fluid's extracellular vesicle content, clotting parameters, and thrombin generation capacity
Bibliographic record
Abstract
BACKGROUND: Platelet transfusion is challenging in emergency medicine because of short platelet shelf life and stringent storage conditions. Platelet-derived extracellular vesicles (PEV) exhibit platelet-like properties. A plasma generated from expired platelet units rich in procoagulant PEV may be able to combine the benefits of plasma and platelets for resuscitation while increasing shelf life and utilizing an otherwise wasted resource. STUDY DESIGN AND METHODS: Freeze-thaw cycling of platelet-rich plasma (PRP) followed by centrifugation to remove platelet remnants was utilized to generate platelet-enhanced plasma (PEP). An in vitro model of dilutional coagulopathy was also designed and used to test PEP. Rotational thromboelastometry and calibrated automated thrombography were used to assess clotting and extracellular vesicles (EV) procoagulant activity. Capture arrays were used to specifically measure EV subpopulations of interest (ExoView™, NanoView Biosciences). Captured vesicles were quantified and labeled with Annexin-V-FITC, CD41-PE, and CD63-AF647. Platelet alpha granule content (platelet-derived growth factor AB, soluble P-selectin, vascular endothelial growth factor A, and neutrophil activating peptide 2-chemokine (C-X-C motif) ligand 7) was measured. Commercially available platelet lysates were also characterized. RESULTS: CD41+PS+ EV/ml in platelet-free plasma (p = .0079). Commercial lysates show impaired but rescuable clotting. DISCUSSION: PEP is a unique candidate resuscitation fluid containing high PEV concentration with preliminary evidence, indicating a potential for upscaling the approach using platelet concentrates. Commercial lysate manufacturer workflows may be suitable for this, but further optimization and characterization of PEP is required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".