Platelet vesicles are potent inflammatory mediators in red blood cell products and washing reduces the inflammatory phenotype
Bibliographic record
Abstract
BACKGROUND: Studies suggest that washing red cell concentrates (RCCs) to remove soluble mediators and/or inflammatory components, such as extracellular vesicles (EVs), may lead to better clinical outcomes. This study tested the hypothesis that non-red blood cell (RBC) generated vesicles in RCC are potent inflammatory mediators in vitro and washing RCCs can reduce these vesicles and subsequently decrease the inflammatory activity of RCCs. STUDY DESIGN AND METHODS: Sixteen RCCs were pooled and split into four groups based on pre-wash storage time (Day 2 or 14; n = 4/group). Each group was tested 24 hours and 7 days post-wash. Characteristics of RBCs and EVs, cytokines released by monocytes, and expression of human umbilical vein endothelial cells (HUVECs) adhesion molecules were assessed. RESULTS: All RCCs meet quality standards for hemolysis, hematocrit, and hemoglobin. Washing did not remove residual platelets from RCCs but led to a significant reduction in platelet-EV count regardless of the group. Supernatant of RCCs washed on Day 14 and stored for 24 hours had significantly lower concentrations of RBC-EVs and white blood cell EVs compared to unwashed controls. Supernatant of unwashed RCCs showed higher production of inflammatory cytokines/chemokines MCP-1, IL-8, and TNF-α, and heightened expression of HUVEC VCAM-1, which were significantly reduced by washing. Spiking washed RCC supernatants with platelet-EVs showed significant increase in IL-8, MCP-1, VCAM-1, and E-selection in groups washed on Day 14. CONCLUSIONS: Platelet-EVs in RCCs are associated with pro-inflammatory activity. As washing significantly reduced RCC immunomodulatory activity, implementation of this process may improve transfusion outcomes.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".