Characteristics of Extracellular Vesicles in Red Blood Concentrates Change with Storage Time and Blood Manufacturing Method
Bibliographic record
Abstract
Background: Extracellular vesicles (EVs) in blood products are potential effectors of inflammation and coagulation after transfusion. The aim of this study was to assess the impact of different blood manufacturing methods and duration of hypothermic storage on the EV subpopulations in relation to other in vitro quality parameters of red blood cell concentrate (RCC) products. Methods: RCCs were produced using whole blood filtration (WBF) or red cell filtration (RCF) (n = 12/method), refrigerated for 43 days, and evaluated for EV size profile and concentration, red cell deformability, ATP and 2,3-DPG, hemolysis, and hematological indices. Results: The total number of EVs increased significantly with storage in both methods, and WBF-RCCs contained the higher numbers of EVs compared to RCF-RCCs. The concentration of small EVs was greater in WBF-RCCs versus RCF-RCCs, with difference between the two methods observed on day 43 of storage (p = 0.001). Throughout storage, significant decreases were identified in ATP, 2,3-DPG, and EImax, while an increase in hemolysis was observed in both RCC products. Conclusion: The dynamic shift in the size and concentration of the EV subpopulations is dependent on the blood manufacturing method and length of storage. Better understanding of the potential clinical implications of these heterogeneous populations of EVs are needed.
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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 | 0.000 |
| 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".