Screening of red blood cells for extracellular vesicle content as a product quality indicator
Bibliographic record
Abstract
BACKGROUND: The controversy around the quality and clinical impact of stored and differentially manufactured red cell concentrates (RCCs) from different donor groups is ongoing. Current studies are limited by the lack of quality measures suitable for routine screening of RCCs. As extracellular vesicles (EVs) are markers of cellular activation or degradation, this study investigated the utility of EV screening to characterize the effects of RBCs production methods and storage. STUDY DESIGN AND METHODS: RCCs were prepared by whole blood filtration or red blood cell (RBC) filtration methods, centrifuged to prepare a supernatant, and tested for EV content (dynamic light scattering or tunable resistive pulse-sensing techniques), hemolysis, ATP, and RBC deformability on Days 7, 21, and 42 of storage. To simulate nondestructive quality control (QC) testing, 1 RBC unit was tested in parallel with six 10-mL aliquots that were stored in small-volume containers. RESULTS: EV content showed a linear increase with storage time (p < 0.001) and correlated with supernatant hemoglobin and inversely with ATP or RBC deformability. The method of component manufacturing influenced the characteristics of the EVs during storage. A strong correlation between both EV testing methods' measure of total EV was observed. EV content in the six aliquots were consistent at each time point but statistically higher than in the original RCCs on and after 21 days of storage. CONCLUSIONS: EV content correlates with measures of hemolysis and other RBC quality indicators and could be implemented as a routine screening tool for nondestructive QC testing of RCCs.
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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 | 0.004 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".