Buffy coat (top/bottom)‐ and whole‐blood filtration (top/top)‐produced red cell concentrates differ in size of extracellular vesicles
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The influence that blood component separation methods have on changes to the red blood cell membrane during storage is not well understood. In Canada, red cell concentrates (RCCs) are produced using the buffy coat (BC, top/bottom) and the whole-blood filtration (WBF, top/top) methods, and this study aimed at comparing their influence on the characteristics of the extracellular vesicles (EV) which accumulated in the respective products during storage. MATERIALS AND METHODS: Using flow cytometry, dynamic light scattering and mass spectrometry, we assessed RCC EVs for concentration, size, lipid composition and correlation with supernatant haemoglobin (Hb). RESULTS: Accumulation of RBC EVs (CD235a(+) ) with storage time was similar in WBF and BC RCCs. The size of the EVs changed from <100 nm at d5 to near 200 nm by d42, with the EVs from WBF being smaller (P < 0·001) than BC RCCs at all storage times. The amount of EV-bound Hb in the WBF and BC units was similar (about 10% of total supernatant Hb). WBF EVs and BC EVs displayed similar lipid composition. CONCLUSION: Haemolysis and EVs increase in BC and WBF RCCs during storage. Differences in the size characteristics of the EVs in WBF and BC RCCs suggest that non-RBC EVs are more prevalent in WBF products. Understanding the impact that manufacturing has on the characteristics of the different populations of EVs in RCCs will aid quality improvement efforts.
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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) | 0.001 | 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".