Buffy coat (top/bottom)‐ and whole‐blood filtration (top/top)‐produced red cell concentrates differ in size of extracellular vesicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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 it