Blood manufacturing methods affect red blood cell product characteristics and immunomodulatory activity
Bibliographic record
Abstract
Transfusion of red cell concentrates (RCCs) is associated with increased risk of adverse outcomes that may be affected by different blood manufacturing methods and the presence of extracellular vesicles (EVs). We investigated the effect of different manufacturing methods on hemolysis, residual cells, cell-derived EVs, and immunomodulatory effects on monocyte activity. Thirty-two RCC units produced using whole blood filtration (WBF), red cell filtration (RCF), apheresis-derived (AD), and whole blood-derived (WBD) methods were examined (n = 8 per method). Residual platelet and white blood cells (WBCs) and the concentration, cell of origin, and characterization of EVs in RCC supernatants were assessed in fresh and stored supernatants. Immunomodulatory activity of RCC supernatants was assessed by quantifying monocyte cytokine production capacity in an in vitro transfusion model. RCF units yielded the lowest number of platelet and WBC-derived EVs, whereas the highest number of platelet EVs was in AD (day 5) and in WBD (day 42). The number of small EVs (<200 nm) was greater than large EVs (≥200 nm) in all tested supernatants, and the highest level of small EVs were in AD units. Immunomodulatory activity was mixed, with evidence of both inflammatory and immunosuppressive effects. Monocytes produced more inflammatory interleukin-8 after exposure to fresh WBF or expired WBD supernatants. Exposure to supernatants from AD and WBD RCC suppressed monocyte lipopolysaccharide-induced cytokine production. Manufacturing methods significantly affect RCC unit EV characteristics and are associated with an immunomodulatory effect of RCC supernatants, which may affect the quality and safety of RCCs.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".