Red cell supernatant effects on endothelial cell function and innate immune activation is influenced by donor age and sex
Bibliographic record
Abstract
Background and objectives Blood donor characteristics significantly affect the pre‐storage and post‐storage quality of red cell concentrates (RCCs). This study investigated the impact of donor age and sex on RCC characteristics and the effect of RCC supernatants on the vascular endothelium and monocytes. Materials and methods RCC units selected from four donor groups based on donor age and sex (male ≤ 30‐years old (yo), male ≥ 60‐yo, female ≤ 30‐yo and female ≥ 60‐yo) were stored at 1–6°C and tested on day 7, 21 and 42. RCC supernatants were analysed for the number, concentration and size of extracellular vesicles (EVs). Human vein endothelial cells (HUVECs) monolayers were incubated with RCC supernatants and assessed for expression of vascular cell adhesion molecule‐1 (VCAM‐1) and E‐selectin by flow cytometry as well as endothelial permeability using a trans‐epithelial‐electrical‐resistance assay. Monocytes and HUVECs were incubated with RCC supernatants and supernatants assayed for cytokines/chemokines. Results RCCs from female donors were found to have lower unit volume, haemoglobin content and haematocrit compared to RCCs from male donors (P < 0·05). RCC supernatants from female ≥ 60‐yo had a higher concentration of large EVs (≥200 nm) compared to both male ≤ 30‐yo (P = 0·039) and male ≥ 60‐yo supernatants (P = 0·019). Treatment with RCC supernatants from male ≤ 30‐yo donors induced lower expression of VCAM‐1, lower cytokine/chemokine release and showed the least effect on endothelial permeability. Conclusion Donor characteristics affect RCC quality parameters and influence the endothelial and immune modulation potential of RCC supernatants in vitro.
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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) | 0.002 | 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".