Donor sex, <scp>pre‐donation</scp> hemoglobin, and manufacturing affect <scp>CD71</scp><sup>+</sup><scp>cells</scp> in red cell concentrates
Bibliographic record
Abstract
Abstract Background Circulating CD71+ red blood cells (RBCs) have been reported to play an immunomodulatory role in vivo, which may contribute to adverse donor–recipient sex‐mismatched transfusion outcomes. However, it is not clear how CD71+ RBC quantity in red cell concentrates (RCCs) is affected by manufacturing methods and donor factors such as donor sex, donor age, pre‐donation hemoglobin (Hb), venous Hb (Hbv) levels, and donation frequency. Methods We determined CD71+ RBCs and Hb levels in whole blood (WB) from healthy donors (42 male/38 female). Using small‐scale red cell filtration (RCF) and whole blood filtration (WBF) methods, leukoreduced RCCs were processed from WB samples (n = 6) and the CD71+ RBCs were determined at days 1, 7, and 28. We examined uni‐ and multivariate associations among CD71+ RBCs, donor factors, and manufacturing method. Results Male donors had a higher CD71+ RBC concentration than females (p < .001), especially male donors aged 17–50 years with 1 or 2 WB donations over the previous 12 months. Donors with a Hbv above 155 g/L had a higher CD71+ RBC concentration than an Hbv level below 140 g/L (p < .05). There was a positive correlation between pre‐donation Hb and CD71+ RBC concentration (Pearson r = 0.41). WBF RCCs had a higher total number of CD71+ RBCs than RCF‐produced RCCs on day 1 (p < .05). Discussion RCCs have variable numbers of CD71+ RBCs. This makes understanding the impact of donor factors and manufacturing methods on the immunomodulatory effect of CD71+ RBCs critical in exploring donor–recipient sex‐mismatched transfusions.
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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 | 0.000 | 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) | 0.003 | 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".