Donor‐dependent aging of young and old red blood cell subpopulations: Metabolic and functional heterogeneity
Bibliographic record
Abstract
Abstract Background Characteristics of red blood cells (RBCs) are influenced by donor variability. This study assessed quality and metabolomic variables of RBC subpopulations of varied biologic age in red blood cell concentrates (RCCs) from male and female donors to evaluate their contribution to the storage lesion. Study Design and Methods Red blood cell concentrates from healthy male (n = 6) and female (n = 4) donors were Percoll separated into less dense (“young”, Y‐RCCs) and dense (“old”, O‐RCCs) subpopulations, which were assessed weekly for 28 days for changes in hemolysis, mean cell volume (MCV), hemoglobin concentration (MCHC), hemoglobin autofluorescence (HGB), morphology index (MI), oxygen affinity (p50), rigidity, intracellular reactive oxygen species (ROS), calcium ([Ca2+]), and mass spectrometry–based metabolomics. Results Young RCCs having disc‐to‐discoid morphology showed higher MCV and MI, but lower MCHC, HGB, and rigidity than O‐RCCs, having discoid‐to‐spheroid shape. By Day 14, Y‐RCCs retained lower hemolysis and rigidity and higher p50 compared to O‐RCCs. Donor sex analyses indicated that females had higher MCV, HGB, ROS, and [Ca2+] and lower hemolysis than male RBCs, in addition to having a decreased rate of change in hemolysis by Day 28. Metabolic profiling indicated a significant sex‐related signature across all groups with increased markers of high membrane lipid remodeling and antioxidant capacity in Y‐RCCs, whereas O‐RCCs had increased markers of oxidative stress and decreased coping capability. Conclusion The structural, functional, and metabolic dissimilarities of Y‐RCCs and O‐RCCs from female and male donors demonstrate RCC heterogeneity, where RBCs from females contribute less to the storage lesion and age slower than males.
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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 | 0.000 |
| 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) | 0.001 | 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".