Differences in Rat and Human Erythrocytes Following Blood Component Manufacturing: The Effect of Additive Solutions
Bibliographic record
Abstract
BACKGROUND: Small animal models have been previously used in transfusion medicine studies to evaluate the safety of blood transfusion products. Although there are multiple studies on the effects of blood banking practices on human red blood cells (RBCs), little is known about the effect of blood component manufacturing on the quality of rat RBCs. METHODS: Blood from Sprague-Dawley rats and human volunteers (n = 6) was collected in CPD anticoagulant, resuspended in SAGM or AS3, and leukoreduced. In vitro quality was analyzed, including deformability, aggregation, microvesiculation, phosphatidylserine (PS) expression, percent hemolysis, ATP, 2,3-DPG, osmotic fragility, and potassium concentrations. RESULTS: Compared to human RBCs, rat RBCs had decreased deformability, membrane rigidity, aggregability, and microvesiculation after component manufacturing process. Rat RBCs in SAGM showed higher hemolysis compared to human RBCs in SAGM (rat 4.70 ± 0.83% vs. human 0.34 ± 0.07%; p = 0.002). Rat RBCs in AS3 had greater deformability and rigidity than in SAGM. The number of microparticles/µl and the percentage PS expression were lower in rat RBCs in AS3 than in rat RBCs in SAGM. Hemolysis was also significantly lower in AS3 compared to SAGM (2.21 ± 0.68% vs. 0.87 ± 0.39%; p = 0.028). CONCLUSION: Rat RBCs significantly differ from human RBCs in metabolic and membrane-related aspects. SAGM, which is commonly used for human RBC banking, causes high hemolysis and is not compatible with rat RBCs.
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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 | 0.000 | 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".