P‐BB‐77 | Validation of a Small‐Scale Red Blood Cell Cryopreservation Method
Bibliographic record
Abstract
Red blood cell (RBC) cryopreservation using the high glycerol method is an accepted approach for long-term storage of red cell concentrates (RCCs). This process is commonly used to maintain frozen RCC units for rare blood programs. However, small-scale (SS) cryopreservation is advantageous for research and development applications where functional and intact RBCs are required, but large-scale processing is not feasible or practical. Cryopreservation protocols for SS RBC samples were previously developed. This work aimed to characterize the inherent variability of these methods. Two glycerolization methods were evaluated: (G1) Glycerolyte 57 (Fenwal) was added dropwise to an aliquot of RBCs from an RCC, and (G2) the RCC aliquot was centrifuged (2200g, 10 min, RT) for supernatant reduction, glycerolized, then centrifuged for glycerol reduction. For both methods, 1 mL of glycerolized RCCs was aliquoted into vials and frozen at 1°C/min to −80°C. Samples were stored at ≤ −65°C, thawed, and deglycerolized using a three-step wash method (12% NaCl, 1.6% NaCl, and 0.9% NaCl/0.2% dextrose). RBCs were suspended in AS-3 post-deglycerolization and tested for hemolysis and hematocrit at <1 and 24 h post-deglycerolization (PD). Within lot (WL) repeatability was evaluated by glycerolizing an RCC sample and splitting it into multiple vials (n = 10/method). Across lot (AL) repeatability was evaluated for both methods by glycerolizing 10 separate RCC samples. Three technicians glycerolized RCCs (n = 6) using G1 and G2 methods to determine technician variability. WL and AL results (Table 1) demonstrated that the SS cryopreservation process using either glycerolization method is highly repeatable and produces deglycerolized RBCs with <0.8% hemolysis after 24 h of storage. The largest increase in variability was observed for hemolysis at the 24-h time point. When comparing across technicians, the bias between techs varied from −0.02 to 0.05 and −0.09 to 0.17 for <1 h PD hemolysis results for G1 and G2, respectively. TABLE 1. Hematocrit and hemolysis results for method G1 and G2.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".