Automated processing of Meryman‐frozen red blood cells: A novel protocol for deglycerolization
Bibliographic record
Abstract
BACKGROUND: Many blood services maintain large inventories of red blood cell (RBC) units cryopreserved in glycerol using the Meryman method. With the discontinuation of the COBE® 2991 cell processor, an alternative thawing method is needed. We aimed to develop a deglycerolization protocol for Meryman-frozen units using the ACP® 215 cell washer. METHODS: In the optimization phase, Meryman-frozen RBC units stored for 10 years were thawed, paired, and divided into two groups: one with a centrifugation step to remove glycerol before deglycerolization ("volume reduction") and one without. Biochemical and hematological parameters assessed included hemolysis, hematocrit, hemoglobin, ATP, pH, and osmolality. The protocol was then validated. RESULTS: Hemolysis rates were lower with than without volume reduction (0.4% vs. 0.6%). Centrifuged RBCs also showed higher recovery (72% vs. 63%), increased hematocrit (0.51 L/L vs. 0.40 L/L), and improved pH stability (6.17 vs. 6.11). In the validation phase, six RBC units deglycerolized using the volume reduction step met Canadian Standards Association requirements for hematocrit, hemoglobin, hemolysis, and sterility. DISCUSSION: We optimized and validated a new protocol leveraging the ACP® 215 cell washer to deglycerolize Meryman-frozen RBCs. This method yielded low hemolysis, acceptable pH, and satisfactory recovery, especially with prior glycerol removal by centrifugation. The protocol has been successfully implemented, and Meryman-frozen RBC units have since been reliably thawed, meeting regulatory standards and supporting hospital needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".