Closed system processing variables affect <scp>post‐thaw</scp> quality characteristics of cryopreserved red cell concentrates
Bibliographic record
Abstract
BACKGROUND: Differences in manufacturing conditions using the Haemonetics ACP 215 cell processor result in cryopreserved red cell concentrates (RCCs) of varying quality. This work studied the effect of processing method, additive solution, and storage duration on RCC quality to identify an optimal protocol for the manufacture of cryopreserved RCCs. MATERIALS AND METHODS: RCCs were pooled-and-split and stored for 7, 14, or 21 days before cryopreservation. Units were glycerolized with the ACP 215 using a single or double centrifugation method. After thawing, the RCCs were deglycerolized, suspended in AS-3, SAGM, ESOL, or SOLX/AS-7, and stored for 0, 3, 7, 14, or 21 days before quality testing. Quality assessments included hemoglobin content, hematocrit, hemolysis, adenosine triphosphate (ATP), supernatant potassium, and mean cell volume. RESULTS: Both glycerolization methods produced RCCs that met regulatory standards for blood quality. Dual centrifugation resulted in higher hemoglobin content, fewer processing alerts, and a shorter deglycerolization time than single centrifugation processing. Units processed with AS-3 and ESOL met regulatory standards when stored for up to 21 days pre-cryopreservation and 21 days post-deglycerolization. However, ESOL demonstrated superior maintenance of ATP over RBCs in AS-3. Some RCCs suspended in SAGM and SOLX exceeded acceptable hemolysis values after 7 days of post-deglycerolization storage regardless of pre-processing storage length. CONCLUSIONS: When manufacturing cryopreserved RCCs using the ACP 215, dual centrifugation processing with AS-3 or ESOL additive solutions is preferred, with storage periods of up to 21 days both pre-processing and post-deglycerolization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".