Impact of input volume on red cell quality in deglycerolized <scp>RBCs</scp> using a modified <scp>ACP</scp>‐215 protocol
Bibliographic record
Abstract
BACKGROUND: The ACP 215 automated cell processor is used to glycerolize and deglycerolize red cell concentrates (RCCs). Its primary advantage over the COBE 2991, previously used to cryopreserve RCCs, is that it maintains a closed system enabling extended post-thaw expiry. However, it was observed that post-deglycerolization hematocrits (Hct) of units processed with the LN236 kit are markedly lower than those processed using the COBE 2991. Therefore, we intended to determine whether a modified process using a smaller volume deglycerolization kit (LN235) could increase the final Hct with limited deleterious effects on product characteristics. STUDY DESIGN AND METHODS: Two proof-of-concept (POC) studies, conducted to determine the feasibility of using the LN235 processing kit for deglycerolization, identified the necessary modifications to the pre- and post-deglycerolization process, after which a two-part study characterized the modified protocol. The impact of pre-cryopreservation storage duration (7-21 days), input red cell mass, and the type of CPD/SAGM RCC production method (red cell filtration and whole blood filtration) were investigated. RESULTS: Using the LN235 kit in conjunction with a volume reduction step for RCCs with a red cell mass exceeding 180 mL allowed for an ~8% increase in Hct. As expected, slightly lower recoveries were seen for large RCCs due to volume reduction; however, there were no other detrimental outcomes on product quality. CONCLUSIONS: Leveraging the LN235 kit, recommended by Haemonetics for units with a red cell mass of ≤180 mL, can be used to increase the post-deglycerolization Hct of RCCs that exceed this volume.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".