Rapid removal of glycerol from frozen‐thawed red blood cells
Bibliographic record
Abstract
The storage of red blood cells (RBCs) in a refrigerated state allows a shelf life of a few weeks, whereas RBCs frozen in 40% glycerol have a shelf life of 10 years. Despite the clear logistical advantages of frozen blood, it is not widely used in transfusion medicine. One of the main reasons is that existing post-thaw washing methods to remove glycerol are prohibitively time consuming, requiring about an hour to remove glycerol from a single unit of blood. In this study, we have investigated the potential for more rapid removal of glycerol. Using published biophysical data for human RBCs, we mathematically optimized a three-step deglycerolization process, yielding a procedure that was less than 32 s long. This procedure was found to yield 70% hemolysis, a value that was much higher than expected. Consequently, we systematically evaluated three-step deglycerolization procedures, varying the solution composition and equilibration time in each step. Our best results consisted of less than 20% hemolysis for a deglycerolization time of 3 min, and it is expected that even further improvements could be made with a more thorough optimization and more reliable biophysical data. Our results demonstrate the potential for significantly reducing the deglycerolization time compared with existing methods. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29:609-620, 2013.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".