Synergistic effects of liposomes, trehalose, and hydroxyethyl starch for cryopreservation of human erythrocytes
Bibliographic record
Abstract
Red blood cells (RBCs) can be cryopreserved using glycerol as a cryoprotective agent, but one of the main disadvantages is the time-consuming deglycerolization step. Novel cryopreservation strategies for RBCs using nontoxic cryoprotective agents are urgently needed. The effect of DMPC, DOPC, and DPPC liposomes on survival of RBCs cryopreserved with trehalose and HES has been evaluated. DMPC caused hemolysis before freezing and affected RBC deformability parameters. DMPC treated RBCs displayed a strong increase in trehalose uptake compared to control cells, whereas DOPC treated liposomes only displayed a slight increase in trehalose uptake. High intracellular trehalose contents were observed after cryopreservation. The recovery of cells incubated with trehalose and liposomes, frozen in HES ranged between 92.6 and 97.4% immediately after freezing. Recovery values of RBCs frozen in HES, however, decreased to 66.5% after 96 h at 4°C compared to 77.5% for DOPC treated RBCs. The recovery of RBCs incubated and frozen in trehalose medium was 77.8%. After 96 hours post-thaw storage recovery of these cells was 81.6%. DOPC and DPPC treated RBCs displayed higher recovery rates (up to 89.7%) after cryopreservation in trehalose compared to control RBCs. Highest survival rates were obtained using a combination of trehalose and HES: 97.8% directly after thawing and 81.8% 96-h post-thaw. DOPC liposomes, trehalose and HES protect RBCs during cryopreservation in a synergistic manner. The advantage is that the protective compounds do not need to be removed before transfusion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".