Extended supercooled storage of red blood cells
Bibliographic record
Abstract
Red blood cell (RBC) transfusions facilitate many life-saving acute and chronic interventions. Transfusions are enabled through the gold-standard hypothermic storage of RBCs. Today, the demand for RBC units is unfulfilled, partially due to the limited storage time, 6 weeks, in hypothermic storage. This time limit stems from high metabolism-driven storage lesions at +1-6 °C. A recent and promising alternative to hypothermic storage is the supercooled storage of RBCs at subzero temperatures, pioneered by our group. Here, we report on long-term supercooled storage of human RBCs at physiological hematocrit levels for up to 23 weeks. Specifically, we assess hypothermic RBC additive solutions for their ability to sustain supercooled storage. We find that a commercially formulated next-generation solution (Erythro-Sol 5) enables the best storage performance and can form the basis for further improvements to supercooled storage. Our analyses indicate that oxidative stress is a prominent time- and temperature-dependent injury during supercooled storage. Thus, we report on improved supercooled storage of RBCs at -5 °C by supplementing Erythro-Sol 5 with the exogenous antioxidants, resveratrol, serotonin, melatonin, and Trolox. Overall, this study shows the long-term preservation potential of supercooled storage of RBCs and establishes a foundation for further improvement toward clinical translation.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".