Supercooled storage of red blood cells slows down the metabolic storage lesion
Bibliographic record
Abstract
Red blood cell (RBC) transfusion, a life-saving intervention, is limited by reduced RBC potency over time. Cold storage at +4 °C for up to 42 days can reduce transfusion efficacy due to alterations termed the "storage lesion." Strategies to mitigate the storage lesion include alkaline additive solutions and supercooled storage to extend storage by reducing metabolic stresses. However, RBC metabolism during supercooled storage in standard or alkaline additives remains unstudied. This study, thus, investigated the impact of storage additives (alkaline E-Sol5 and standard SAGM) and temperatures (+4 °C, -4 °C, -8 °C) on RBC metabolism during 21- and 42-days storage using high-throughput metabolomics. RBCs stored with E-Sol5 showed increased glycolysis and higher ratios of reduced to oxidized glutathione compared to SAGM. Supercooled storage at -4 °C showed markedly lower hemolysis than -8 °C, preserved adenylate pools, decreased glucose consumption, and reduced lactate accumulation and pentose phosphate pathway activation. The combination of supercooled storage and E-Sol5 helped to preserve ATP and 2,3-DPG reservoirs, while preventing catabolism and free fatty acid accumulation. While supercooled storage with E-Sol5 offers a promising alternative to standard storage for preserving RBC metabolic and functional quality, further research is necessary to validate and improve on these foundational findings..
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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".