Biopreservation of red blood cells – the struggle with hemoglobin oxidation
Bibliographic record
Abstract
One of the least recognized causes of cellular damage during ex vivo preservation of red blood cells is oxidative injury to the hemoglobin. The latter has been associated with hemolysis through the release of toxic substances and oxidation of vital cell components. This review delineates some of the major pathways that link hemoglobin oxidation and cellular damage, and summarizes the incidence of red blood cell oxidative injury during hypothermic storage, cryopreservation and desiccation stress. Red blood cell hypothermic storage, despite its success, is not exempt from oxidative injury. Growing evidence portrays a time-dependant oxidative assault including formation of reactive oxygen species, attachment of denatured hemoglobin to membrane phospholipids and the release of hemoglobin-containing membrane microvesicles throughout storage. Similar symptoms have been observed in attempts to stabilize red blood cells in the dried state, in which methemoglobin levels of reconstituted red blood cells reached 50%. Factors affecting the rate of hemoglobin oxidation during red blood cell ex vivo storage include compromised antioxidant activity, high concentrations of glucose in the storage media and the presence of molecular oxygen. Hemoglobin oxidation largely dictates our ability to effectively preserve red blood cells. Understanding its origins along with investigating methods to minimize it can significantly improve the quality of our future blood products.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".