Improved in vitro quality of stored red blood cells upon oxygen reduction prior to riboflavin/UV light treatment of whole blood
Bibliographic record
Abstract
BACKGROUND: The application of riboflavin/UV-based pathogen inactivation (PI) to whole blood (WB) is currently limited by its negative impact on red blood cell (RBC) quality. The generation of reactive oxidative species in RBC products contributes to increased hemolysis. This study evaluated the impact of deoxygenation of WB prior to riboflavin/UV light treatment versus deoxygenation of RBC concentrates after PI treatment by monitoring RBC in vitro quality parameters. STUDY DESIGN AND METHODS: Six ABO-matched WB units were pooled and split. Within three pairs, one unit was treated with riboflavin/UV light while the other was kept as an untreated control prior to manufacture into red cell concentrates (RCCs). The first pair (Cntr; Cntr-PI) served as the normoxic controls. Deoxygenation was performed at the RCC level for the second pair (RCCdeox; PI-RCCdeox), and at the WB level of the third pair (WBdeox; WBdeox-PI). In vitro qualities of the respective RBC units were assessed throughout storage. RESULTS: The data for the Cntr and Cntr-PI units were comparable to previous reports. The PI-RCCdeox units exhibited worse in vitro quality for most parameters tested compared to Cntr-PI and WBdeox-PI units throughout storage. Hemolysis and microvesicle release was significantly (p < 0.05) higher on Days 21 and 42 in Cntr-PI units compared to WBdeox-PI units. CONCLUSION: WB deoxygenation may help to decrease the accelerated deterioration in RCC in vitro quality caused by treatment with riboflavin/UV light. Treatment of WB under reduced oxygen levels needs to be assessed for PI effectiveness.
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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 | 0.001 | 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".