Protein quality in Mirasol pathogen reduction technology–treated, apheresis‐derived fresh‐frozen plasma
Bibliographic record
Abstract
BACKGROUND: The Mirasol pathogen reduction technology (PRT) system for plasma is based on a riboflavin (vitamin B(2)) and ultraviolet (UV) light treatment process resulting in pathogen inactivation due to irreversible photo-oxidative damage of nucleic acids. The purpose of this study was to evaluate the in vitro protein quality of apheresis-derived plasma treated with riboflavin and UV light in comparison with untreated fresh-frozen plasma (FFP). STUDY DESIGN AND METHODS: Twenty apheresis plasma samples (270 + or - 10 mL) were combined with 35 + or - 5 mL of riboflavin solution (500 microM), yielding a mean 60 microM final riboflavin concentration, and then exposed to UV light (6.24 J/mL). Riboflavin and UV light-treated plasma was then flash frozen, within 8 hours of collection, generating treated FFP. Treated FFP was thawed and analyzed using standard coagulation assays, and the percent retention of protein activity was reported, relative to untreated, paired controls. RESULTS: Plasma proteins demonstrated different sensitivities to riboflavin and UV treatment. The amount of total protein remained unchanged. After treatment, fibrinogen (antigen) showed 99% retention; Factor (F)XII, FXIII, ADAMTS-13, and von Willebrand factor (ristocetin cofactor) 96% to 100%. Fibrinogen retained 77% activity, FII 80%, FVIIIc 75%, and FV 73% after treatment. Antithrombin, protein S, plasminogen, and alpha(2)-antiplasmin retained between 91 and 100% activity. CONCLUSION: The results from this study demonstrate that coagulant and anticoagulant proteins in riboflavin and UV light-treated (PRT) apheresis plasma are well preserved.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".