Identification of potential protein quality markers in pathogen inactivated and gamma‐irradiated red cell concentrates
Bibliographic record
Abstract
Purpose Post‐collection manipulations (PCMs) aim to increase blood product safety. However, PCMs improve safety at a cost to quality, causing elevated hemolysis. As hemolysis is linked to red blood cell membrane integrity, a quantitative proteomics approach was employed to assess membrane proteome alterations induced by PCMs. Experimental design Three ABO‐matched whole blood (WB) units were pooled‐and‐split into three identical units. One WB unit was treated with riboflavin/ultraviolet illumination prior to red cell concentrate (RCC) production (RCCWB*). Two WB units were produced into RCC; one was gamma‐irradiated (RCCγ) and the other was left untreated as control (RCCØ). In vitro quality parameters were measured during storage. Membrane protein profiles of RCCØ, RCCγ, and RCCWB* were assessed on selected hemoglobin‐depleted membrane fractions using a quantitative proteomics approach based on iTRAQ. Results Quantitative proteomic analysis identified 100 proteins at the membrane, with seven unique proteins exhibiting significant changes in RCCWB* at day 28 of storage. Membrane peroxiredoxin‐2, catalase, and proteasome levels demonstrated robust negative correlation with percentage hemolysis. Conclusion Overall, the in vitro parameters and alterations of membrane protein profiles indicated that pathogen inactivation treatment impacts RCC quality more severely than gamma‐irradiation and that it may induce damage through a predominately oxidative mechanism.
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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 | 0.001 |
| 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 | 0.001 | 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".