Protein translation occurs in platelet concentrates despite riboflavin/UV light pathogen inactivation treatment
Bibliographic record
Abstract
PURPOSE: Pathogen inactivation technologies (PITs) were introduced into blood banking to further improve the safety of blood products. However, the UV light used in PITs to terminate pathogen growth might alter the functionality of the cells in the blood product as well as the protein profile of the blood components. This study employed proteomic approaches to assess changes in the platelet proteome and translatome. EXPERIMENTAL DESIGN: Apheresis-derived platelet concentrates treated with riboflavin/UV light or untreated controls were analyzed throughout blood bank storage by quantitative proteomics using iTRAQ and puromycin-associated nascent chain (PUNCH) proteomics. RESULTS: Quantitative proteomic analysis identified 408 individual proteins including 26 unique proteins that changed in the treated arm during storage. Proteomic results were confirmed using immunoblot analyses and results suggested a translational control of the protein expression profile. PUNCH proteomic analysis of day 7 samples from illuminated units identified 52 unique platelet proteins that incorporated puromycin, including proteins involved in the cytoskeleton, metabolism, and signaling. CONCLUSION AND CLINICAL RELEVANCE: This study demonstrates for the first time that platelets can synthesize proteins despite the riboflavin and UV treatment and suggests that platelets may possess a mechanism to protect their mRNA from damage by the PI treatment.
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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) | 0.001 | 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".