Riboflavin and ultraviolet illumination affects selected platelet mRNA transcript amounts differently
Bibliographic record
Abstract
BACKGROUND: Pathogen inactivation (PI) techniques use ultraviolet (UV) illumination with or without a photosensitizer to destroy pathogen RNA and DNA. Although lacking a nucleus and innate DNA transcription, platelets (PLTs) contain RNA and can synthesize proteins. The impact of PI on PLT protein synthesis and function is unknown; altered synthesis may affect overall PLT quality. In this study we determine to what extent PLT RNA is affected by PI. STUDY DESIGN AND METHODS: In a pool-and-split design, paired apheresis PLT concentrates were treated with riboflavin and UV illumination or were left untreated. PLT total RNA and mRNA amounts specific for glycoproteins (GP)IIIa, GPIIb, and GPIb; α-granule proteins PLT factor (PF)4; osteonectin and thrombospondin (TSP); and housekeeping protein glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were determined using absorbance and quantitative polymerase chain reaction. RESULTS: After treatment, amounts of all analyzed mRNAs were significantly reduced (p < 0.05), but to different degrees. For GAPDH and PF4, transcripts appeared less susceptible to the treatment, with 70% remaining 1 hour after UV illumination. For GPIIIa and TSP, less than 15% remained after treatment. There was a correlation (R(2) = 0.85) between transcript length and amount of mRNA remaining 1 hour after treatment. Total RNA demonstrated a life span equal to the PLT life span of 10 to 11 days. CONCLUSION: This is the first report of the impact of riboflavin and UV illumination on PLT mRNA. Results suggest that all mRNA present in PLTs is affected by the treatment although the degree of the effect varies among transcripts.
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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) | 0.002 | 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".