Effects of pathogen reduction systems on platelet microRNAs, mRNAs, activation, and function
Bibliographic record
Abstract
Pathogen reduction (PR) systems for platelets, based on chemically induced cross-linking and inactivation of nucleic acids, potentially prevent transfusion transmission of infectious agents, but can increase clinically significant bleeding in some clinical studies. Here, we documented the effects of PR systems on microRNA and mRNA levels of platelets stored in the blood bank, and assessed their impact on platelet activation and function. Unlike platelets subjected to gamma irradiation or stored in additive solution, platelets treated with Intercept (amotosalen+ ultraviolet-A [UVA] light) exhibited significantly reduced levels of 6 of the 11 microRNAs, and 2 of the 3 anti-apoptotic mRNAs (Bcl-xl and Clusterin) that we monitored, compared with platelets stored in plasma. Mirasol (riboflavin+ UVB light) treatment of platelets did not produce these effects. PR neither affected platelet microRNA synthesis or function nor induced cross-linking of microRNA-sized endogenous platelet RNA species. However, the reduction in the platelet microRNA levels induced by Intercept correlated with the platelet activation (p < 0.05) and an impaired platelet aggregation response to ADP (p < 0.05). These results suggest that Intercept treatment may induce platelet activation, resulting in the release of microRNAs and mRNAs from platelets. The clinical implications of this reduction in platelet nucleic acids secondary to Intercept remain to be established.
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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 | 0.001 |
| 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".