Platelet Pathogen Reduction Technologies Alter the MicroRNA Profile of Platelet-Derived Microparticles
Bibliographic record
Abstract
Despite improvements in donor screening and increasing efforts to avoid contamination and the spread of pathogens in clinical platelet concentrates (PCs), the risks of transfusion-transmitted infections remain important. Relying on an ultraviolet photo activation system, pathogen reduction technologies (PRTs), such as Intercept and Mirasol, utilize amotosalen, and riboflavin (vitamin B2), respectively, to mediate inactivation of pathogen nucleic acids. Although they are expected to increase the safety and prolong the shelf life of clinical PCs, these PRTs might affect the quality and function of platelets, as recently reported. Upon activation, platelets release microparticles (MPs), which are involved in intercellular communications and regulation of gene expression, thereby mediating critical cellular functions. Here, we have used small RNA sequencing (RNA-Seq) to document the effect of PRT treatment on the microRNA profiles of platelets and derived MPs. PRT treatment did not affect the microRNA profile of platelets. However, we observed a specific loading of certain microRNAs into platelet MPs, which was impaired by treatment with Intercept or its Additive solution (SSP+). Whereas, Intercept had an impact on the microRNA profile of platelet-derived MPs, Mirasol did not impact the microRNA profile of platelets and derived MPs, compared to non-treated control. Considering that platelet MPs are able to transfer their microRNA content to recipient cells, and that this content may exert biological activities, those findings suggest that PRT treatment of clinical PCs may modify the bioactivity of the platelets and MPs to be transfused and argue for further investigations into PRT-induced changes in clinical PC content and function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".