The clinical significance of platelet microparticle-associated microRNAs
Bibliographic record
Abstract
Circulating blood platelets play a central role in the maintenance of hemostasis. They adhere to subendothelial extracellular matrix proteins that become exposed upon vessel wall damage, which is followed by platelet activation, further platelet recruitment, platelet aggregation and formation of an occlusive, or non-occlusive, platelet thrombus. Platelets host a surprisingly diverse transcriptome, which is comprised of ~9500 messenger RNAs (mRNAs) and different classes of non-coding RNAs, including microRNAs, as well as a significant repertoire of proteins that contribute to their primary (adhesion, aggregation, granule secretion) and alternative (RNA transfer, mRNA translation, immune regulation) functions. Platelets have the propensity to release microparticles (MPs; 0.1-1 μm in diameter) upon activation, which may mediate inflammatory responses and contribute to exacerbate inflammatory diseases and conditions. Carrying components of the platelets' cytoplasm, platelet MPs may exert their effects on recipient cells by transferring their content in platelet-derived bioactive lipid mediators, cytokines, mRNAs and microRNAs. Platelet MP-associated microRNAs may thus function also outside of platelets and play an important role in intercellular signaling and gene expression programming across the entire circulatory system. The role and importance of platelet MP-associated microRNAs in various aspects of biology and pathophysiology are increasingly recognized, and now provide the scientific basis and rationale to support further translational research and clinical studies. The clinical significance, pathophysiological role as well as the diagnostic and therapeutic potential of platelet MP-associated microRNAs in cardiovascular diseases, platelet transfusion and cancer will be discussed.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".