Platelet extracellular vesicles and the secretory interactome join forces in health and disease
Bibliographic record
Abstract
Extracellular vesicles (EVs) are small membrane-bound vesicles released by cells under various conditions. They are found in the extracellular milieu in all biological fluids. As the concentrations, contents, and origin of EVs can change during inflammation, the assessment of EVs can be used as a proxy of cellular activation. Here, we review the literature regarding EVs, more particularly those released by platelets and their mother cells, the megakaryocytes. Their cargo includes cytokines, growth factors, organelles (mitochondria and proteasomes), nucleic acids (messenger and non-coding RNA), transcription factors, and autoantigens. EVs may thus contribute to intercellular communication by facilitating exchange of material between cells. EVs also interact with other molecules secreted by cells. In autoimmune diseases, EVs are associated with antibodies secreted by B cells. By definition, EVs necessarily comprise a phospholipid moiety, which is thus the target of secreted phospholipases also abundantly expressed in the extracellular milieu. We discuss how platelet-derived EVs, which represent the majority of the circulating EVs, may contribute to immunity through the activity of their cargo or in combination with the secretory interactome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".