Thematic Review Series: Exosomes and Microvesicles: Lipids as Key Components of their Biogenesis and Functions Extracellular vesicles and their content in bioactive lipid mediators: more than a sack of microRNA
Bibliographic record
Abstract
Extracellular vesicles (EVs), such as exosomes and microvesicles, are small membrane-bound vesicles released by cells under various conditions. In a multitude of physiological and pathological conditions, EVs contribute to intercellular communication by facilitating exchange of material between cells. Rapidly growing interest is aimed at better understanding EV function and their use as biomarkers. The vast EV cargo includes cytokines, growth factors, organelles, nucleic acids (messenger and micro RNA), and transcription factors. A large proportion of research dedicated to EVs is focused on their microRNA cargo; however, much less is known about other EV constituents, in particular, eicosanoids. These potent bioactive lipid mediators, derived from arachidonic acid, are shuttled in EVs along with the enzymes in charge of their synthesis. In the extracellular milieu, EVs also interact with secreted phospholipases to generate eicosanoids, which then regulate the transfer of cargo into a cellular recipient. Eicosanoids are useful as biomarkers and contribute to a variety of biological functions, including modulation of distal immune responses. Here, we review the reported roles of eicosanoids conveyed by EVs and describe their potential as biomarkers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".