Bioactive DNA from extracellular vesicles and particles
Bibliographic record
Abstract
Extracellular vesicles (EVs) and particles (EPs) have recently emerged as active carriers of molecular biomarkers and mediators of intercellular communication. While most investigations have focused exclusively on the protein, lipid and RNA constituents of these extracellular entities, EV/EP DNA remains poorly understood, despite DNA being found in association with virtually all EV/EP populations. The functional potential of EV/EP DNA has been proposed in a number of pathological states, including malignancies and autoimmune diseases. Moreover, the effectiveness of cell-free DNA as the biomarker of choice in emerging liquid biopsy applications highlights the role that EV/EP DNA may play as a novel disease biomarker. In this review, we provide a comprehensive overview of EV/EP DNA studies conducted to date, with a particular focus on the roles of EV/EP DNA as a functional mediator and molecular biomarker in various pathologic states. We also review what is currently known about the origins, structure, localisation and distribution of EV/EP DNA, highlighting current controversies as well as opportunities for future investigation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".