Extracellular vesicle-associated DNA: ten years since its discovery in human blood
Bibliographic record
Abstract
Extracellular vesicles (EVs) have emerged as key players in intercellular communication, facilitating the transfer of crucial cargo between cells. Liquid biopsy, particularly through the isolation of EVs, has unveiled a rich source of potential biomarkers for health and disease, encompassing proteins and nucleic acids. A milestone in this exploration occurred a decade ago with the identification of extracellular vesicle-associated DNA (EV-DNA) in the bloodstream of a patient diagnosed with pancreatic cancer. Subsequent years have witnessed substantial advancements, deepening our insights into the molecular intricacies of EV-DNA emission, detection, and analysis. Understanding the complexities surrounding the release of EV-DNA and addressing the challenges inherent in EV-DNA research are pivotal steps toward enhancing liquid biopsy-based strategies. These strategies, crucial for the detection and monitoring of various pathological conditions, particularly cancer, rely on a comprehensive understanding of why and how EV-DNA is released. In our review, we aim to provide a thorough summary of a decade's worth of research on EV-DNA. We will delve into diverse mechanisms of EV-DNA emission, its potential as a biomarker, its functional capabilities, discordant findings in the field, and the hurdles hindering its clinical application. Looking ahead to the next decade, we envision that advancements in EV isolation and detection techniques, coupled with improved standardization and data sharing, will catalyze the development of novel strategies exploiting EV-DNA as both a source of biomarkers and therapeutic targets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".