Isolation and characterization of extracellular vesicles for clinical applications in cancer – time for standardization?
Bibliographic record
Abstract
. An important physiological role of EVs is cell-cell communication. EVs interact with, and deliver, their contents to recipient cells in a functional capacity; this makes EVs desirable vehicles for the delivery of therapeutic cargoes. In addition, as EVs contain proteins, lipids, glycans, and nucleic acids that reflect their cell of origin, their potential utility in disease diagnosis and prognostication is of great interest. The number of published studies analyzing EVs and their contents in the pre-clinical and clinical setting is rapidly expanding. However, there is little standardization as to what techniques should be used to isolate, purify and characterize EVs. Here we provide a comprehensive literature review encompassing the use of EVs as diagnostic and prognostic biomarkers in cancer. We also detail their use as therapeutic delivery vehicles to treat cancer in pre-clinical and clinical settings and assess the EV isolation and characterization strategies currently being employed. Our report details diverse isolation strategies which are often dependent upon multiple factors such as biofluid type, sample volume, and desired purity of EVs. As isolation strategies vary greatly between studies, thorough EV characterization would be of great importance. However, to date, EV characterization in pre-clinical and clinical studies is not consistently or routinely adhered to. Standardization of EV characterization so that all studies image EVs, quantitate protein concentration, identify the presence of EV protein markers and contaminants, and measure EV particle size and concentration is suggested. Additionally, the use of RNase, DNase and protease EV membrane protection control experiments is recommended to ensure that the cargo being investigated is truly EV associated. Overall, diverse methodology for EV isolation is advantageous as it can support different sample types and volumes. Nevertheless, EV characterization is crucial and should be performed in a rigorous manor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".