EVQuant; high-throughput quantification and characterization of extracellular vesicle (sub)populations
Bibliographic record
Abstract
Abstract Extracellular vesicles (EVs) reflect the cell of origin in terms of nucleic acids and protein content. They are found in biofluids and represent an ideal liquid biopsy biomarker source for many diseases. Unfortunately, clinical implementation is limited by available technologies for EV analysis. We have developed a simple, robust and sensitive microscopy-based high-throughput assay (EVQuant) to overcome these limitations and allow widespread use in the EV community. The EVQuant assay can detect individual immobilized EVs as small as 35 nm and determine their concentration in biofluids without extensive EV isolation or purification procedures. It can also identify specific EV subpopulations based on combinations of biomarkers and is used here to identify prostate-derived urinary EVs as CD9-/CD63+. Moreover, characterization of individual EVs allows analysis of their size distribution. The ability to identify, quantify and characterize EV (sub-)populations in high-throughput substantially extents the applicability of the EVQuant assay over most current EV quantification assays.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".