Separation and isolation of CD9-positive extracellular vesicles from plasma using flow cytometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extracellular vesicles (EVs) are nanosized (∼30-1000 nm) lipid-enclosed particles released by a variety of cell types. EVs are found in biological fluids and are considered a promising material for disease detection and monitoring. Given their nanosized properties, EVs are difficult to isolate and study. In complex biological samples, this difficulty is amplified by other small particles and contaminating proteins making the discovery and validation of EV-based biomarkers challenging. Developing new strategies to isolate EVs from complex biological samples is of significant interest. Here, we evaluate the utility of flow cytometry to isolate particles in the nanoscale size range. Flow cytometry calibration was performed and 100 nm nanoparticles and ∼124 nm virus were used to test sorting capabilities in the nanoscale size range. Next, using blood plasma, we assessed the capabilities of flow cytometry sorting for the isolation of CD9-positive EVs. Using flow cytometry, CD9-positive EVs could be sorted from pre-enriched EV fractions and directly from plasma without the need for any EV pre-enrichment isolation strategies. These results demonstrate that flow cytometry can be employed as a method to isolate subpopulations of EVs from biological samples.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it