Rapid Detection and Trapping of Extracellular Vesicles by Electrokinetic Concentration for Liquid Biopsy on Chip
Bibliographic record
Abstract
Exosomes have gained immense importance since their proteomic and genetic contents could potentially be used for disease diagnostics, monitoring of cancer progression, metastasis, and drug efficacy. However, establishing the clinical utility of exosomes has been restricted due to small sizes and high sample loss from extensive sample preparation. Sample loss is particularly critical for body fluids limited in volume and difficult to access, e.g., cerebrospinal fluid. We present a microfluidic technique that locally enhances the concentration of extracellular vesicles extracted from MDA-MB-231 human breast cancer cell lines by using an ion concentration polarization (ICP)-based electrokinetic concentrator. Our design incorporates a trapping mechanism near the conductive polymer membrane; therefore, we can preconcentrate and capture extracellular vesicles simultaneously. Compared with standard fluorescence detection, our method increased the limit of detection (LOD) of extracellular vesicles by two orders of magnitude in 30 min. Our concentrator increased the extracellular vesicle concentration for 5.0 × 10⁷ particles/1 mL (LOD), 5.0 × 10⁸ particles/1 mL, and 5.0 × 10⁸ particles/1 mL by ~100-fold each within 30 min using 45 V. This study demonstrates an alternative platform to simultaneously preconcentrate and capture extracellular vesicles that can be incorporated as part of a liquid biopsy-on-a-chip system for the detection of exosomal biomarkers and analysis of their contents for early cancer diagnosis.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".