An Extracellular Vesicle (EV) Paper Strip for Rapid and Convenient Estimation of EV Concentration
Bibliographic record
Abstract
Extracellular vesicles (EVs) have emerged as promising biomarkers and therapeutic agents, yet their quantification remains technically challenging due to the limitations of conventional methods. Here, a low-cost, fluorescence-based, paper-strip immunoassay is presented for rapid and semi-quantitative estimation of EV concentration, inspired by pH strips. The assay utilizes nitrocellulose membranes functionalized with capture antibodies (anti-CD63, CD9, CD81) and fluorescent dye (ExoBrite™) for EV detection. Systematic optimization of assay parameters-including dye application sequence, incubation time, antibody configuration, and dye concentration-revealed that labeling EVs with dye and incubating on the nitrocellulose paper strips for 20 min yielded the strongest and most reproducible signal. A 200× dilution of ExoBrite™ dye was determined to provide the best balance between sensitivity and specificity. A standard curve generated through twofold serial dilution of EVs from ovarian cancer cell culture medium confirmed a positive, concentration-dependent fluorescence response, establishing a usable dynamic range. Compared to existing technologies, this platform enables fast, simple-to-implement EV quantification using minimal sample volume and equipment. The simplicity and scalability of the method offer strong potential for use in clinical diagnostics and EV research applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".