CandyCollect: An Open-Microfluidic Device for the Direct Capture and Analysis of Salivary-Extracellular Vesicles
Bibliographic record
Abstract
Extracellular vesicles (EVs) are promising biomarkers for disease detection using a 'liquid biopsy' approach, in which they are enriched and analyzed directly from biofluids. However, implementing EV biomarker technologies in the clinic remains limited by the need for practical and patient-centric biofluid collection methods that are compatible with downstream EV processing and analysis. While saliva offers a non-invasive source of EVs, its complexity and heterogeneity-cells, debris, and other non-EV proteins-can present hurdles when using traditional analytical platforms. Here, we present the CandyCollect, a lollipop-inspired sampling device with open microfluidic channels, as a patient-friendly approach for rapid salivary EV capture. CandyCollect simplifies sample preparation by effectively pre-concentrating EVs in oxygen-plasma treated open microfluidic channels. In this proof-of-principle study, we show that following a 3-5 minute-oral sampling period, EVs collected by the CandyCollect can be released with high purity within minutes and subsequently quantified and analyzed for cargo content. We observed consistent EV capture across repeated collections within individuals and expected variability across healthy participants. Additionally, single and pooled collections of EVs from a healthy participant resulted in a concordant protein profile. Overall, the CandyCollect is a new platform for rapid, non-invasive salivary EV collection and analysis for clinical diagnostics.
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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 | 0.000 | 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 | 0.000 |
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 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".