Microparticle parking and isolation for highly sensitive microRNA detection
Bibliographic record
Abstract
Isolating small objects, such as particles, cells, and molecules, in individual aqueous droplets is useful for chemical and biological assays. We have developed a simple microfluidic platform to immobilize (park) microparticles at defined locations, and isolate particles in monodisperse droplets surrounded by immiscible oil. While conventional methods can only achieve stochastic encapsulation of objects within larger droplets, our in situ method ensures that a single particle is entrapped in a similar-sized droplet, with ∼95% yield for parking and isolation. This enables time-lapse studies of reactions in confined volumes and can be used to perform enzymatic amplification of a desired signal to improve the sensitivity of diagnostic assays. To demonstrate the utility of our technique, we perform highly sensitive, multiplexed microRNA detection by isolating encoded, functional hydrogel microparticles in small aqueous droplets. Non-fouling hydrogel microparticles are attractive for microRNA detection due to favorable capture kinetics. By encapsulating these particles in droplets and employing a generalizable enzyme amplification scheme, we demonstrate an order of magnitude improvement in detection sensitivity compared to a non-amplified assay.
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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) | 0.000 | 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) | 0.001 | 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".