Quantitative and Multiplex Detection of Extracellular Vesicle‐Derived MicroRNA via Rolling Circle Amplification within Encoded Hydrogel Microparticles
Bibliographic record
Abstract
Abstract Extracellular vesicle‐derived microRNA (EV‐miRNA) represent a promising cancer biomarker for disease diagnosis and monitoring. However, existing techniques to detect EV‐miRNA rely on complex, bias‐prone strategies, and preprocessing steps, making absolute quantification highly challenging. This work demonstrates the development and application of a method for quantitative and multiplex detection of EV‐miRNA, via rolling circle amplification within encoded hydrogel particles. By a one‐pot extracellular vesicle lysis and microRNA capture step, the bias and losses associated with standard RNA extraction techniques is avoided. The system offers a large dynamic range (3 orders of magnitude), ease of multiplexing, and a limit of detection down to 2.3 zmol (46 × 10 −18 m ), demonstrating its utility in clinical applications based on liquid biopsy tests. Furthermore, orthogonal measurements of EV concentrations coupled with the direct, absolute quantification of miRNA in biological samples results in quantitative measurements of miRNA copy numbers per volume sample, and per extracellular vesicle.
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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 | 0.000 | 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".