CRISPR/Cas13a-Responsive and RNA-Bridged DNA Hydrogel Capillary Sensor for Point-of-Care Detection of RNA
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Disease diagnostics and surveillance increasingly highlight the importance of portable, cost-effective, and sensitive point-of-care (POC) detection of nucleic acids. Here, we report a CRISPR/Cas13a-responsive and RNA-bridged DNA hydrogel capillary sensor for the direct and visual detection of specific RNA with high sensitivity. The capillary sensor was simply prepared by loading RNA-cross-linking DNA hydrogel film (∼0.2 mm ± 0.02 mm) at the end of a capillary. When CRISPR/Cas13a specifically recognizes the target RNA, the RNA bridge in the hydrogel film is cleaved by the trans -cleavage activity of CRISPR/Cas13a, increasing the permeability of the hydrogel film. Different concentrations of target RNA activate different amounts of Cas13a, cleaving different amounts of the RNA bridge in the hydrogel and causing corresponding changes in the permeability of the hydrogel. Therefore, samples containing different amounts of the target RNA travel to different distances in the capillary. Visual reading of the distance provides quantitative detection of the RNA target without the need for any nucleic acid amplification or auxiliary equipment. The technique was successfully used for the determination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in clinical nasopharyngeal (NP) swab and saliva samples. Easily quantifiable distance using a ruler eliminates the need for any optical or electrochemical detection equipment, making this assay potentially useful for POC and on-site 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 | 0.000 | 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 | 0.001 | 0.000 |
| 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".