Accelerating Cleavage Activity of CRISPR-Cas13 System on a Microfluidic Chip for Rapid Detection of RNA
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide It is extremely advantageous to detect nucleic acid levels in the early phases of disease management; such early detection facilitates timely treatment, and it can prevent altogether certain cancers and infectious diseases. A simple, rapid, and versatile detection platform without enzymatic amplification for both short and long sequences would be highly desirable in this regard. Our study addresses this need by introducing IMACC, an I CP-based M icrofluidic A ccelerator C ombined with C RISPR, for amplification-free nucleic acid detection. It exploits electrokinetically induced ion concentration polarization (ICP) to concentrate target nucleic acids and CRISPR reagents near the depletion zone boundary within a microfluidic channel. This localized accumulation accelerates the CRISPR-guided promiscuous cleavage of reporter molecules while enhancing their fluorescence signals simultaneously. Simultaneous accumulation of RNA and ribonucleoproteins (RNP) in confined spaces was validated experimentally and numerically, showing overlapping regions. IMACC enabled detection of miRNA-21 (22 bp) down to 10 pM within 2 min of ICP. IMACC ensured CRISPR specificity (single mismatch ( N = 1) sensitivity) during ICP, as shown by off-target and mismatch sequence experiments. IMACC was applied to long RNA samples (i.e., SARS-CoV-2), but it statistically remained challenging at this point due to nonlinear intensity trends with copy numbers and large deviations. IMACC enabled rapid detection of short RNAs such as microRNAs using only basic CRISPR reagents in a single microfluidic channel, eliminating the need for extra enzymes or buffer sets, streamlining workflow and reducing turnaround time. IMACC has the potential to advance CRISPR diagnostics and holds promise for improved detection and future prescreening 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 | 0.000 |
| 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".