A one-pot method for universal Dengue virus detection by combining RT-RPA amplification and CRISPR/Cas12a assay
Bibliographic record
Abstract
Dengue Virus (DENV) is a life-threatening pathogen leading to dengue fever, which brings about huge public health challenges globally. However, traditional detection methods currently fail to meet the increasing demands of clinic practice in terms of speed, simplicity, and accuracy. To address these limitations, we developed a novel, rapid, and highly sensitive diagnostic method for universal DENV detection by integrating recombinase polymerase amplification (RPA) assay and the Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) and associated (Cas) protein 12a (CRISPR/Cas12a) system into one-pot. This approach achieves exceptional sensitivity and specificity for DENV detection, with the entire process completed within 40 min, without the need for sophisticated equipment. The limit of detection (LOD) was determined to be 91.7 copies/test. Using this one-pot RT-RPA CRISPR/Cas12a detection system, all four serotypes of DENV (1 to 4) were successfully identified. In terms of specificity, the assay accurately detected DENV-infected positive samples without cross-reactivity with four other interfering viruses-infected samples (VSV, SeV, HSV-1 and IAV). Furthermore, we established a universal DENV RT-RPA-CRISPR/Cas12a-lateral flow dipstick (LFD) platform, which successfully identified all four serotypes of DENV with a sensitivity of approximately 250 copies/test. Collectively, our method not only provides a robust alternative for universal DENV detection but also offers valuable insights for the identification of other viruses.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".