A novel rapid visual nucleic acid detection technique for tick-borne encephalitis virus by combining RT-recombinase-aided amplification and CRISPR/Cas13a coupled with a lateral flow dipstick
Bibliographic record
Abstract
Tick-borne encephalitis virus (TBEV), a zoonotic pathogen, can cause severe neurological complications and fatal outcomes in humans. Early diagnosis of TBEV infection is crucial for clinical practice. Although serological assays are frequently employed for detection, the lack of antibodies in the early stages of infection and the cross-reactivity of antibodies limit their efficacy. Conventional molecular diagnostic methods such as RT-qPCR can achieve early and accurate identification but require specialized instrumentation and professionals, hindering their application in resource-limited areas. Our study developed a rapid and visual TBEV molecular detection method by combining RT-recombinase-aided amplification, the CRISPR/Cas13a system, and lateral flow dipsticks. The diagnostic sensitivity of this method is 50 CFU/ml, with no cross-reactivity with a variety of viruses. The detection can be carried out within 1 h at a temperature between 37 and 42 °C, and the results can be visually determined without the need for complex instruments and professionals. Subsequently, this assay was used to analyze clinical samples from 15 patients suspected of TBEV infection and 10 healthy volunteers, and its sensitivity and specificity reached 100 %, which was consistent with the results of RT-qPCR. These results indicate that this new method can be a promising point-of-care test for the diagnosis of tick-borne encephalitis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".