Visual recombinase aided amplification technology for detecting feline coronavirus
Bibliographic record
Abstract
Feline Coronavirus (FCoV) is responsible for causing intestinal lesions and potentially fatal systemic infections in cats, with a worldwide prevalence. Currently, there is no effective vaccine available, making early diagnosis essential for treatment. Although RT-PCR detection is known for its high specificity and accuracy, the method involves complex experimental procedures and necessitates costly equipment, which restricts its broader clinical use. In this study, we utilized Recombinase Aided Amplification (RAA) technology in conjunction with a nucleic acid-colloidal gold immunochromatographic test strip to establish a visual detection method that can rapidly and accurately identify FCoV and differentiate between its two serotypes, serotype I and serotype II. Primers and probes were designed based on conserved sequences from the N gene and the S1 gene. Under isothermal conditions at 39°C, results can be obtained in just 30 minutes. This method demonstrates no cross-reactivity with other prevalent infectious viruses in felines, such as feline calicivirus (FCV), feline panleukopenia virus (FPV), and feline herpesvirus type 1 (FHV-1). In comparison to qRT-PCR, which has a detection limit of approximately 100 copies/μL, the RAA method exhibits significantly enhanced sensitivity. The detection limits for the FCoV N gene and the S1 gene serotypes I and II were determined to be 15.5 copies/μL, 1.97 copies/μL, and 1.46 copies/μL, respectively. Preliminary clinical applications have shown that the results align perfectly with those obtained from RT-PCR detection, achieving 100 % consistency. In summary, this novel detection method offers high accuracy, a brief reaction time, robust specificity, elevated sensitivity, and is straightforward and convenient to implement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".