Rapid detection of Chinese sacbrood virus via CRISPR-Cas13a-based lateral flow strips
Bibliographic record
Abstract
Abstract Sacbrood virus (SBV) is one of the most pathogenic honeybee viruses with host specificity and regional variation. The SBV strain infecting the Chinese honeybee (Apis cerana) is known as Chinese sacbrood virus (CSBV). The extensively used CSBV detection methods require professionals and expensive equipment; thus, they are unsuitable for rapid onsite CSBV detection. To achieve early and rapid detection of CSBV, we developed a lateral flow detection (LFD) strip method for CSBV detection via clustered regularly interspaced short palindromic repeats (CRISPR) and the Cas13a technique. On the basis of the conserved CSBV VP2 gene nucleotide region, we designed 3 recombinant enzyme-assisted amplification (RAA) primer pairs and prepared 3 corresponding crRNAs. We investigated key performance metrics, including the sensitivity, specificity, and accuracy of LFD strips. The results demonstrated that the LFD strip based on the optimal combination (primer 2+crRNA 2) presented the lowest detection limit (2.80×101 copies/μL), and this strip could complete CSBV detection within 1 h. Furthermore, this strip exhibited excellent detection specificity, with no cross-reactivity with four other honeybee viruses. A test of 100 clinical samples indicated the feasibility of the LFD method for CSBV detection. A comparison of various CSBV detection methods revealed that the CRISPR-Cas13a-based LFD method was more accurate, efficient, and sensitive than the other methods were, indicating great application prospects in onsite CSBV detection. Our developed method is highly important for preventing and controlling CSBV infection as well as maintaining honeybee health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".