KP177R-based visual assay integrating RPA and CRISPR/Cas12a for the detection of African swine fever virus

IntroductionEarly detection of the virus in the environment or in infected pigs is a critical step to stop African swine fever virus (ASFV) transmission. The p22 protein encoded by ASFV KP177R gene has been shown to have no effect on viral replication and virulence and can serve as a molecular marker for distinguishing field virus strains from future candidate KP177R deletion vaccine strains.MethodsThis study established an ASFV detection assay specific for the highly conserved ASFV KP177R gene based on recombinase polymerase amplification (RPA) and the CRISPR/Cas12 reaction system. The KP177R gene served as the initial template for the RPA reaction to generate amplicons, which were recognized by guide RNA to activate the trans-cleavage activity of Cas12a protein, thereby leading to non-specific cleavage of single-stranded DNA as well as corresponding color reaction. The viral detection in this assay could be determined by visualizing the results of fluorescence or lateral flow dipstick (LFD) biotin blotting for color development, and was respectively referred to as fluorescein-labeled RPA-CRISPR/Cas12a and biotin-labeled LFD RPA-CRISPR/Cas12a. The clinical samples were simultaneously subjected to the aforementioned assay, while real-time quantitative PCR (RT-qPCR) was employed as a control for determining the diagnostic concordance rate between both assays.ResultsThe results showed that fluorescein- and biotin-labeled LFD KP177R RPA-CRISPR/Cas12a assays specifically detected ASFV, did not cross-react with other swine pathogens including PCV2, PEDV, PDCoV, and PRV. The detection assay established in this study had a limit of detection (LOD) of 6.8 copies/μL, and both assays were completed in 30 min. The KP177R RPA-CRISPR/Cas12a assay demonstrated a diagnostic coincidence rate of 100% and a kappa value of 1.000 (p < 0.001), with six out of ten clinical samples testing positive for ASFV using both KP177R RPA-CRISPR/Cas12a and RT-qPCR, while four samples tested negative in both assays.DiscussionThe rapid, sensitive and visual detection assay for ASFV developed in this study is suitable for field application in swine farms, particularly for future differentiation of field virus strains from candidate KP177R gene-deleted ASFV vaccines, which may be a valuable screening tool for ASF eradication

I329L protein-based indirect ELISA for detecting antibodies specific to African swine fever virus

African swine fever (ASF) is a disease that causes severe economic losses to the global porcine industry. As no vaccine or drug has been discovered for the prevention and control of ASF virus (ASFV), accurate diagnosis and timely eradication of infected animals are the primary measures, which necessitate accurate and effective detection methods. In this study, the truncated ASFV I329L (amino acids 70–237), was induced using IPTG and expressed in Escherichia coli cells. The highly antigenic viral protein I329L was used to develop an indirect enzyme-linked immunosorbent assay (iELISA), named I329L-ELISA, which cut-off value was 0.384. I329L-ELISA was used to detect 186 clinical pig serum samples, and the coincidence rate between the indirect ELISA developed here and the commercial kit was 96.77%. No cross-reactivity was observed with CSFV, PRRSV, PCV2, or PRV antibody-positive pig sera, indicating good specificity. Both intra- assay and inter-assay coefficients were below 10%, and the detection sensitivity of the iELISA reached 1:3200. In this study, an iELISA for ASFV antibody detection was developed based on the truncated ASFV I329L protein. Overall, the I329L-ELISA is a user-friendly detection tool that is suitable for ASFV antibody detection and epidemiological surveillance

AFCN: An attention‐directed feature‐fusion ConvNeXt network for low‐voltage apparatus assembly quality inspection

Abstract In the production of low‐voltage apparatus, assembly quality inspection is of great relevance for ensuring the final quality of the entire product. With the continuous improvement of production efficiency and people's requirements for production quality, traditional manual inspection methods can no longer meet the quality inspection requirements. In this paper, an Attention‐guided Feature‐fusion ConvNeXt Network (AFCN) for the automated visual inspection is proposed. By embedding the attention mechanism of the Coordinate Attention block into the residual channel of the ConvNeXt block, the position‐aware information and features of the low‐voltage apparatus images can be effectively captured to locate the quality problems. Then, an improved attention feature fusion module is adopted to merge the output features at different stages, which introduces a 3D non‐parameter attention SimAM block and adapts output accordingly. Therefore, this model can capture the key information of the feature map in a coordinated way in terms of channel and position, fully integrating multiscale features and obtaining contour texture information and semantic information of the low‐voltage apparatus. Experiments show the proposed approach can effectively classify defective and normal products