Figure 1. Design principles of the GPV RPA-PfAgo platform and RPA optimization strategies. (A) Conceptual design of the GPV RPA-PfAgo detection platform. (B) Assessment of the selection and repeatability of the RPA primers. (C) Optimization of the reaction conditions for RPA, including MgAc (280 mM): 2.0, 2.2, 2.5, 2.8, and 3.0 μL. Reaction temperatures: 35, 37, 39, 42, 45°C. Reaction times: 10, 15, 20, 25, and 30 min. Band intensities were quantified using ImageJ software. NC: negative control (ddH2O); M: DNA molecular weight marker.
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Figure 2. Multiparametric optimization of the GPV RPA-PfAgo detection platform. (A) Selection of gDNA candidates. (B) gDNA concentration tuning. (C) MnCl2 concentration adjustment. (D) Probe concentration refinement. (E) PfAgo concentration calibration. (F) RPA product concentration fine-tuning. The fluorescence intensity (× 104) was plotted against time (min). NC, negative control; Con, concentration; FAM, fluorescein amidite; ROX, rhodamine X.
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Figure 3. Assessment of the sensitivity, specificity, and repeatability of the optimized GPV RPA-PfAgo platform. (A) Sensitivity tested across a range of plasmid DNA concentrations (3.89 × 108 to 3.89 × 10° copies/μL). (B) Specificity confirmed by testing against various pathogens. (C) Repeatability was established through multiple independent experiments and visualized using principal component analysis (PCA) and a clustering heatmap. The repeatability of 3.89 × 108 copies/μL plasmid DNA is presented here, whereas the repeatability of 3.89 × 102 copies/μL plasmid DNA is shown in Supplementary Figure 1.
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Figure 4. Clinical validation of the GPV RPA-PfAgo detection platform and comparison with qPCR and PCR methods. (A) GPV RPA-PfAgo assay results for 46 clinical samples. (B) qPCR results for the same set of samples. (C) PCR amplification outcomes for these samples. (D) Comparative analysis of GPV detection efficiency with RPA-PfAgo (FAM, ROX), qPCR, and PCR. RPA-PfAgo and qPCR data reflect end-point fluorescence intensities, while PCR band intensities were quantified via ImageJ software (Schroeder et al., 2021).
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In conclusion, the RPA-PfAgo system heralds a significant transformation in diagnostic methodology for GPV, shifting away from traditional techniques towards more advanced molecular diagnostics. This innovative system meets the critical demand for swift, precise, and onsite diagnostic capabilities and is crucial for combating emergent viral diseases that pose a risk to poultry production worldwide. Owing to its streamlined operation, robust specificity, and elevated sensitivity, the RPA-PfAgo system is an invaluable resource for the poultry sector and animal health management at large. Prospective research endeavors will concentrate on enhancing this technology, broadening its diagnostic scope to encompass an extensive array of avian pathogens and incorporating it into an all-encompassing disease surveillance framework. These developments are poised to equip poultry farmers and veterinarians with indispensable tools for the expedited detection and management of infectious diseases, thereby enhancing avian health and fortifying the sustainability of worldwide poultry production.
| https://www.sciencedirect.com/science/article/pii/S003257912400720X?via%3Dihub
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Figure 1. Design principles of the GPV RPA-PfAgo platform and RPA optimization strategies. (A) Conceptual design of the GPV RPA-PfAgo detection platform. (B) Assessment of the selection and repeatability of the RPA primers. (C) Optimization of the reaction conditions for RPA, including MgAc (280 mM): 2.0, 2.2, 2.5, 2.8, and 3.0 μL. Reaction temperatures: 35, 37, 39, 42, 45°C. Reaction times: 10, 15, 20, 25, and 30 min. Band intensities were quantified using ImageJ software. NC: negative control (ddH2O); M: DNA molecular weight marker.
Figure 2. Multiparametric optimization of the GPV RPA-PfAgo detection platform. (A) Selection of gDNA candidates. (B) gDNA concentration tuning. (C) MnCl2 concentration adjustment. (D) Probe concentration refinement. (E) PfAgo concentration calibration. (F) RPA product concentration fine-tuning. The fluorescence intensity (× 104) was plotted against time (min). NC, negative control; Con, concentration; FAM, fluorescein amidite; ROX, rhodamine X.
Figure 3. Assessment of the sensitivity, specificity, and repeatability of the optimized GPV RPA-PfAgo platform. (A) Sensitivity tested across a range of plasmid DNA concentrations (3.89 × 108 to 3.89 × 10° copies/μL). (B) Specificity confirmed by testing against various pathogens. (C) Repeatability was established through multiple independent experiments and visualized using principal component analysis (PCA) and a clustering heatmap. The repeatability of 3.89 × 108 copies/μL plasmid DNA is presented here, whereas the repeatability of 3.89 × 102 copies/μL plasmid DNA is shown in Supplementary Figure 1.
Figure 4. Clinical validation of the GPV RPA-PfAgo detection platform and comparison with qPCR and PCR methods. (A) GPV RPA-PfAgo assay results for 46 clinical samples. (B) qPCR results for the same set of samples. (C) PCR amplification outcomes for these samples. (D) Comparative analysis of GPV detection efficiency with RPA-PfAgo (FAM, ROX), qPCR, and PCR. RPA-PfAgo and qPCR data reflect end-point fluorescence intensities, while PCR band intensities were quantified via ImageJ software (Schroeder et al., 2021).