Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Subject: search.filters.subject.Antibodies, Bacterial

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    A novel Bayesian Latent Class Model (BLCM) evaluates multiple continuous and binary tests: A case study for Brucella abortus in dairy cattle.
    (Elsevier B.V., 2024-03-01) Wang Y; Vallée E; Compton C; Heuer C; Guo A; Wang Y; Zhang Z; Vignes M
    Bovine brucellosis, primarily caused by Brucella abortus, severely affects both animal health and human well-being. Accurate diagnosis is crucial for designing informed control and prevention measures. Lacking a gold standard test makes it challenging to determine optimal cut-off values and evaluate the diagnostic performance of tests. In this study, we developed a novel Bayesian Latent Class Model that integrates both binary and continuous testing outcomes, incorporating additional fixed (parity) and random (farm) effects, to calibrate optimal cut-off values by maximizing Youden Index. We tested 651 serum samples collected from six dairy farms in two regions of Henan Province, China with four serological tests: Rose Bengal Test, Serum Agglutination Test, Fluorescence Polarization Assay, and Competitive Enzyme-Linked Immunosorbent Assay. Our analysis revealed that the optimal cut-off values for FPA and C-ELISA were 94.2 mP and 0.403 PI, respectively. Sensitivity estimates for the four tests ranged from 69.7% to 89.9%, while specificity estimates varied between 97.1% and 99.6%. The true prevalences in the two study regions in Henan province were 4.7% and 30.3%. Parity-specific odds ratios for positive serological status ranged from 1.2 to 2.2 for different parity groups compared to primiparous cows. This approach provides a robust framework for validating diagnostic tests for both continuous and discrete tests in the absence of a gold standard test. Our findings can enhance our ability to design targeted disease detection strategies and implement effective control measures for brucellosis in Chinese dairy farms.
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    Validation of an Indirect Immunofluorescence Assay and Commercial Q Fever Enzyme-Linked Immunosorbent Assay for Use in Macropods
    (American Society for Microbiology, 2022-07) Tolpinrud A; Stenos J; Chaber A-L; Devlin JM; Herbert C; Pas A; Dunowska M; Stevenson MA; Firestone SM; Barrs, VR
    Kangaroos are considered to be an important reservoir of Q fever in Australia, although there is limited knowledge on the true prevalence and distribution of coxiellosis in Australian macropod populations. Serological tests serve as useful surveillance tools, but formal test validation is needed to be able to estimate true seroprevalence rates, and few tests have been validated to screen wildlife species for Q fever. In this study, we modified and optimized a phase-specific indirect immunofluorescence assay (IFA) for the detection of IgG antibodies against Coxiella burnetii in macropod sera. The assay was validated against the commercially available ID Screen Q fever indirect multispecies enzyme-linked immunosorbent assay (ELISA) kit (IDVet, Grabels, France) to estimate the diagnostic sensitivity and specificity of each assay, using Bayesian latent class analysis. A direct comparison of the two tests was performed by testing 303 serum samples from 10 macropod populations from the east coast of Australia and New Zealand. The analysis indicated that the IFA had relatively high diagnostic sensitivity (97.6% [95% credible interval [CrI], 88.0 to 99.9]) and diagnostic specificity (98.5% [95% CrI, 94.4 to 99.9]). In comparison, the ELISA had relatively poor diagnostic sensitivity (42.1% [95% CrI, 33.7 to 50.8]) and similar diagnostic specificity (99.2% [95% CrI, 96.4 to 100]) using the cutoff values recommended by the manufacturer. The estimated true seroprevalence of C. burnetii exposure in the macropod populations included in this study ranged from 0% in New Zealand and Victoria, Australia, up to 94.2% in one population from New South Wales, Australia.