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Accuracy of molecular diagnostic assays for detection of Mycobacterium bovis: A systematic review and meta-analysis

牛分枝杆菌 计算生物学 生物 荟萃分析 肺结核 医学 结核分枝杆菌 病理
作者
Lerato Tshepiso Mabe,Mpho Muthevhuli,Oriel Thekisoe,Essa Suleman
出处
期刊:Preventive Veterinary Medicine [Elsevier BV]
卷期号:226: 106190-106190 被引量:4
标识
DOI:10.1016/j.prevetmed.2024.106190
摘要

Bovine tuberculosis (bovine TB) is a chronic wasting disease of cattle caused primarily by Mycobacterium bovis. Controlling bovine TB requires highly sensitive, specific, quick, and reliable diagnostic methods. This systematic review and meta-analysis evaluated molecular diagnostic tests for M. bovis detection to inform the selection of the most viable assay. On a per-test basis, loop-mediated isothermal amplification (LAMP) showed the highest overall sensitivity of 99.0% [95% CI: 86.2%-99.9%] and specificity of 99.8% [95% CI: 96.2%-100.00%]. Quantitative real-time polymerase chain reaction (qPCR) outperformed conventional PCR and nested PCR (nPCR) with a diagnostic specificity of 96.6% [95% CI: 88.9%-99.0%], while the diagnostic sensitivity of 70.8% [95% CI: 58.6-80.5%] was comparable to that of nPCR at 71.4% [95% CI: 60.7-80.2%]. Test sensitivity was higher with the input of milk samples (90.9% [95% CI: 56.0%-98.7%]), while specificity improved with tests based on major M. bovis antigens (97.8% [95% CI: 92.3%-99.4%]), the IS6110 insertion sequence (95.4% [95% CI: 87.6%-98.4%]), and the RD4 gene (90.7% [95% CI: 52.2%-98.9%]). The design of the currently available molecular diagnostic assays, while mostly based on nonspecific gene targets, prevents them from being accurate enough to diagnose M. bovis infections in cattle, despite their promise. Future assay development should focus on the RD4 region since it is the only target identified by genome sequence data as being distinctive for detecting M. bovis. The availability of a sufficiently accurate diagnostic test combined with the routine screening of milk samples can decrease the risk of zoonotic transmissions of M. bovis.
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