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Evaluation and standardization of molecular diagnostic techniques to differentiate Mycobacterium bovis from isolated bovine lymphonods in the central-west region of the state of São Paulo

Abstract

Bovine tuberculosis (TBb), caused by Mycobacterium bovis, is a chronic infectious disease that has a wide distribution worldwide, affects humans, domestic and wild animals, predisposing to great economic losses, trade restrictions and public health problems. Mycobacterium tuberculosis Complex species have 99.9% genetic identity, genotypic similarity that can be identified by DNA hybridization, by the identity of enzymes, by common elements in DNA, such as the 16SrRNA sequence, the IS6110 insertion element, and a succession of repetitive bases (Direct Repeated). Some diagnostic techniques have been developed to overcome limitations, since there is no diagnostic method that has an absolute efficacy, those currently used in the routine are slow, expensive and imprecise. Quantitative Polymerase Chain Reaction (qPCR) has the potential to change the current paradigm of mycobacteria identification, decreasing the response time for identification from weeks to hours, improving diagnosis, sensitivity and specificity. Currently, there is no qPCR protocol to diagnose TBb in bovine carcasses, subjectively suspected of this disease in slaughterhouses, accepted and approved by official and competent bodies. The present study focuses on the evaluation of diagnostic techniques to differentiate strains of Mycobacterium bovis by means of qPCR from lymph node samples from bovine animals suspected of TBb from fridges in the central-western region of the state of São Paulo; the development of a sample collection and preservation protocol, the standardization of qPCR to diagnose TBb in cattle, as well as the cost and benefit of such protocol more adapted to the Brazilian reality. Thus, the main objective will be to establish a more specific, sensitive and effective diagnostic method to diagnose tuberculosis in cattle. (AU)

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VEICULO: TITULO (DATA)