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Comparison of diagnostic techniques for the detection of Cryptosporidium oocysts in animal samples

  • Marzieh Ezzaty Mirhashemi
  • , Annetta Zintl
  • , Tim Grant
  • , Frances E. Lucy
  • , Grace Mulcahy
  • , Theo De Waal
    • University College Dublin

    Research output: Contribution to journalArticlepeer-review

    39 Citations (Scopus)

    Abstract

    While a large number of laboratory methods for the detection of Cryptosporidium oocysts in faecal samples are now available, their efficacy for identifying asymptomatic cases of cryptosporidiosis is poorly understood. This study was carried out to determine a reliable screening test for epidemiological studies in livestock. In addition, three molecular tests were compared to identify Cryptosporidium species responsible for the infection in cattle, sheep and horses. A variety of diagnostic tests including microscopic (Kinyoun's staining), immunological (Direct Fluorescence Antibody tests or DFAT), enzyme-linked immunosorbent assay (ELISA), and molecular methods (nested PCR) were compared to assess their ability to detect Cryptosporidium in cattle, horse and sheep faecal samples. The results indicate that the sensitivity and specificity of each test is highly dependent on the input samples; while Kinyoun's and DFAT proved to be reliable screening tools for cattle samples, DFAT and PCR analysis (targeted at the 18S rRNA gene fragment) were more sensitive for screening sheep and horse samples. Finally different PCR primer sets targetedat the same region resulted in the preferential amplification of certain Cryptosporidium species when multiple species were present in the sample. Therefore, for identification of Cryptosporidium spp. in the event of asymptomatic cryptosporidiosis, the combination of different 18S rRNA nested PCR primer sets is recommended for further epidemiological applications and also tracking the sources of infection.

    Original languageEnglish
    Pages (from-to)14-20
    Number of pages7
    JournalExperimental Parasitology
    Volume151-152
    DOIs
    Publication statusPublished - 1 Apr 2015

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    Keywords

    • Cattle
    • Cryptosporidium
    • Diagnostic techniques
    • Horse
    • Latent class analysis
    • Molecular methods
    • Sheep

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