Evaluation of an Immunoassay-Based Algorithm for Screening and Identification of Giardia and Cryptosporidium Antigens in Human Faecal Specimens from Saudi Arabia
An immunoassay-based algorithm, involving three commercial kits, was introduced and evaluated for screening and identification of Giardia/Cryptosporidium antigens in human stool specimens. Initially, Giardia/Cryptosporidium Chek kit (TechLab), an enzyme-linked immunosorbent assay (ELISA), was adopte...
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Veröffentlicht in: | Journal of Parasitology Research 2014, Vol.2014 (2014), p.52-57 |
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Sprache: | eng |
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Zusammenfassung: | An immunoassay-based algorithm, involving three commercial kits, was introduced and evaluated for screening and identification of Giardia/Cryptosporidium antigens in human stool specimens. Initially, Giardia/Cryptosporidium Chek kit (TechLab), an enzyme-linked immunosorbent assay (ELISA), was adopted for screening. The ELISA-positive reactions were subsequently characterised by RIDA Quick Giardia and RIDA Quick Cryptosporidium immunochromatographic kits (R-Biopharm). A gold standard test comprising PCR and microscopy was used for preparing control samples. Performance of individual kits was tested against these samples which included 50 Giardia-positive, 40 Cryptosporidium-positive, and 70 Cryptosporidium/Giardia-negative. For Cryptosporidium, specificities of the ELISA and RIDA Quick Cryptosporidium kits were 95.71% and 100%, respectively. Both kits demonstrated sensitivity of 95%. For Giardia, the ELISA and RIDA Quick Giardia kits showed sensitivities of 100% and 97.5%, respectively. Specificities obtained by the ELISA and RIDA Quick Giardia were 95.7% and 100%, respectively. Based on the results of two reference PCRs, on 250 random samples, the algorithm exhibited sensitivity, specificity, positive predictive value, and negative predictive value of 97.06%, 100.00%, 100.00%, and 98.91%, respectively. In conclusion, this immunoassay-based algorithm can be used as routine test in diagnostic laboratories for screening and identification of a large number of samples. |
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ISSN: | 2090-0023 2090-0031 |
DOI: | 10.1155/2014/213745 |