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Journal of Parasitology Research
Volume 2014 (2014), Article ID 213745, 6 pages
Research Article

Evaluation of an Immunoassay-Based Algorithm for Screening and Identification of Giardia and Cryptosporidium Antigens in Human Faecal Specimens from Saudi Arabia

1Department of Medical Parasitology, National Liver Institute, Menoufia University, Shebin-El Koum 32513, Menoufia, Egypt
2Department of Medical Laboratory Science, College of Applied Medical Science, Taif University, Taif 2425, Saudi Arabia

Received 25 October 2013; Revised 4 December 2013; Accepted 15 December 2013; Published 29 January 2014

Academic Editor: Alvin A. Gajadhar

Copyright © 2014 Yousry Hawash. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.