TF-Test techniques for the laboratory diagnosis of gastrointestinal parasites of humans and animals

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Inacio, Sandra Valeria ; Gomes, Jancarlo Ferreira ; Falcao, Alexandre Xavier ; Romualdo da Silva, Debora Regina ; Nagata, Walter Bertequini ; Nagase Suzuki, Celso Tetsuo ; dos Santos, Bianca Martins ; Soares, Felipe Augusto ; Nery Loiola, Saulo Hudson ; Benitez, Aline do Nascimento ; Rosa, Stefani Laryssa ; Saraiva Bresciani, Katia Denise

• Document type: Journal article
• Source: VETERINARIA ITALIANA; v. 58, n. 2, p. 7-pg., 2022-01-01.

Abstract

Intestinal parasites inhabit the intestinal tract of humans and animals, causing damages whose severity depends on several factors related to the parasite and the host. Immunocompromised individuals are more likely to develop severe forms of parasitic infestation. The diagnosis of the gastrointestinal parasitosis is mainly performed by the examination of the feces, which consists of the direct visualization and identification of the parasites eliminated through the feces. These tests are generally low sensitive and the microscope slides contain a large number of impurities, which can impair the result of the diagnosis. In order to improve the diagnostic accuracy, a new parasitological technique called Three Fecal Test (TF-Test) was developed. To further improve its diagnostic accuracy, few modifications of the original protocols have been made with the years. In this study the performance of these new techniques to detect gastrointestinal parasites in human and animal fecal samples was described and discussed in relation to the performance of other conventional coprological tests. It could be concluded that the TF-Test conventional and modified can be used for the diagnosis of several human and animal parasites, with satisfactory results. (AU)

• FAPESP's process: 17/14189-9 - Automated diagnosis of gastrointestinal parasite images of dogs and cats
• Grantee: Sandra Valéria Inácio
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants