Development of New Staining Procedures for Diagnosing Cryptosporidium spp. In Fecal Samples by Computerized Image Analysis

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Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorLoiola, Saulo Hudson Nery-
Autor(es): dc.creatorGalvão, Felipe Lemes-
Autor(es): dc.creatorSantos, Bianca Martins Dos-
Autor(es): dc.creatorRosa, Stefany Laryssa-
Autor(es): dc.creatorSoares, Felipe Augusto-
Autor(es): dc.creatorInácio, Sandra Valéria-
Autor(es): dc.creatorSuzuki, Celso Tetsuo Nagase-
Autor(es): dc.creatorSabadini, Edvaldo-
Autor(es): dc.creatorBresciani, Katia Denise Saraiva-
Autor(es): dc.creatorFalcão, Alexandre Xavier-
Autor(es): dc.creatorGomes, Jancarlo Ferreira-
Data de aceite: dc.date.accessioned2025-08-21T17:04:16Z-
Data de disponibilização: dc.date.available2025-08-21T17:04:16Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1017/S1431927621012903-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/233727-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/233727-
Descrição: dc.descriptionInterpretation errors may still represent a limiting factor for diagnosing Cryptosporidium spp. oocysts with the conventional staining techniques. Humans and machines can interact to solve this problem. We developed a new temporary staining protocol associated with a computer program for the diagnosis of Cryptosporidium spp. oocysts in fecal samples. We established 62 different temporary staining conditions by studying 20 experimental protocols. Cryptosporidium spp. oocysts were concentrated using the Three Fecal Test (TF-Test®) technique and confirmed by the Kinyoun method. Next, we built a bank with 299 images containing oocysts. We used segmentation in superpixels to cluster the patches in the images; then, we filtered the objects based on their typical size. Finally, we applied a convolutional neural network as a classifier. The trichrome modified by Melvin and Brooke, at a concentration use of 25%, was the most efficient dye for use in the computerized diagnosis. The algorithms of this new program showed a positive predictive value of 81.3 and 94.1% sensitivity for the detection of Cryptosporidium spp. oocysts. With the combination of the chosen staining protocol and the precision of the computational algorithm, we improved the Ova and Parasite exam (O&P) by contributing in advance toward the automated diagnosis.-
Descrição: dc.descriptionSchool of Medical Sciences University of Campinas, 126 Tessália Vieira de Camargo St. São Paulo-
Descrição: dc.descriptionUniversity of Campinas Institute of Computing, 573, IC-3,5 Saturnino de Brito St. São Paulo-
Descrição: dc.descriptionSchool of Veterinary Medicine São Paulo State University (UNESP), 793 Clóvis Pestana St. São Paulo-
Descrição: dc.descriptionUniversity of Campinas Institute of Chemistry, 126 Josué de Castro St. São Paulo-
Descrição: dc.descriptionSchool of Veterinary Medicine São Paulo State University (UNESP), 793 Clóvis Pestana St. São Paulo-
Formato: dc.format1-11-
Idioma: dc.languageen-
Relação: dc.relationMicroscopy and Microanalysis-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCryptosporidium spp.-
Palavras-chave: dc.subjectfeces-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectoocysts-
Palavras-chave: dc.subjectstain-
Título: dc.titleDevelopment of New Staining Procedures for Diagnosing Cryptosporidium spp. In Fecal Samples by Computerized Image Analysis-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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