Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Western São Paulo – UNOESTE-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorPereira, Viviane Ribas-
Autor(es): dc.creatorPereira, Danillo Roberto-
Autor(es): dc.creatorde Melo Tavares Vieira, Kátia Cristina-
Autor(es): dc.creatorRibas, Vitor Pereira-
Autor(es): dc.creatorConstantino, Carlos José Leopoldo [UNESP]-
Autor(es): dc.creatorAntunes, Patrícia Alexandra-
Autor(es): dc.creatorFavareto, Ana Paula Alves-
Data de aceite: dc.date.accessioned2022-02-22T00:27:56Z-
Data de disponibilização: dc.date.available2022-02-22T00:27:56Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2019-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s11356-019-06407-0-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/199641-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/199641-
Descrição: dc.descriptionDifenoconazole is a fungicide extensively used in agriculture. The aim of this study was to evaluate the effects of difenoconazole fungicide on the sperm quality of rats. Wistar rats were divided into four groups: control and exposed to 5 (D5), 10 (D10), or 50 mg−1 kg bw−1day (D50) of difenoconazole for 30 days, by gavage. Classical sperm parameters and surface-enhanced Raman scattering (SERS) were performed. Progressive motility, acrosomal integrity, and percentage of morphologically normal spermatozoa were reduced in the D10 and D50 groups in comparison with the control group. Sperm viability was reduced only in the D50 group. Sperm number in the testis and caput/corpus epididymis and daily sperm production were reduced in the three exposed groups. SERS measurements showed changes in the spectra of spermatozoa from D50 group, suggesting DNA damage. In addition, machine learning (ML) methods were used to evaluate the performance of three classification algorithms (artificial neural network—ANN, K-nearest neighbors—K-NN, and support vector machine—SVM) in the identification task of the groups exposed to difenoconazole. The results obtained by ML algorithms were very promising with accuracy ≥ 90% and validated the hypothesis of the exposure to difenoconazole reduces sperm quality. In conclusion, exposure of rats to different doses of the fungicide difenoconazole may impair sperm quality, with a recognizable classification pattern of exposure groups.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionGraduate Program in Environment and Regional Development University of Western São Paulo – UNOESTE-
Descrição: dc.descriptionCollege of Science Letters and Education from Presidente Prudente – FACLEPP University of Western São Paulo – UNOESTE-
Descrição: dc.descriptionSchool of Technology and Applied Sciences São Paulo State University (UNESP) Campus Presidente Prudente-
Descrição: dc.descriptionSchool of Technology and Applied Sciences São Paulo State University (UNESP) Campus Presidente Prudente-
Descrição: dc.descriptionFAPESP: 2013/14262-7-
Descrição: dc.descriptionFAPESP: 2014/11410-8-
Formato: dc.format35253-35265-
Idioma: dc.languageen-
Relação: dc.relationEnvironmental Science and Pollution Research-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial intelligence-
Palavras-chave: dc.subjectFungicide-
Palavras-chave: dc.subjectRaman spectroscopy-
Palavras-chave: dc.subjectRat-
Palavras-chave: dc.subjectReproduction-
Palavras-chave: dc.subjectSpermatozoa-
Título: dc.titleSperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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