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

Detalhes bibliográficos
Autor(a) principal: Pereira, Viviane Ribas
Data de Publicação: 2019
Outros Autores: Pereira, Danillo Roberto, de Melo Tavares Vieira, Kátia Cristina, Ribas, Vitor Pereira, Constantino, Carlos José Leopoldo [UNESP], Antunes, Patrícia Alexandra, Favareto, Ana Paula Alves
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/s11356-019-06407-0
http://hdl.handle.net/11449/199641
Resumo: Difenoconazole 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.
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spelling Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methodsArtificial intelligenceFungicideRaman spectroscopyRatReproductionSpermatozoaDifenoconazole 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.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Graduate Program in Environment and Regional Development University of Western São Paulo – UNOESTECollege of Science Letters and Education from Presidente Prudente – FACLEPP University of Western São Paulo – UNOESTESchool of Technology and Applied Sciences São Paulo State University (UNESP) Campus Presidente PrudenteSchool of Technology and Applied Sciences São Paulo State University (UNESP) Campus Presidente PrudenteFAPESP: 2013/14262-7FAPESP: 2014/11410-8University of Western São Paulo – UNOESTEUniversidade Estadual Paulista (Unesp)Pereira, Viviane RibasPereira, Danillo Robertode Melo Tavares Vieira, Kátia CristinaRibas, Vitor PereiraConstantino, Carlos José Leopoldo [UNESP]Antunes, Patrícia AlexandraFavareto, Ana Paula Alves2020-12-12T01:45:24Z2020-12-12T01:45:24Z2019-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article35253-35265http://dx.doi.org/10.1007/s11356-019-06407-0Environmental Science and Pollution Research, v. 26, n. 34, p. 35253-35265, 2019.1614-74990944-1344http://hdl.handle.net/11449/19964110.1007/s11356-019-06407-02-s2.0-85074833298Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEnvironmental Science and Pollution Researchinfo:eu-repo/semantics/openAccess2021-10-23T08:53:36Zoai:repositorio.unesp.br:11449/199641Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T08:53:36Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods
title Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods
spellingShingle Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods
Pereira, Viviane Ribas
Artificial intelligence
Fungicide
Raman spectroscopy
Rat
Reproduction
Spermatozoa
title_short Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods
title_full Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods
title_fullStr Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods
title_full_unstemmed Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods
title_sort Sperm quality of rats exposed to difenoconazole using classical parameters and surface-enhanced Raman scattering: classification performance by machine learning methods
author Pereira, Viviane Ribas
author_facet Pereira, Viviane Ribas
Pereira, Danillo Roberto
de Melo Tavares Vieira, Kátia Cristina
Ribas, Vitor Pereira
Constantino, Carlos José Leopoldo [UNESP]
Antunes, Patrícia Alexandra
Favareto, Ana Paula Alves
author_role author
author2 Pereira, Danillo Roberto
de Melo Tavares Vieira, Kátia Cristina
Ribas, Vitor Pereira
Constantino, Carlos José Leopoldo [UNESP]
Antunes, Patrícia Alexandra
Favareto, Ana Paula Alves
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv University of Western São Paulo – UNOESTE
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Pereira, Viviane Ribas
Pereira, Danillo Roberto
de Melo Tavares Vieira, Kátia Cristina
Ribas, Vitor Pereira
Constantino, Carlos José Leopoldo [UNESP]
Antunes, Patrícia Alexandra
Favareto, Ana Paula Alves
dc.subject.por.fl_str_mv Artificial intelligence
Fungicide
Raman spectroscopy
Rat
Reproduction
Spermatozoa
topic Artificial intelligence
Fungicide
Raman spectroscopy
Rat
Reproduction
Spermatozoa
description Difenoconazole 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.
publishDate 2019
dc.date.none.fl_str_mv 2019-12-01
2020-12-12T01:45:24Z
2020-12-12T01:45:24Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/s11356-019-06407-0
Environmental Science and Pollution Research, v. 26, n. 34, p. 35253-35265, 2019.
1614-7499
0944-1344
http://hdl.handle.net/11449/199641
10.1007/s11356-019-06407-0
2-s2.0-85074833298
url http://dx.doi.org/10.1007/s11356-019-06407-0
http://hdl.handle.net/11449/199641
identifier_str_mv Environmental Science and Pollution Research, v. 26, n. 34, p. 35253-35265, 2019.
1614-7499
0944-1344
10.1007/s11356-019-06407-0
2-s2.0-85074833298
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Environmental Science and Pollution Research
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 35253-35265
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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