Efeitos da exposição à mistura comercial de gasolina/etanol sobre a função reprodutiva masculina em modelo experimental

• Ano de defesa: 2018
• Autor(a) principal: VIEIRA, Kátia Cristina de Melo Tavares
• Orientador(a): FAVARETO, Ana Paula Alves
• Banca de defesa: GIOMETTI, Inês Cristina , FERNANDES, Glaura Scantamburlo Alves
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade do Oeste Paulista
• Programa de Pós-Graduação: Mestrado em Meio Ambiente e Desenvolvimento Regional
• Departamento: Mestrado em Meio Ambiente e Desenvolvimento Regional
• País: Brasil
• Palavras-chave em Português: benzeno, tolueno, xileno, reprodução, rato.
• Palavras-chave em Inglês: benzene, toluene, xylene, reproduction, rat.
• Área do conhecimento CNPq: OUTROS::CIENCIAS
• Link de acesso: http://bdtd.unoeste.br:8080/jspui/handle/jspui/1083

Leaks at gas stations cause major problems for the environment, especially with regard to contamination of groundwater and soils. The major problems of oil derivative contamination are attributed to monoaromatic hydrocarbons (BTEX - benzene, toluene and xylene), which are the most soluble and mobile constituents of the gasoline fraction, being the first to reach the water table. In the most recent survey carried out by the State Company of Environmental Sanitation Technology of the State of São Paulo (CETESB), it was identified that more than 85% of the contamination cases in the region of West Paulista are related to waste from fuel stations. The present study evaluated the toxicological potential of exposure of adult male rats to the commercial gasoline / ethanol blend (75/25 by volume), with emphasis on the possible impacts on reproductive morphophysiology. Adult male Wistar rats were allocated into 4 experimental groups (n = 8 / group). The animals received commercial gasoline diluted in corn oil via gavage at doses of 0 (control group- C), 16 (group A - GA), 160 (group B - GB) or 800 mg / kg / day (group C - GC). The reproductive organs were collected and weighed. The sperm parameters evaluated were motility, plasma membrane integrity and acrosomal, mitochondrial activity and morphology. In addition, the epididymis and right testis were collected for sperm counts. Histopathological analysis of the testis and epididymis, spermatogenesis and cell counts. For the machine learning test, different pattern recognition algorithms were used: Artificial Neural Network (ANN), Bayes Classifier (BAY), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Optimum Path Forest OPF). The results were compared by ANOVA with Tukey's posterior test, and the Kruskall-Wallis test with Dunn's a posteriori, considering p <0.05. The sperm parameters were reduced in a dose-dependent manner. Mitochondrial activity and acrosomal integrity were decreased in the GC and GB groups in relation to the control group. There was an increase of isolated heads in the GC and GB groups in relation to the control group. Plasma membrane integrity was reduced in GC in relation to the control group. A decrease in the number of mobile spermatozoa with progressive trajectory was observed in the GC and GB groups, in relation to the control group. The daily sperm production was reduced in GC and GB in relation to the control group. In addition, this parameter was lower in GC than in GB. There was a significant delay in transit time in the tail of the epididymis in the GC and GB groups in relation to the control group. The number of Sertoli cells decreased in the GB and GC groups when compared to the CG and GB groups. Histopathological analysis, spermatogenesis and germ cell counts showed no significant difference between the groups. There was presence of pattern with the subdivision of two classes (control group and exposed groups), with better classification with the SVM test and 90% accuracy for control group. Thus, it was concluded that the exposure of rats to different doses of the commercial gasoline / ethanol mixture can alter spermatogenesis and sperm quality, with a recognizable classification pattern of machine learning algorithms.