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Giseli Karenina Traesel,Flavio Henrique Souza de Araujo,Luis Henrique Almeida Castro,Fernando Freitas de Lima,Sara Emilia Lima Tolouei Menegati,Priscilla Narciso Justi,Candida Aparecida Leite Kassuya 한국식품영양과학회 2017 Journal of medicinal food Vol.20 No.8
Genotoxic data of medicinal plants and functional foods are required as part of the risk assessment by international regulatory agencies. Due to its food consumption and ethnopharmacological relevance, pequi oil (Caryocar brasiliense Camb.) is one of these compounds to be studied. The aim of this study was to evaluate the cytotoxic, genotoxic, and clastogenic effects of the oil from the pulp of C. brasiliense (OPCB) in vivo and in vitro. Initially, the Artemia salina in vitro assay was conducted to determine the cells viability rate of different doses of the OPCB. Subsequently, comet assay (Organization for Economic Cooperation and Development, OECD 489) and micronucleus test (OECD 474) were performed in blood and bone marrow of Wistar rats treated orally with a 125, 250, 500, or 1000 mg/kg/bw of the OPCB for 4 weeks. The chemical analysis indicated the presence of β-carotene and lycopene in the oil. In the A. salina test, all OPCB doses maintained cell viability rates statistically similar to the negative control. The in vivo tests performed showed that OPCB did not show significant genotoxic or clastogenic effects in cells analyzed with the four doses tested. Altogether, these results indicate that, under our experimental conditions, C. brasiliense fruit oil did not reveal genetic toxicity in rat cells.
Clustering Wear Particle Using Computer Vision and Self-Organizing Maps
Marcos Alessandro C. Ramos,Bruno Cesar C. Leme,Luis Fernando de Almeida,Francisco Carlos P. Bizarria,Walter P. Bizarria 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10
This work presents the implementation of a method for classification of wear particle contaminant present in industrial oil by using image processing and neural networks. It is based on morphological data obtained from a computer vision system and employs Self-Organizing Maps to classify particles’ features intro different wear debris groups. The dataset used for training the neural network and further validation of the results was gathered using reports provided by a specialist company in wear particle analysis. The objective is to develop a system feasible for most industries to turn the process of particle classification more autonomous and faster. The results demonstrate that our proposed system could classify particles considering their shape in a reliable and autonomous way.