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 | Acesso ao texto completo restrito à biblioteca da Embrapa Semiárido. Para informações adicionais entre em contato com cpatsa.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
30/05/2017 |
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Data da última atualização: |
31/08/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
OLIVEIRA JÚNIOR, R. G. de; FERRAZ, C. A. A.; SILVA, J. C.; OLIVEIRA, A. P. de; DINIZ, T. C.; SILVA, M. G. e; QUINTANS JÚNIOR, L. J.; SOUZA, A. V. de; SANTOS, U. S. dos; TURATTI, I. C. C.; LOPES, N. P.; LORENZO, V. P.; ALMEIDA, J. R. G. da S. |
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Afiliação: |
RAIMUNDO GONÇALVES DE OLIVEIRA JÚNIOR, UNIVASF; CHRISTIANE ADRIELLY ALVES FERRAZ, UNIVASF; JULIANE CABRAL SILVA, UNIVASF; ANA PAULA DE OLIVEIRA, UNIVASF; TÂMARA COIMBRA DINIZ, UNIVASF; MARIANA GAMA E SILVA, UNIVASF; LUCINDO JOSÉ QUINTANS JÚNIOR, Universidade Federal de Sergipe; ANA VALERIA VIEIRA DE SOUZA, CPATSA; UILIANE SOARES DOS SANTOS; IZABEL CRISTINA CASANOVA TURATTI, Faculty of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo; NORBERTO PEPORINE LOPES, Faculty of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo; VITOR PRATES LORENZO, Federal Institute of Education, Science and Technology Sertão Pernambucano; JACKSON ROBERTO GUEDES DA SILVA ALMEIDA, UNIVASF. |
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Título: |
Antinociceptive effect of the essential oil from Croton conduplicatus Kunth (Euphorbiaceae). |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Molecules, v. 22, n. 6, p. 1-14, may. 2017. |
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DOI: |
10.3390/molecules22060900 |
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Idioma: |
Inglês |
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Conteúdo: |
Medicinal plants have been widely used in the treatment of chronic pain. In this study, we describe the antinociceptive effect of the essential oil from Croton conduplicatus (the EO 25, 50, and 100 mg/kg, i.p.), a medicinal plant native to Brazil. Antinociceptive activity was investigated by measuring the nociception induced by acetic acid, formalin, hot plate and carrageenan. A docking study was performed with the major constituents of the EO (E-caryophyllene, caryophyllene oxide, and camphor). The EO reduced nociceptive behavior at all doses tested in the acetic acid-induced nociception test (p< 0.05). The same was observed in both hases (neurogenic and inflammatory) of the formalin test. When the hot-plate test was conducted, the EO (50 mg/kg) extended the latency time after 60 min of treatment. The EO also reduced leukocyte migration at all doses, suggesting that its antinociceptive effect involves both central and peripheral mechanisms. Pretreatment with glibenclamide and atropine reversed the antinociceptive effect of the EO on the formalin test, suggesting the involvement of K ATP channels and muscarinic receptors. The docking study revealed a satisfactory interaction profile between the major components of the EO and the different muscarinic receptor subtypes (M2, M3, and M4). These results corroborate the medicinal use of C. conduplicatus in folk medicine |
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Palavras-Chave: |
Croton conduplicatus; Essential oil; Planta nativa; Quebra faca. |
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Thesagro: |
Caatinga; Óleo essencial; Planta medicinal; Vegetação Nativa. |
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Thesaurus Nal: |
Medicinal plants. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 02510naa a2200385 a 4500
001 2070195
005 2017-08-31
008 2017 bl uuuu u00u1 u #d
024 7 $a10.3390/molecules22060900$2DOI
100 1 $aOLIVEIRA JÚNIOR, R. G. de
245 $aAntinociceptive effect of the essential oil from Croton conduplicatus Kunth (Euphorbiaceae).$h[electronic resource]
260 $c2017
520 $aMedicinal plants have been widely used in the treatment of chronic pain. In this study, we describe the antinociceptive effect of the essential oil from Croton conduplicatus (the EO 25, 50, and 100 mg/kg, i.p.), a medicinal plant native to Brazil. Antinociceptive activity was investigated by measuring the nociception induced by acetic acid, formalin, hot plate and carrageenan. A docking study was performed with the major constituents of the EO (E-caryophyllene, caryophyllene oxide, and camphor). The EO reduced nociceptive behavior at all doses tested in the acetic acid-induced nociception test (p< 0.05). The same was observed in both hases (neurogenic and inflammatory) of the formalin test. When the hot-plate test was conducted, the EO (50 mg/kg) extended the latency time after 60 min of treatment. The EO also reduced leukocyte migration at all doses, suggesting that its antinociceptive effect involves both central and peripheral mechanisms. Pretreatment with glibenclamide and atropine reversed the antinociceptive effect of the EO on the formalin test, suggesting the involvement of K ATP channels and muscarinic receptors. The docking study revealed a satisfactory interaction profile between the major components of the EO and the different muscarinic receptor subtypes (M2, M3, and M4). These results corroborate the medicinal use of C. conduplicatus in folk medicine
650 $aMedicinal plants
650 $aCaatinga
650 $aÓleo essencial
650 $aPlanta medicinal
650 $aVegetação Nativa
653 $aCroton conduplicatus
653 $aEssential oil
653 $aPlanta nativa
653 $aQuebra faca
700 1 $aFERRAZ, C. A. A.
700 1 $aSILVA, J. C.
700 1 $aOLIVEIRA, A. P. de
700 1 $aDINIZ, T. C.
700 1 $aSILVA, M. G. e
700 1 $aQUINTANS JÚNIOR, L. J.
700 1 $aSOUZA, A. V. de
700 1 $aSANTOS, U. S. dos
700 1 $aTURATTI, I. C. C.
700 1 $aLOPES, N. P.
700 1 $aLORENZO, V. P.
700 1 $aALMEIDA, J. R. G. da S.
773 $tMolecules$gv. 22, n. 6, p. 1-14, may. 2017.
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Registro original: |
Embrapa Semiárido (CPATSA) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
16/12/2019 |
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Data da última atualização: |
16/12/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
SILVEIRA, L. S.; MARTINS FILHO, S.; AZEVEDO, C. F.; BARBOSA, E. C.; RESENDE, M. D. V. de; TAKAHASHI, E. K. |
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Afiliação: |
L. S. Silveira, UFV; S. Martins Filho, UFV; C. F. Azevedo, UFV; E. C. Barbosa, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; E. K. Takahashi, CENIBRA. |
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Título: |
Bayesian models applied to genomic selection for categorical traits. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 18, n. 4: gmr18490, 2019. 10 p. |
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DOI: |
10.4238/gmr18490 |
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Idioma: |
Inglês |
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Conteúdo: |
We compared two statistical methodologies applied to genetic and genomic analyses of categorical traits. The first one consists of a Bayesian approach to the Bayesian Linear Mixed Model (BLMM), which addresses the statistical problems of genomic prediction. The second methodology, called Bayesian Generalized Linear Mixed Model (BGLMM) is similar, but it is used when the distribution of the response variable is not Gaussian, as in the case of disease resistance phenotype categories. These models were compared according to predictive ability, bias, computational time and cross validation error rate (CVER). Additionally, an alternative classification method for the BLMM was proposed, which allowed us to obtain the CVER for this model. Estimates of the genetic parameters were obtained using BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator) and Bayesian G-BLUP (Genomic Best Linear Unbiased Prediction) estimation methods applied to BLMM and BGLMM. The models were applied in two scenarios, with two and four classes for the phenotype of resistance to rust disease caused by the pathogen Puccinia psidii and classified as reaction types (two classes) and infection levels (four classes) recorded for 559 trees of Eucalyptus urophylla with 24,806 SNP markers. Modeling this trait through SNPs allow the next generation of plants to be selected early, reducing time and costs. We found the same predictive ability for both models and a bias value closer to the ideal for BLMM (GBLUP). The BGLMM had the best CVER (0.29 against 0.32 and 0.47 against 0.51 for 2 and 4 categories, respectively), BLMM had a three times shorter computational time, and though BLMM is not the most appropriate model for handling categorical data, this model presented similar responses to BGLMM. Thus, we consider it as an appropriate alternative for categorical data modeling. MenosWe compared two statistical methodologies applied to genetic and genomic analyses of categorical traits. The first one consists of a Bayesian approach to the Bayesian Linear Mixed Model (BLMM), which addresses the statistical problems of genomic prediction. The second methodology, called Bayesian Generalized Linear Mixed Model (BGLMM) is similar, but it is used when the distribution of the response variable is not Gaussian, as in the case of disease resistance phenotype categories. These models were compared according to predictive ability, bias, computational time and cross validation error rate (CVER). Additionally, an alternative classification method for the BLMM was proposed, which allowed us to obtain the CVER for this model. Estimates of the genetic parameters were obtained using BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator) and Bayesian G-BLUP (Genomic Best Linear Unbiased Prediction) estimation methods applied to BLMM and BGLMM. The models were applied in two scenarios, with two and four classes for the phenotype of resistance to rust disease caused by the pathogen Puccinia psidii and classified as reaction types (two classes) and infection levels (four classes) recorded for 559 trees of Eucalyptus urophylla with 24,806 SNP markers. Modeling this trait through SNPs allow the next generation of plants to be selected early, reducing time and costs. We found the same predictive ability for both models and a bias value closer to the ideal for BLMM (G... Mostrar Tudo |
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Palavras-Chave: |
Bayesian inference; Statistical methods. |
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Thesagro: |
Melhoramento Genético Vegetal. |
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Thesaurus NAL: |
Genetic improvement; Plant breeding. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02649naa a2200253 a 4500
001 2116962
005 2019-12-16
008 2019 bl uuuu u00u1 u #d
024 7 $a10.4238/gmr18490$2DOI
100 1 $aSILVEIRA, L. S.
245 $aBayesian models applied to genomic selection for categorical traits.$h[electronic resource]
260 $c2019
520 $aWe compared two statistical methodologies applied to genetic and genomic analyses of categorical traits. The first one consists of a Bayesian approach to the Bayesian Linear Mixed Model (BLMM), which addresses the statistical problems of genomic prediction. The second methodology, called Bayesian Generalized Linear Mixed Model (BGLMM) is similar, but it is used when the distribution of the response variable is not Gaussian, as in the case of disease resistance phenotype categories. These models were compared according to predictive ability, bias, computational time and cross validation error rate (CVER). Additionally, an alternative classification method for the BLMM was proposed, which allowed us to obtain the CVER for this model. Estimates of the genetic parameters were obtained using BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator) and Bayesian G-BLUP (Genomic Best Linear Unbiased Prediction) estimation methods applied to BLMM and BGLMM. The models were applied in two scenarios, with two and four classes for the phenotype of resistance to rust disease caused by the pathogen Puccinia psidii and classified as reaction types (two classes) and infection levels (four classes) recorded for 559 trees of Eucalyptus urophylla with 24,806 SNP markers. Modeling this trait through SNPs allow the next generation of plants to be selected early, reducing time and costs. We found the same predictive ability for both models and a bias value closer to the ideal for BLMM (GBLUP). The BGLMM had the best CVER (0.29 against 0.32 and 0.47 against 0.51 for 2 and 4 categories, respectively), BLMM had a three times shorter computational time, and though BLMM is not the most appropriate model for handling categorical data, this model presented similar responses to BGLMM. Thus, we consider it as an appropriate alternative for categorical data modeling.
650 $aGenetic improvement
650 $aPlant breeding
650 $aMelhoramento Genético Vegetal
653 $aBayesian inference
653 $aStatistical methods
700 1 $aMARTINS FILHO, S.
700 1 $aAZEVEDO, C. F.
700 1 $aBARBOSA, E. C.
700 1 $aRESENDE, M. D. V. de
700 1 $aTAKAHASHI, E. K.
773 $tGenetics and Molecular Research$gv. 18, n. 4: gmr18490, 2019. 10 p.
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Embrapa Florestas (CNPF) |
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