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1.
Interdiscip Sci ; 2(3): 221-7, 2010 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-20658334

RESUMO

Quantitative composition-activity relationship (QCAR) study makes it possible to discover active components in traditional Chinese medicine (TCM) and to predict the integral bioactivity by its chemical composition. In the study, 28 samples of Radix Tinosporae were quantitatively analyzed by high performance liquid chromatography, and their analgesic activities were investigated via abdominal writhing tests on mice. Three genetic algorithms (GA) based approaches including partial least square regression, radial basis function neural network, and support vector regression (SVR) were established to construct QCAR models of R. Tinosporae. The result shows that GA-SVR has the best model performance in the bioactivity prediction of R. Tinosporae; seven major components thereof were discovered to have analgesic activities, and the analgesic activities of these components were partly confirmed by subsequent abdominal writhing test. The proposed approach allows discovering active components in TCM and predicting bioactivity by its chemical composition, and is expected to be utilized as a supplementary tool for the quality control and drug discovery of TCM.


Assuntos
Algoritmos , Analgésicos/análise , Medicamentos de Ervas Chinesas/química , Relação Estrutura-Atividade , Tinospora/química , Dor Abdominal , Analgésicos/química , Analgésicos/farmacologia , Animais , Comportamento Animal/efeitos dos fármacos , Cromatografia Líquida de Alta Pressão , Descoberta de Drogas , Medicamentos de Ervas Chinesas/farmacologia , Análise dos Mínimos Quadrados , Masculino , Medicina Tradicional Chinesa , Camundongos , Camundongos Endogâmicos ICR , Modelos Biológicos , Raízes de Plantas , Controle de Qualidade , Análise de Regressão , Máquina de Vetores de Suporte
2.
NMR Biomed ; 22(6): 601-8, 2009 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-19322815

RESUMO

This study proposes an NMR-based metabonomic approach to early prognostic evaluation of sepsis. Forty septic rats receiving cecal ligation and puncture (CLP) were divided into the surviving group and nonsurviving group on day 6, while 20 sham-operated rats served as the control group. Serum samples were collected from septic and sham-operated rats at 12 h after surgery and analyzed using (1)H NMR spectroscopy. Orthogonal partial least squares (OPLS) were applied and showed clustering according to predefined groups, indicating that NMR-based metabolic profiling could reveal pathologic characteristics in the serum of sham-operated, surviving, and nonsurviving septic rats. In addition, six characteristic metabolites including lactate, alanine, acetate, acetoacetate, hydroxybutyrate, and formate, which are mainly involved in energy metabolism, changed markedly in septic rats, especially in the nonsurvivors. Using these metabolites, a predictive model for prognostic evaluation of sepsis was constructed using a radial basis function neural network (RBFNN) with a prediction accuracy of about 87% by test samples. The results indicated that the NMR-based metabonomic approach is a potential technique for the early prognostic evaluation of sepsis.


Assuntos
Metabolômica/métodos , Ressonância Magnética Nuclear Biomolecular , Sepse , Animais , Humanos , Masculino , Prognóstico , Distribuição Aleatória , Ratos , Ratos Sprague-Dawley , Sensibilidade e Especificidade , Sepse/diagnóstico , Sepse/metabolismo , Sepse/fisiopatologia , Soro/química , Soro/metabolismo , Taxa de Sobrevida
3.
J Infect ; 56(6): 474-81, 2008 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-18471887

RESUMO

OBJECTIVES: Early prognostic evaluation of sepsis is an attractive strategy to decrease the mortality of septic patients, but presently there are no satisfactory approaches. Our goal is to establish an early, rapid and efficient approach for prognostic evaluation of sepsis. METHODS: Forty-five septic rats, induced by cecal ligation and puncture, were divided into surviving (n=23) and nonsurviving group (n=22) on day 6. Serum samples were obtained from septic and sham-operated rats (n=25) at 12h after surgery. HPLC/MS assays were performed to acquire serum metabolic profiles, and radial basis function neural network (RBFNN) was employed to build predictive model for prognostic evaluation of sepsis. RESULTS: Principle component analysis allows a clear discrimination of the pathologic characteristics among rats from surviving, nonsurviving and sham-operated groups. Six metabolites related to the outcome of septic rats were then structurally identified, which included linolenic acid, linoleic acid, oleic acid, stearic acid, docosahexaenoic acid and docosapentaenoic acid. A RBFNN model was built upon the metabolic profile data from rat serum, and a high predictive accuracy over 94% was achieved. CONCLUSIONS: HPLC/MS-based metabonomic approach combined with pattern recognition permits accurate outcome prediction of septic rats in the early stage. The proposed approach has advantages of rapid, low-cost and efficiency, and is expected to be applied in clinical prognostic evaluation of septic patients.


Assuntos
Metabolismo/fisiologia , Sepse/metabolismo , Animais , Cromatografia Líquida de Alta Pressão , Masculino , Espectrometria de Massas , Análise de Componente Principal , Prognóstico , Distribuição Aleatória , Ratos , Ratos Sprague-Dawley , Sepse/sangue , Organismos Livres de Patógenos Específicos
4.
Oral Oncol ; 44(5): 477-83, 2008 May.
Artigo em Inglês | MEDLINE | ID: mdl-17936673

RESUMO

Early diagnosis of oral squamous cell carcinoma (OSCC) and precursor lesions is an attractive strategy to decrease patient morbidity and mortality, but presently there are no satisfied diagnostic approaches. This study proposed a metabonomics-based diagnostic approach for OSCC and its precancerous lesions, including oral lichen planus (OLP) and oral leukoplakia (OLK). Saliva samples were collected from patients and healthy donors, and HPLC/MS analysis was performed to acquire metabolic profiles. Diagnostic model was then constructed with hierarchical principal component analysis (HPCA) and discriminate analysis algorithms. The results indicate that metabolic profiling can properly describe the pathologic characteristics of OSCC, OLP and OLK. HPCA combined with kernel fisher discriminant analysis achieved 100% accuracy in diagnosis of test samples, which is superior to direct principal component analysis and other modeling algorithms. The metabonomic approach based on the integral investigation of oral metabolites enables the detection of OSCC and precancerous lesions on noninvasive saliva samples. The proposed approach is noninvasive, efficient and low-cost, and it can be developed as a promising method for population-based screening of cancers and precancers in the oral cavity.


Assuntos
Carcinoma de Células Escamosas/diagnóstico , Leucoplasia Oral/diagnóstico , Líquen Plano Bucal/diagnóstico , Metabolômica/métodos , Neoplasias Bucais/diagnóstico , Adulto , Algoritmos , Carcinoma de Células Escamosas/metabolismo , Diagnóstico Precoce , Feminino , Humanos , Leucoplasia Oral/metabolismo , Líquen Plano Bucal/metabolismo , Espectroscopia de Ressonância Magnética/métodos , Masculino , Neoplasias Bucais/metabolismo , Lesões Pré-Cancerosas/diagnóstico , Lesões Pré-Cancerosas/metabolismo , Saliva/química
5.
Zhongguo Yi Xue Ke Xue Yuan Xue Bao ; 29(6): 811-7, 2007 Dec.
Artigo em Chinês | MEDLINE | ID: mdl-18595264

RESUMO

Metabonomics, as a newly developed technique, is expected to be a powerful technique in clinical diagnosis. Metabonomics-based diagnosis involves the global metabolic analysis of body-fluids, determination of biomarkers by multivariate statistic analysis, and establishemen of mathematic models for clinical diagnosis with the aid of pattern recognition. This article reviews the adoption of various analytical and computational strategies, application of metabonomics in clinical diagnosis, and potential challenges and development trends.


Assuntos
Metaboloma , Metabolômica/métodos , Biomarcadores/metabolismo , Líquidos Corporais/metabolismo , Técnicas e Procedimentos Diagnósticos , Humanos , Modelos Teóricos , Análise Multivariada
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