Base de dados : MEDLINE
Pesquisa : G07.690.773 [Categoria DeCS]
Referências encontradas : 125 [refinar]
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  1 / 125 MEDLINE  
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[PMID]:28081198
[Au] Autor:Mu XY; Zhao LC; Zhang ZX
[Ad] Endereço:Laboratory of Systematic Evolution and Biogeography of Woody Plants, College of Nature Conservation, Beijing Forestry University, Beijing, PR China.
[Ti] Título:Molecular Analysis of Chinese Celastrus and Tripterygium and Implications in Medicinal and Pharmacological Studies.
[So] Source:PLoS One;12(1):e0169973, 2017.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Celastrus and Tripterygium species, which are used in traditional Chinese medicine, have attracted much attention due to their anti-tumor promoting and neuroprotective activities, in addition to their applications in autoimmune disorders. However, systematic relationships between them and among species are unclear, and it may disturb their further medicinal utilization. In the present study, the molecular analysis of combined chloroplast and nuclear markers of all Chinese Celastrus and Tripterygium was performed, and clear inter- and intra-genus relationships were presented. The result suggests that Tripterygium constitute a natural monophyletic clade within Celastrus with strong support value. Fruit and seed type are better than inflorescence in subgeneric classification. Chinese Celastrus are classified for three sections: Sect. Sempervirentes (Maxim.) CY Cheng & TC Kao, Sect. Lunatus XY Mu & ZX Zhang, sect. nov., and Sect. Ellipticus XY Mu & ZX Zhang, sect. nov. The phylogenetic data was consistent with their chemical components reported previously. Owing to the close relationship, several evergreen Celastrus species are recommended for chemical and pharmacological studies. Our results also provide reference for molecular identification of Chinese Celastrus and Tripterygium.
[Mh] Termos MeSH primário: Celastrus/classificação
Celastrus/genética
Filogenia
Tripterygium/classificação
Tripterygium/genética
[Mh] Termos MeSH secundário: Celastrus/química
Celastrus/metabolismo
Cloroplastos/química
Cloroplastos/metabolismo
DNA de Plantas/genética
Frutas/química
Medicina Tradicional Chinesa
Fenômenos Farmacológicos
Sementes/química
Análise de Sequência de DNA
Tripterygium/química
Tripterygium/metabolismo
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (DNA, Plant)
[Em] Mês de entrada:1708
[Cu] Atualização por classe:170824
[Lr] Data última revisão:
170824
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170113
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0169973


  2 / 125 MEDLINE  
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[PMID]:27560606
[Au] Autor:Yamagata T; Zanelli U; Gallemann D; Perrin D; Dolgos H; Petersson C
[Ad] Endereço:a Global Early Development/Quantitative Pharmacology and Drug Disposition (QPD), Merck KGaA , Grafing , Germany and.
[Ti] Título:Comparison of methods for the prediction of human clearance from hepatocyte intrinsic clearance for a set of reference compounds and an external evaluation set.
[So] Source:Xenobiotica;47(9):741-751, 2017 Sep.
[Is] ISSN:1366-5928
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:1. We compared direct scaling, regression model equation and the so-called "Poulin et al." methods to scale clearance (CL) from in vitro intrinsic clearance (CL ) measured in human hepatocytes using two sets of compounds. One reference set comprised of 20 compounds with known elimination pathways and one external evaluation set based on 17 compounds development in Merck (MS). 2. A 90% prospective confidence interval was calculated using the reference set. This interval was found relevant for the regression equation method. The three outliers identified were justified on the basis of their elimination mechanism. 3. The direct scaling method showed a systematic underestimation of clearance in both the reference and evaluation sets. The "Poulin et al." and the regression equation methods showed no obvious bias in either the reference or evaluation sets. 4. The regression model equation was slightly superior to the "Poulin et al." method in the reference set and showed a better absolute average fold error (AAFE) of value 1.3 compared to 1.6. A larger difference was observed in the evaluation set were the regression method and "Poulin et al." resulted in an AAFE of 1.7 and 2.6, respectively (removing the three compounds with known issues mentioned above). A similar pattern was observed for the correlation coefficient. Based on these data we suggest the regression equation method combined with a prospective confidence interval as the first choice for the extrapolation of human in vivo hepatic metabolic clearance from in vitro systems.
[Mh] Termos MeSH primário: Taxa de Depuração Metabólica
Fenômenos Farmacológicos
[Mh] Termos MeSH secundário: Hepatócitos/metabolismo
Seres Humanos
Cinética
Fígado/metabolismo
Modelos Biológicos
Estudos Prospectivos
Análise de Regressão
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1709
[Cu] Atualização por classe:170929
[Lr] Data última revisão:
170929
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:160826
[St] Status:MEDLINE
[do] DOI:10.1080/00498254.2016.1222639


  3 / 125 MEDLINE  
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[PMID]:27978984
[Au] Autor:Lavé T; Caruso A; Parrott N; Walz A
[Ad] Endereço:Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Switzerland. Electronic address: thierry.lave@roche.com.
[Ti] Título:Translational PK/PD modeling to increase probability of success in drug discovery and early development.
[So] Source:Drug Discov Today Technol;21-22:27-34, 2016 Sep - Dec.
[Is] ISSN:1740-6749
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:In this review we present ways in which translational PK/PD modeling can address opportunities to enhance probability of success in drug discovery and early development. This is achieved by impacting efficacy and safety-driven attrition rates, through increased focus on the quantitative understanding and modeling of translational PK/PD. Application of the proposed principles early in the discovery and development phases is anticipated to bolster confidence of successfully evaluating proof of mechanism in humans and ultimately improve Phase II success. The present review is centered on the application of predictive modeling and simulation approaches during drug discovery and early development, and more specifically of mechanism-based PK/PD modeling. Case studies are presented, focused on the relevance of M&S contributions to real-world questions and the impact on decision making.
[Mh] Termos MeSH primário: Modelos Biológicos
Farmacocinética
Fenômenos Farmacológicos
[Mh] Termos MeSH secundário: Animais
Ensaios Clínicos Fase II como Assunto
Descoberta de Drogas
Seres Humanos
Pesquisa Médica Translacional
[Pt] Tipo de publicação:JOURNAL ARTICLE; REVIEW
[Em] Mês de entrada:1702
[Cu] Atualização por classe:170216
[Lr] Data última revisão:
170216
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:161217
[St] Status:MEDLINE


  4 / 125 MEDLINE  
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[PMID]:27460622
[Au] Autor:Leucht S; Samara M; Heres S; Davis JM
[Ad] Endereço:stefan.leucht@lrz.tum.de.
[Ti] Título:Dose Equivalents for Antipsychotic Drugs: The DDD Method.
[So] Source:Schizophr Bull;42 Suppl 1:S90-4, 2016 Jul.
[Is] ISSN:1745-1701
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: Dose equivalents of antipsychotics are an important but difficult to define concept, because all methods have weaknesses and strongholds. METHODS: We calculated dose equivalents based on defined daily doses (DDDs) presented by the World Health Organisation's Collaborative Center for Drug Statistics Methodology. Doses equivalent to 1mg olanzapine, 1mg risperidone, 1mg haloperidol, and 100mg chlorpromazine were presented and compared with the results of 3 other methods to define dose equivalence (the "minimum effective dose method," the "classical mean dose method," and an international consensus statement). RESULTS: We presented dose equivalents for 57 first-generation and second-generation antipsychotic drugs, available as oral, parenteral, or depot formulations. Overall, the identified equivalent doses were comparable with those of the other methods, but there were also outliers. CONCLUSIONS: The major strength of this method to define dose response is that DDDs are available for most drugs, including old antipsychotics, that they are based on a variety of sources, and that DDDs are an internationally accepted measure. The major limitations are that the information used to estimate DDDS is likely to differ between the drugs. Moreover, this information is not publicly available, so that it cannot be reviewed. The WHO stresses that DDDs are mainly a standardized measure of drug consumption, and their use as a measure of dose equivalence can therefore be misleading. We, therefore, recommend that if alternative, more "scientific" dose equivalence methods are available for a drug they should be preferred to DDDs. Moreover, our summary can be a useful resource for pharmacovigilance studies.
[Mh] Termos MeSH primário: Antipsicóticos/administração & dosagem
Tratamento Farmacológico/normas
Fenômenos Farmacológicos
[Mh] Termos MeSH secundário: Seres Humanos
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (Antipsychotic Agents)
[Em] Mês de entrada:1706
[Cu] Atualização por classe:170701
[Lr] Data última revisão:
170701
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:160728
[St] Status:MEDLINE
[do] DOI:10.1093/schbul/sbv167


  5 / 125 MEDLINE  
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[PMID]:27352096
[Au] Autor:Puszynski K; Gandolfi A; d'Onofrio A
[Ad] Endereço:Institute of Automatic Control, Silesian University of Technology, Akademicka 16, Gliwice, Poland.
[Ti] Título:The role of stochastic gene switching in determining the pharmacodynamics of certain drugs: basic mechanisms.
[So] Source:J Pharmacokinet Pharmacodyn;43(4):395-410, 2016 08.
[Is] ISSN:1573-8744
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:In this paper we analyze the impact of the stochastic fluctuation of genes between their ON and OFF states on the pharmacodynamics of a potentially large class of drugs. We focus on basic mechanisms underlying the onset of in vitro experimental dose-response curves, by investigating two elementary molecular circuits. Both circuits consist in the transcription of a gene and in the successive translation into the corresponding protein. Whereas in the first the activation/deactivation rates of the single gene copy are constant, in the second the protein, now a transcription factor, amplifies the deactivation rate, so introducing a negative feedback. The drug is assumed to enhance the elimination of the protein, and in both cases the success of therapy is assured by keeping the level of the given protein under a threshold for a fixed time. Our numerical simulations suggests that the gene switching plays a primary role in determining the sigmoidal shape of dose-response curves. Moreover, the simulations show interesting phenomena related to the magnitude of the average gene switching time and to the drug concentration. In particular, for slow gene switching a significant fraction of cells can respond also in the absence of drug or with drug concentrations insufficient for the response in a deterministic setting. For higher drug concentrations, the non-responding fraction exhibits a maximum at intermediate values of the gene switching rates. For fast gene switching, instead, the stochastic prediction follows the prediction of the deterministic approximation, with all the cells responding or non-responding according to the drug dose.
[Mh] Termos MeSH primário: Redes Reguladoras de Genes
Modelos Biológicos
Preparações Farmacêuticas/administração & dosagem
Fenômenos Farmacológicos/genética
[Mh] Termos MeSH secundário: Relação Dose-Resposta a Droga
Retroalimentação Fisiológica
Seres Humanos
Fenômenos Farmacológicos/efeitos dos fármacos
Processos Estocásticos
Fatores de Transcrição/genética
Fatores de Transcrição/metabolismo
Transcrição Genética
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Nm] Nome de substância:
0 (Pharmaceutical Preparations); 0 (Transcription Factors)
[Em] Mês de entrada:1710
[Cu] Atualização por classe:171101
[Lr] Data última revisão:
171101
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:160629
[St] Status:MEDLINE
[do] DOI:10.1007/s10928-016-9480-2


  6 / 125 MEDLINE  
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[PMID]:27116466
[Au] Autor:van der Laan JW; Kasper P; Silva Lima B; Jones DR; Pasanen M
[Ad] Endereço:a Medicines Evaluation Board , Utrecht , The Netherlands ;
[Ti] Título:Critical analysis of carcinogenicity study outcomes. Relationship with pharmacological properties.
[So] Source:Crit Rev Toxicol;46(7):587-614, 2016 Aug.
[Is] ISSN:1547-6898
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:Predicting the outcome of life-time carcinogenicity studies in rats based on chronic (6-month) toxicity studies in this species is possible in some instances. This should reduce the number of such studies and hence have a significant impact on the total number of animals used in safety assessment of new medicines. From a regulatory perspective, this should be sufficient to grant a waiver for a carcinogenicity study in those cases where there is confidence in the outcome of the prediction. Pharmacological properties are a frequent key factor for the carcinogenic mode of action of some pharmaceuticals, but data-analysis on a large dataset has never been formally conducted. We have conducted an analysis of a dataset based on the perspective of the pharmacology of 255 compounds from industrial and regulatory sources. It is proposed that a pharmacological, class-specific, model may consist of an overall causal relationship between the pharmacological class and the histopathology findings in rats after 6 months treatment, leading to carcinogenicity outcome after 2 years. Knowledge of the intended drug target and pathway pharmacology should enhance the prediction of either positive or negative outcomes of rat carcinogenicity studies. The goal of this analysis is to review the pharmacological properties of compounds together with the histopathology findings from the chronic toxicity study in rodents in order to introduce an integrated approach to estimate the risk of human carcinogenicity of pharmaceuticals. This approach would allow scientists to define conditions under which 2-year rat carcinogenicity studies will or will not add value to such an assessment. We have demonstrated the possibility of a regulatory waiver for a carcinogenicity study in rats, as currently discussed in the International Council for Harmonization (ICH) - formerly known as the International Conference on Harmonization (ICH), by applying the proposed prediction approach in a number of case studies.
[Mh] Termos MeSH primário: Carcinógenos/toxicidade
Fenômenos Farmacológicos
[Mh] Termos MeSH secundário: Animais
Testes de Carcinogenicidade
Seres Humanos
Preparações Farmacêuticas
Ratos
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (Carcinogens); 0 (Pharmaceutical Preparations)
[Em] Mês de entrada:1708
[Cu] Atualização por classe:170815
[Lr] Data última revisão:
170815
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:160427
[St] Status:MEDLINE
[do] DOI:10.3109/10408444.2016.1163664


  7 / 125 MEDLINE  
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[PMID]:27071755
[Au] Autor:Fu G; Ding Y; Seal A; Chen B; Sun Y; Bolton E
[Ad] Endereço:National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA. gang.fu@nih.gov.
[Ti] Título:Predicting drug target interactions using meta-path-based semantic network analysis.
[So] Source:BMC Bioinformatics;17:160, 2016 Apr 12.
[Is] ISSN:1471-2105
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction. RESULTS: Supervised machine learning models were constructed based on meta-path topological features of an enriched semantic network, which was derived from Chem2Bio2RDF, and was expanded by adding compound and protein similarity neighboring links obtained from the PubChem databases. The additional semantic links significantly improved the predictive performance of the supervised learning models. The binary classification model built upon the enriched feature space using the Random Forest algorithm significantly outperformed an existing semantic link prediction algorithm, Semantic Link Association Prediction (SLAP), to predict unknown links between compounds and protein targets in an evolving network. In addition to link prediction, Random Forest also has an intrinsic feature ranking algorithm, which can be used to select the important topological features that contribute to link prediction. CONCLUSIONS: The proposed framework has been demonstrated as a powerful alternative to SLAP in order to predict DTIs using the semantic network that integrates chemical, pharmacological, genomic, biological, functional, and biomedical information into a unified framework. It offers the flexibility to enrich the feature space by using different normalization processes on the topological features, and it can perform model construction and feature selection at the same time.
[Mh] Termos MeSH primário: Desenho de Drogas
Aprendizado de Máquina
Preparações Farmacêuticas/química
[Mh] Termos MeSH secundário: Mineração de Dados
Bases de Dados Factuais
Modelos Químicos
Fenômenos Farmacológicos
Proteínas/química
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Nm] Nome de substância:
0 (Pharmaceutical Preparations); 0 (Proteins)
[Em] Mês de entrada:1612
[Cu] Atualização por classe:170220
[Lr] Data última revisão:
170220
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:160414
[St] Status:MEDLINE
[do] DOI:10.1186/s12859-016-1005-x


  8 / 125 MEDLINE  
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[PMID]:27069773
[Au] Autor:Lötsch J; Ultsch A
[Ad] Endereço:Institute of Clinical Pharmacology, Goethe University, Frankfurt am Main, Germany.
[Ti] Título:Process Pharmacology: A Pharmacological Data Science Approach to Drug Development and Therapy.
[So] Source:CPT Pharmacometrics Syst Pharmacol;5(4):192-200, 2016 Apr.
[Is] ISSN:2163-8306
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:A novel functional-genomics based concept of pharmacology that uses artificial intelligence techniques for mining and knowledge discovery in "big data" providing comprehensive information about the drugs' targets and their functional genomics is proposed. In "process pharmacology", drugs are associated with biological processes. This puts the disease, regarded as alterations in the activity in one or several cellular processes, in the focus of drug therapy. In this setting, the molecular drug targets are merely intermediates. The identification of drugs for therapeutic or repurposing is based on similarities in the high-dimensional space of the biological processes that a drug influences. Applying this principle to data associated with lymphoblastic leukemia identified a short list of candidate drugs, including one that was recently proposed as novel rescue medication for lymphocytic leukemia. The pharmacological data science approach provides successful selections of drug candidates within development and repurposing tasks.
[Mh] Termos MeSH primário: Descoberta de Drogas/métodos
Farmacologia/métodos
Leucemia-Linfoma Linfoblástico de Células Precursoras/tratamento farmacológico
[Mh] Termos MeSH secundário: Analgésicos/farmacologia
Anti-Hipertensivos/farmacologia
Antineoplásicos/farmacologia
Inteligência Artificial
Reposicionamento de Medicamentos
Genômica
Seres Humanos
Terapia de Alvo Molecular
Fenômenos Farmacológicos
Leucemia-Linfoma Linfoblástico de Células Precursoras/genética
Biologia de Sistemas
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (Analgesics); 0 (Antihypertensive Agents); 0 (Antineoplastic Agents)
[Em] Mês de entrada:1708
[Cu] Atualização por classe:170811
[Lr] Data última revisão:
170811
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:160413
[St] Status:MEDLINE
[do] DOI:10.1002/psp4.12072


  9 / 125 MEDLINE  
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[PMID]:26727457
[Au] Autor:Marsman AF; Kneepkens EL; Ruwaard J; Wei JC; Nurmohamed MT; van Denderen C; van der Horst-Bruinsma IE; Rispens T; Wolbink G
[Ad] Endereço:a Amsterdam Rheumatology and Immunology Centre , location Reade, Amsterdam , The Netherlands.
[Ti] Título:Search for a concentration-effect curve of adalimumab in ankylosing spondylitis patients.
[So] Source:Scand J Rheumatol;45(4):331-4, 2016 Jul.
[Is] ISSN:1502-7732
[Cp] País de publicação:England
[La] Idioma:eng
[Mh] Termos MeSH primário: Adalimumab/uso terapêutico
Antirreumáticos/uso terapêutico
Espondilite Anquilosante/tratamento farmacológico
[Mh] Termos MeSH secundário: Adalimumab/sangue
Adulto
Antirreumáticos/sangue
Estudos de Coortes
Monitoramento de Medicamentos
Feminino
Seres Humanos
Masculino
Meia-Idade
Fenômenos Farmacológicos
Estudos Prospectivos
[Pt] Tipo de publicação:LETTER; OBSERVATIONAL STUDY
[Nm] Nome de substância:
0 (Antirheumatic Agents); FYS6T7F842 (Adalimumab)
[Em] Mês de entrada:1701
[Cu] Atualização por classe:170123
[Lr] Data última revisão:
170123
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:160105
[St] Status:MEDLINE
[do] DOI:10.3109/03009742.2015.1114666


  10 / 125 MEDLINE  
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[PMID]:26578524
[Au] Autor:Lippert J; Burghaus R; Kuepfer L; Ploeger B; Schaller S; Schmitt W; Willmann S
[Ad] Endereço:Bayer Pharma AG, Aprather Weg 18a, 42113, Wuppertal, Germany. joerg.lippert@bayer.com.
[Ti] Título:Modeling and Simulation of In Vivo Drug Effects.
[So] Source:Handb Exp Pharmacol;232:313-29, 2016.
[Is] ISSN:0171-2004
[Cp] País de publicação:Germany
[La] Idioma:eng
[Ab] Resumo:The concept of a pharmacokinetics-pharmacodynamics (PK/PD) assessment of drug development candidates is well established in pharmaceutical research and development, and PK/PD modeling is common practice in all pharmaceutical companies. A recent analysis (Morgan et al., Drug Discov Today 17(9-10):419-424, 2012) revealed however that insufficient certainty in the integrity of the causal chain of fundamental pharmacological steps from drug dosing through systemic exposure, target tissue exposure, and engagement of molecular target to pharmacological response is still the major driver of failure in phase II of clinical drug development. Despite the rise of molecular biomarkers, ethical, scientific, and practical constraints very often still prevent a direct assessment of each necessary step ultimately leading to an intended drug effect or an unintended adverse reaction. Yet, incomplete investigation of the causality of drug responses is a major risk for translational assessments and the prediction of drug responses in different species or other populations. Mechanism-based modeling and simulation (M&S) offers a means to investigate complex physiological and pharmacological processes and to complement experimental data for non-accessible steps in the pharmacological causal chain. With the help of two examples, it is illustrated, what level of physiological detail, state-of-the-art models can represent, how predictive these models are and how mechanism-based approaches can be combined with empirical correlation-based concepts.
[Mh] Termos MeSH primário: Descoberta de Drogas
Fenômenos Farmacológicos
[Mh] Termos MeSH secundário: Animais
Simulação por Computador
Seres Humanos
Modelos Biológicos
Biologia de Sistemas
[Pt] Tipo de publicação:JOURNAL ARTICLE; REVIEW
[Em] Mês de entrada:1606
[Cu] Atualização por classe:160331
[Lr] Data última revisão:
160331
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:151119
[St] Status:MEDLINE
[do] DOI:10.1007/164_2015_21



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