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Find video protocols related to scientific articles indexed in Pubmed.
Systems biology in a commercial quality study of the Japanese Angelica radix: toward an understanding of traditional medicinal plants.
Am. J. Chin. Med.
PUBLISHED: 07-02-2011
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The commercial quality of Japanese Angelica radices -- Angelica acutiloba Kitagawa (Yamato-toki) and A. acutiloba Kitagawa var. sugiyama Hikino (Hokkai-toki) -- used in Kampo traditional herbal medicines, was studied by use of omics technologies. Complementary and alternative medical providers have observed in their clinical experience that differences in radix commercial quality reflect the differences in pharmacological responses; however, there has been little scientific examination of this phenomenon. The approach of omics, including metabolomics, transcriptomics, genomics, and informatics revealed a distinction between the radix-quality grades based on their metabolites, gene expression in human subjects, and plant genome sequences. Systems biology, constructing a network of omics data used to analyze this complex system, is expected to be a powerful tool for enhancing the study of radix quality and furthering a comprehensive understanding of all medicinal plants.
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Predication of Japanese green tea (Sen-cha) ranking by volatile profiling using gas chromatography mass spectrometry and multivariate analysis.
J. Biosci. Bioeng.
PUBLISHED: 02-03-2011
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The sensory quality ranking of Japanese green tea (Sen-cha) was evaluated and predicted using volatile profiling and multivariate data analyses. The volatile constituents were extracted from tea infusion using vacuum hydrodistillation and analyzed using GC/MS. A quality of green tea could be discriminated to a high or low grade regarding the volatile profile by partial least squares discriminant analysis (PLS-DA). A quality ranking predictive model was developed from the relationship between subjective attributes (sensory quality ranking) and objective attributes (volatile profile) using partial least squares projections to latent structures together with the preprocessing filtering technique, orthogonal signal correction (OSC). Several volatile compounds highly contributed to model prediction were identified as various odor-active compounds, including geraniol, indole, linalool, cis-jasmone, dihydroactinidiolide, 6-chloroindole, methyl jasmonate, coumarin, trans-geranylacetone, linalool oxides, 5,6-epoxy-?-ionone, phytol, and phenylethyl alcohol. The whole fingerprints of these volatile compounds could be possible markers for the overall quality evaluation of green tea beverage.
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Fast GC-FID based metabolic fingerprinting of Japanese green tea leaf for its quality ranking prediction.
J Sep Sci
PUBLISHED: 07-02-2009
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There is a need of reliable, rapid, and cost-effective analysis technique to evaluate food and crop compositions, which are important to improve their qualities and quantities. Prior to fast GC-FID development, metabolic fingerprints, and predictive models obtained from a conventional GC-FID were evaluated by comparison to those derived from GC-TOF-MS. A similar chromatographic pattern with higher sensitivity of polyphenol compounds including epicatechin gallate (ECg) and epigallocatechin gallate (EGCg) had been achieved by using conventional GC-FID. Fast gas chromatograph coupled with flame ionization detector (GC-FID) has been carried out with 10 m x 0.18 mm id x 0.20 microm df capillary column. The analysis time per sample was reduced to less than 14 min compared to those of a conventional GC-FID (38 min) and GC-TOF-MS (28 min). The fast GC-FID also offered reliable retention time reproducibility without significant loss of peak resolution. Projections to latent structures by means of partial least squares (PLS) with orthogonal signal correction filtering (OSC) was applied to the fast GC-FID data. The predictive model showed good model fit and predictability with RMSEP of 3.464, suggesting that fast GC-FID based metabolic fingerprinting could be an alternative method for the prediction of Japanese green tea quality.
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Comprehensive metabolite profiling of phyA phyB phyC triple mutants to reveal their associated metabolic phenotype in rice leaves.
J. Biosci. Bioeng.
PUBLISHED: 03-13-2009
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The phytochrome photoreceptors regulate plant growth and development throughout their life cycle. Rice (Oryza sativa) possesses three phytochromes, phyA, phyB, and phyC. Physiological, genetic, and biochemical analyses of null mutants of each phytochrome have revealed the function of each in rice. However, few studies have investigated the relationship between phytochrome signaling and metabolism. In the present study, non-targeted metabolite analysis by gas chromatography time-of-flight mass spectrometry (GC/TOF-MS) and targeted metabolite analysis by capillary electrophoresis electrospray ionization mass spectrometry (CE/ESI-MS) were employed to investigate metabolic changes in rice phyA phyB phyC triple mutants. Distinct metabolic profiles between phyA phyB phyC triple mutants and the wild type (WT), as well as those between young and mature leaf blades, could be clearly observed by principal component analysis (PCA). The metabolite profiles indicated high accumulation of amino acids, organic acids, sugars, sugar phosphates, and nucleotides in the leaf blades of phyA phyB phyC triple mutants, especially in the young leaves, compared with those in the WT. Remarkable overaccumulation of monosaccharide, such as glucose (53.4-fold), fructose (42.5-fold), and galactose (24.5-fold), was observed in young leaves of phyA phyB phyC triple mutants. These metabolic phenotypes suggest that sugar metabolism, carbon partitioning, sugar transport, or some combination of these is impaired in the phyA phyB phyC triple mutants, and conversely, that phytochromes have crucial roles in sugar metabolism.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.