Auswahl der wissenschaftlichen Literatur zum Thema „Metabolomic analyses“
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Zeitschriftenartikel zum Thema "Metabolomic analyses":
Szczerbinski, Lukasz, Gladys Wojciechowska, Adam Olichwier, Mark A. Taylor, Urszula Puchta, Paulina Konopka, Adam Paszko et al. „Untargeted Metabolomics Analysis of the Serum Metabolic Signature of Childhood Obesity“. Nutrients 14, Nr. 1 (04.01.2022): 214. http://dx.doi.org/10.3390/nu14010214.
Kim, Hyun Woo. „Metabolomic Approaches to Investigate the Effect of Metformin: An Overview“. International Journal of Molecular Sciences 22, Nr. 19 (24.09.2021): 10275. http://dx.doi.org/10.3390/ijms221910275.
Qi, Jinwei, Kang Li, Yunxia Shi, Yufei Li, Long Dong, Ling Liu, Mingyang Li et al. „Cross-Species Comparison of Metabolomics to Decipher the Metabolic Diversity in Ten Fruits“. Metabolites 11, Nr. 3 (12.03.2021): 164. http://dx.doi.org/10.3390/metabo11030164.
Patterson, Jeffrey, Xiaojian Shi, William Bresette, Ryan Eghlimi, Sarah Atlas, Kristin Farr, Sonia Vega-López und Haiwei Gu. „A Metabolomic Analysis of the Sex-Dependent Hispanic Paradox“. Metabolites 11, Nr. 8 (20.08.2021): 552. http://dx.doi.org/10.3390/metabo11080552.
Delporte, Cédric, Nausicaa Noret, Cécile Vanhaverbeke, Olivier J. Hardy, Jean-François Martin, Marie Tremblay-Franco, David Touboul et al. „Does the Phytochemical Diversity of Wild Plants Like the Erythrophleum genus Correlate with Geographical Origin?“ Molecules 26, Nr. 6 (17.03.2021): 1668. http://dx.doi.org/10.3390/molecules26061668.
Cheng, Leo L., Adam S. Feldman, Lindsey A. Vandergrift, Isabella H. Muti, Florian Rumpf, Andrew Gusev, Yannick Berker et al. „Abstract 2222: Detecting clinically significant prostate cancers: Tissue metabolomics refines multiparametric MRI-ultrasound fusion prostate biopsy“. Cancer Research 82, Nr. 12_Supplement (15.06.2022): 2222. http://dx.doi.org/10.1158/1538-7445.am2022-2222.
Byerley, Lauri O., Karyn M. Gallivan, Courtney J. Christopher, Christopher M. Taylor, Meng Luo, Scot E. Dowd, Gregory M. Davis, Hector F. Castro, Shawn R. Campagna und Kristin S. Ondrak. „Gut Microbiome and Metabolome Variations in Self-Identified Muscle Builders Who Report Using Protein Supplements“. Nutrients 14, Nr. 3 (26.01.2022): 533. http://dx.doi.org/10.3390/nu14030533.
Loras, Alba, M. Carmen Martínez-Bisbal, Guillermo Quintás, Salvador Gil, Ramón Martínez-Máñez und José Luis Ruiz-Cerdá. „Urinary Metabolic Signatures Detect Recurrences in Non-Muscle Invasive Bladder Cancer“. Cancers 11, Nr. 7 (29.06.2019): 914. http://dx.doi.org/10.3390/cancers11070914.
Mok, Jeong-Hun, Minjoong Joo, Van-An Duong, Seonghyeon Cho, Jong-Moon Park, Young-Sic Eom, Tae-Hwa Song, Hee-Joung Lim und Hookeun Lee. „Proteomic and Metabolomic Analyses of Maggots in Porcine Corpses for Post-Mortem Interval Estimation“. Applied Sciences 11, Nr. 17 (26.08.2021): 7885. http://dx.doi.org/10.3390/app11177885.
Fitzpatrick, Garrett, Maryam Rahman, Timothy Garrett und Jesse Kresak. „MNGI-11. HIGH-GRADE AND LOW-GRADE MENINGIOMAS HARBOR DIFFERING METABOLOMIC PROFILES“. Neuro-Oncology 21, Supplement_6 (November 2019): vi141—vi142. http://dx.doi.org/10.1093/neuonc/noz175.593.
Dissertationen zum Thema "Metabolomic analyses":
Robinson, Andrew Raymond. „Metabolomic analyses of wood attributes in tree species“. Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/7697.
Hobani, Yahya Hasan. „Metabolomic analyses of Drosophila models for human renal disease“. Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3222/.
Wibom, Carl. „Multivariate analyses of proteomic and metabolomic patterns in brain tumors“. Doctoral thesis, Umeå universitet, Onkologi, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-25670.
Taraboletti, Alexandra Anna. „Chemical and Metabolomic Analyses of Cuprizone-Induced Demyelination and Remyelination“. University of Akron / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1498535047689141.
Orland, Annika [Verfasser]. „Metabolomic and Transcriptomic Analyses in the Characterization of Herbal Substances and their Preparations = Metabolom- und Transkriptom-Analysen zur Charakterisierung von pflanzlichen Substanzen und daraus hergestellten Zubereitungen / Annika Orland“. Bonn : Universitäts- und Landesbibliothek Bonn, 2014. http://d-nb.info/1077290357/34.
Vincent, Isabel May. „Using metabolomic analyses to study mode of action of and resistance to Eflornithine in Trypanosoma brucei“. Thesis, University of Glasgow, 2011. http://theses.gla.ac.uk/3125/.
Boudah, Samia. „Développement et application de méthodes de chromatographie liquide couplées à la spectrométrie de masse à haute résolution pour les analyses métabolomiques et lipidomiques de larges cohortes“. Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066281/document.
Global metabolomic profiling of biological media in large sample sets is a major challenge. In this context, our work aims to develop LC-HRMS approaches and data mining tools for metabolomics and lipidomics analysis of large cohorts. We have first developed and evaluated the reliability of four LC-HRMS methods in the annotation of human serum metabolome and lipidome. Thus, spectral database was implemented using MS spectra, MS/MS and retention times of reference compounds to further ensure datasets annotation. The combination of RP, PFPP and HILIC-HRMS methods allowed identification of 266 metabolites and 706 lipid species in human serum over 20 to 24 chemical classes respectively including 27% of isomeric species. These analytical tools were then applied for the stratification of 78 diabetic patients. Unsurprisingly, we highlighted a metabolic syndrome (energy metabolism disruption), moreover our analyses have shown the deleterious impact of confounding physiological factors on diabetes biomarker discovery –age and BMI-. We finally evaluated their influence on a cohort of 227 CEA employees. Lipidomic fingerprints are robust, however BMI impact is marked for neutral lipids. Gender effect shows significant male catabolism and age altered enzyme activities. These studies combine an overall metabolomics and lipidomics analyses of the same human samples. They aim to build up a relational database including spectral and biological data for biomarker characterization in clinical studies
Díaz, San Pedro Ramón. „Potential of LC-(Q)TOF MS in target and non-target analyses: wide scope screening of organic contaminants and metabolomic applications“. Doctoral thesis, Universitat Jaume I, 2016. http://hdl.handle.net/10803/669026.
Conan, Cécile. „Metabolomics investigations of seaweed extracts used as plant growth biostimulants and transcriptomic studies of their physiological effects on A. thaliana“. Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066760.
To further develop a sustainable agriculture, new bio-solutions include the use of biostimulants such as seaweed aqueous extracts to improve plant growth or/and alleviate the effect of biotic and abiotic stress. These commercial products aim to improve plant nutrition, in order to impact yield and quality parameters. In this domain, some modes of action have been proposed by the Goëmar-Arysta R&D center. However, the bioactive ingredients have not been identified so far, using classical methods of bioassay-guided fractionation. Therefore, their mechanisms of action remain also elusive. The aim of this thesis project was first to identify, using a strategy of metabolomic profiling of seaweed extracts, the bioactive compounds responsible for plant growth stimulation. The 1H-NMR-based profiling and LC-MS metabolomic analyses of commercial seaweed extracts were not suitable to identify candidate molecules that promote plant growth. A classical bioassay-guided fractionation achieved on a Goëmar extract provided a growth promoting purified fraction and further bioactive sub-fractions. The U-HPLC-HR-MS analyses of these sub-fractions highlighted two candidate molecules. A fractionation process used in this work should be patented in order to improve added-value of growth-promoting filtrate and valorize new by-products. In parallel, the physiological effects of these seaweed extracts were studied in the model plant Arabidopsis thaliana through transcriptomic approaches in order to decipher patterns of gene regulation in response to a crude commercial extract and its purified fraction. The transcriptome in response to the application of seaweed extract was completely different of those obtained using its purified fraction. Genes dysregulated by this purified fraction provided potential biomarkers of plant growth that could be used. to assist the bioactive molecule isolation. Finally these two approaches combining, metabolomics-guided and bioassay-guided fractionation of extracts from the brown seaweed Ascophyllum nodosum, and global transcriptomics in Arabidopsis provided several new insights into the nature and structure of different molecules that trigger different physiological responses in plants
Karimpour, Masoumeh. „Multi-platform metabolomics assays to study the responsiveness of the human plasma and lung lavage metabolome“. Doctoral thesis, Umeå universitet, Kemiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-120591.
Metabolomik har använts för att spåra förändringar och störningar i kroppens funktioner genom undersökning av metabolit-profiler. I detta avhandlingasarbete har huvudfokus varit på tillämpning av flera olika analytiska plattformar för metabolomikstudier av det mänskliga metabolomet efter exponering för olika kost och avgasutsläpp från biodieselbränsle. De sofistikerade analytiska plattformarna som användes för detta ändamål var kärnmagnetisk resonans (NMR), samt gaskromatografi (GC) och vätskekromatografi (LC) kopplat till masspektrometri (MS). Varje plattform erbjöd unika karakteriseringsmöjligheter med detektion och identifiering av specifika grupper av metaboliter. Användningen av multipattformmetabolomik förbättrade täckningen av metabolomet och genererade kompletterande resultat som möjliggjorde en bättre förståelse av de biokemiska processer som reflekteras av metabolitprofilerna. Med hjälp av breda analyser har ett stort antal okända metaboliter i plasma identifierats under den postprandial fasen efter en väldefinerad måltid (i Paper I). Dessutom har ett stort antal metaboliter påvisats och identifierats i lungsköljvätska efter exponering av biodieselavgaser jämfört med kontollexponering med filtrerad luft (i Paper II). Parallellt med dessa breda analyser har också riktade analyser genomförts av både lungsköljvätska och plasma. Därigenom har bioaktiva lipider detekterats och kvantifieras efter avgasexponering och resultaten har jämförts med filtrerad luft som kontrollexponering (Paper III och IV). Processning av rådata följt av dataanalys, med både univariata och multivariata metoder möjliggjorde screening och fördjupad undersökning av förändringen i metabolitnivåer. I den första pilotstudien av postprandiala nivåer var syftet att undersöka responsen i plasmametabolomet efter en väldefinierad måltid under den postprandiala fasen vid två olika typer av kost. Resultaten visade att oberoende av kosten, så återvände metabolitnivåerna till sina baslinjenivåer tre timmar efter måltiden. Detta togs i beaktande vid exponeringsstudierna för biodieselavgaser, som designades så att dietens inverkan minimerades. Både breda och riktade analyser resulterade i viktiga resultat. Exempelvis så detekterades olika metabolitprofiler i bronkiell sköljvätska (BW) jämfört med bronkoalveolär sköljvätska (BAL), speciellt med NMR och LC-MS. Dessutom resulterade avgasexponering i förändrade metabolitprofiler, observerade med GC-MS, särskilt i BAL. Dessutom uppvisade fettsyrametaboliter i BW, BAL och plasma förändrade halter efter avgasexponering, uppmätt genom en riktad LC-MS/MS-analys. Sammanfattningsvis så visade sig de nya metoderna som utvecklats för att undersöka förändringar i metabolithalterna i plasma och lungsköljvätska fungera väl ur ett analytiskt perspektiv och resulterade i viktiga biologiska fynd. Fördjupade studier behövs dock för att validera resultaten.
Bücher zum Thema "Metabolomic analyses":
Xia, Yinglin, und Jun Sun. Microbiome and Metabolomics: Statistical Data Analyses. Washington, DC, USA: American Chemical Society, 2022. http://dx.doi.org/10.1021/acsinfocus.7e5003.
Vaidyanathan, Seetharaman, George G. Harrigan und Royston Goodacre, Hrsg. Metabolome Analyses: Strategies for Systems Biology. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/b106967.
Li, Shuzhao, Hrsg. Computational Methods and Data Analysis for Metabolomics. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0239-3.
Xia, Yinglin, Jun Sun und Xiaotao Shen, Ph.D., Stanford University School of Medicine. Statistical Data Analysis of Microbiomes and Metabolomics. Washington, DC, USA: American Chemical Society, 2022. http://dx.doi.org/10.1021/acsinfocus.7e5035.
Datta, Susmita, und Bart J. A. Mertens, Hrsg. Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-45809-0.
Roessner, Ute, und Daniel Anthony Dias. Metabolomics tools for natural product discovery: Methods and protocols. New York: Humana Press, 2013.
Bagchi, Debasis. Genomics, proteomics, and metabolomics in nutraceuticals and functional foods. Ames, Iowa: Wiley-Blackwell, 2010.
Bagchi, Debasis, Anand Swaroop und Manashi Bagchi. Genomics, proteomics and metabolomics in nutraceuticals and functional foods. Chichester, West Sussex: John Wiley & Sons, Inc., 2015.
Bjerrum, Jacob T. Metabonomics: Methods and protocols. New York: Humana Press, 2015.
Armitage, Emily G. Correlation-based network analysis of cancer metabolism: A new systems biology approach in metabolomics. New York: Springer, 2014.
Buchteile zum Thema "Metabolomic analyses":
Buszewska-Forajta, Magdalena, Joanna Raczak-Gutknecht, Anna Rajska und Michał J. Markuszewski. „Metabolomic Analyses of Natural Medicinal Products“. In Handbook of Bioanalytics, 1–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63957-0_21-1.
Kanani, H., B. Dutta, J. Quackenbush und M. I. Klapa. „Time-Series Integrated Metabolomic and Transcriptional Profiling Analyses“. In Concepts in Plant Metabolomics, 93–110. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5608-6_7.
Richard, Vincent R., Simon D. Bourque und Vladimir I. Titorenko. „Metabolomic and Lipidomic Analyses of Chronologically Aging Yeast“. In Methods in Molecular Biology, 359–73. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1363-3_21.
Hayashi, Yohei, und Yasuhisa Matsui. „Metabolomic and Proteomic Analyses of Mouse Primordial Germ Cells“. In Stem Cells and Aging, 259–69. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/7651_2018_164.
Marrocco, Cristina, Angelo D’Alessandro, Sara Rinalducci, Cristiana Mirasole und Lello Zolla. „Untargeted metabolomic analyses open new scenarios in post mortem pig muscles: Casertana and Large White“. In Farm animal proteomics 2013, 270–73. Wageningen: Wageningen Academic Publishers, 2013. http://dx.doi.org/10.3920/978-90-8686-776-9_68.
Scholz, Matthias, und Joachim Selbig. „Visualization and Analysis of Molecular Data“. In Metabolomics, 87–104. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-244-1_6.
Wishart, David S. „Metabolomic Data Exploration and Analysis with the Human Metabolome Database“. In Computational Methods and Data Analysis for Metabolomics, 165–84. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0239-3_10.
Schuster, Stefan, Axel Kamp und Mikhail Pachkov. „Understanding the Roadmap of Metabolism by Pathway Analysis“. In Metabolomics, 199–226. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-244-1_12.
Dailey, Allyson L. „Metabolomic Bioinformatic Analysis“. In Methods in Molecular Biology, 341–52. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6990-6_22.
Steuer, Ralf, Katja Morgenthal, Wolfram Weckwerth und Joachim Selbig. „A Gentle Guide to the Analysis of Metabolomic Data“. In Metabolomics, 105–26. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-244-1_7.
Konferenzberichte zum Thema "Metabolomic analyses":
Yang, P., X. Hu, E. Iffrig, G. S. Martin, D. P. Jones und A. M. Esper. „Serial Metabolomic Analyses in Sepsis-Induced Acute Respiratory Distress Syndrome“. In American Thoracic Society 2022 International Conference, May 13-18, 2022 - San Francisco, CA. American Thoracic Society, 2022. http://dx.doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a5139.
Ha, Soo Jung, Gordon Showalter, Jenna Rickus, Shanbao Cai, Haiyan Wang, Wei Michael Liu, Jann N. Sarkaria et al. „Abstract B37: Proteomic and metabolomic analyses of glioblastoma using mass spectrometry.“ In Abstracts: AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics--Oct 19-23, 2013; Boston, MA. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1535-7163.targ-13-b37.
Chu, Ching-Yu, Szu-Yuan Chen, Fu-Yu Chueh, Mei-Ling Cheng und Chao-Lan Yu. „Abstract 2564: Integrated transcriptomic, proteomic, and metabolomic analyses of human and mouse T cell leukemia“. In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-2564.
„PREDICTED RELATIVE METABOLOMIC TURNOVER - Predicting Changes in the Environmental Metabolome from the Metagenome“. In Metagenomic Sequence Data Analysis. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003314803370345.
Chang, Chun, Ying Liang, Juan Wang, Yongchang Sun und Wanzhen Yao. „Metabolomic profiling differences among asthma, COPD and healthy controls: a LC-MS-based metabolomics analysis“. In ERS International Congress 2019 abstracts. European Respiratory Society, 2019. http://dx.doi.org/10.1183/13993003.congress-2019.pa1701.
Ngo, D., B. Peterson, R. Montanez, T. Sofer, J. Morningstar, X. Shi, D. J. Gottlieb et al. „Metabolomic Analysis of Obstructive Sleep Apnea Traits“. In American Thoracic Society 2019 International Conference, May 17-22, 2019 - Dallas, TX. American Thoracic Society, 2019. http://dx.doi.org/10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a7339.
Cardoso, Sara, Miguel Rocha, Telma Afonso und Marcelo Maraschin. „WebSpecmine: a website for metabolomics data analysis and mining.“ In 3rd International Electronic Conference on Metabolomics. Basel, Switzerland: MDPI, 2018. http://dx.doi.org/10.3390/iecm-3-05842.
Occhipinti, Annalisa, und Claudio Angione. „A Computational Model of Cancer Metabolism for Personalised Medicine“. In Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.
González-Domínguez, Raúl, Ana Sayago und Ángeles Fernández-Recamales. „Comparison of complementary statistical analysis approaches in metabolomic food traceability“. In 3rd International Electronic Conference on Metabolomics. Basel, Switzerland: MDPI, 2018. http://dx.doi.org/10.3390/iecm-3-05839.
Laatikainen, Reino, Pekka Laatikainen und Elias Hakalehto. „QUANTITATIVE QUANTUM MECHANICAL NMR ANALYSIS: THE SUPERIOR TOOL FOR ANALYSIS OF BIOFLUIDS“. In The 1st International Electronic Conference on Metabolomics. Basel, Switzerland: MDPI, 2016. http://dx.doi.org/10.3390/iecm-1-c005.
Berichte der Organisationen zum Thema "Metabolomic analyses":
Aharoni, Asaph, Zhangjun Fei, Efraim Lewinsohn, Arthur Schaffer und Yaakov Tadmor. System Approach to Understanding the Metabolic Diversity in Melon. United States Department of Agriculture, Juli 2013. http://dx.doi.org/10.32747/2013.7593400.bard.
Arp, Daniel, und Luis Sayavedra-Soto. Metabolomic Functional Analysis of Bacterial Genomes: Final Report. Office of Scientific and Technical Information (OSTI), Januar 2008. http://dx.doi.org/10.2172/951563.
Rabinowitz, Joshua D. Final Technical Report--Quantitative analysis of metabolic regulation by integration of metabolomics, proteomics, and fluxomics. Office of Scientific and Technical Information (OSTI), Dezember 2018. http://dx.doi.org/10.2172/1487155.
Lidstrom, Mary E., Ludmila Chistoserdova, Marina G. Kalyuzhnaya, Victoria J. Orphan und David A. Beck. Systems level insights into alternate methane cycling modes in a freshwater lake via community transcriptomics, metabolomics and nano-SIMS analysis. Office of Scientific and Technical Information (OSTI), August 2014. http://dx.doi.org/10.2172/1149958.
Cytryn, Eddie, Mark R. Liles und Omer Frenkel. Mining multidrug-resistant desert soil bacteria for biocontrol activity and biologically-active compounds. United States Department of Agriculture, Januar 2014. http://dx.doi.org/10.32747/2014.7598174.bard.
Mizrahi, Itzhak, und Bryan A. White. Exploring the role of the rumen microbiota in determining the feed efficiency of dairy cows. United States Department of Agriculture, Oktober 2011. http://dx.doi.org/10.32747/2011.7594403.bard.
Jander, Georg, und Daniel Chamovitz. Investigation of growth regulation by maize benzoxazinoid breakdown products. United States Department of Agriculture, Januar 2015. http://dx.doi.org/10.32747/2015.7600031.bard.