Disease Type- and Status-Specific Alteration of CSF Metabolome Coordinated with Clinical Parameters in Inflammatory Demyelinating Diseases of CNS
Central nervous system (CNS) inflammatory demyelinating diseases (IDDs) are a group of disorders with different aetiologies, characterized by inflammatory lesions. These disorders include multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and idiopathic transverse myelitis (ITM...
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| description | Central nervous system (CNS) inflammatory demyelinating diseases (IDDs) are a group of disorders with different aetiologies, characterized by inflammatory lesions. These disorders include multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and idiopathic transverse myelitis (ITM). Differential diagnosis of the CNS IDDs still remains challenging due to frequent overlap of clinical and radiological manifestation, leading to increased demands for new biomarker discovery. Since cerebrospinal fluid (CSF) metabolites may reflect the status of CNS tissues and provide an interfacial linkage between blood and CNS tissues, we explored multi-component biomarker for different IDDs from CSF samples using gas chromatography mass spectrometry-based metabolite profiling coupled to multiplex bioinformatics approach. We successfully constructed the single model with multiple metabolite variables in coordinated regression with clinical characteristics, expanded disability status scale, oligoclonal bands, and protein levels. The multi-composite biomarker simultaneously discriminated four different immune statuses (a total of 145 samples; 54 MS, 49 NMOSD, 30 ITM, and 12 normal controls). Furthermore, systematic characterization of transitional metabolic modulation identified relapse-associated metabolites and proposed insights into the disease network underlying type-specific metabolic dysfunctionality. The comparative analysis revealed the lipids, 1-monopalmitin and 1-monostearin were common indicative for MS, NMOSD, and ITM whereas fatty acids were specific for the relapse identified in all types of IDDs. |
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These disorders include multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and idiopathic transverse myelitis (ITM). Differential diagnosis of the CNS IDDs still remains challenging due to frequent overlap of clinical and radiological manifestation, leading to increased demands for new biomarker discovery. Since cerebrospinal fluid (CSF) metabolites may reflect the status of CNS tissues and provide an interfacial linkage between blood and CNS tissues, we explored multi-component biomarker for different IDDs from CSF samples using gas chromatography mass spectrometry-based metabolite profiling coupled to multiplex bioinformatics approach. We successfully constructed the single model with multiple metabolite variables in coordinated regression with clinical characteristics, expanded disability status scale, oligoclonal bands, and protein levels. The multi-composite biomarker simultaneously discriminated four different immune statuses (a total of 145 samples; 54 MS, 49 NMOSD, 30 ITM, and 12 normal controls). Furthermore, systematic characterization of transitional metabolic modulation identified relapse-associated metabolites and proposed insights into the disease network underlying type-specific metabolic dysfunctionality. The comparative analysis revealed the lipids, 1-monopalmitin and 1-monostearin were common indicative for MS, NMOSD, and ITM whereas fatty acids were specific for the relapse identified in all types of IDDs.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0166277</identifier><identifier>PMID: 27855220</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Amino acids ; Aquaporins ; Area Under Curve ; Bioindicators ; Bioinformatics ; Biology and Life Sciences ; Biomarkers ; Biomarkers - cerebrospinal fluid ; Brain research ; Cancer ; Central nervous system ; Central Nervous System - metabolism ; Central Nervous System - pathology ; Cerebrospinal fluid ; Comparative analysis ; Computer and Information Sciences ; Demyelinating diseases ; Demyelinating Diseases - cerebrospinal fluid ; Demyelinating Diseases - complications ; Demyelination ; Differential diagnosis ; Discriminant Analysis ; Disease ; Disorders ; Fatty acids ; Fermentation ; Gas chromatography ; Glycerol ; Hospitals ; Humans ; Inflammation - cerebrospinal fluid ; Inflammation - complications ; Inflammatory diseases ; Least-Squares Analysis ; Lesions ; Lipid metabolism ; Lipids ; Mass spectrometry ; Mass spectroscopy ; Medicine and Health Sciences ; Metabolism ; Metabolites ; Metabolome ; Models, Statistical ; Multiple sclerosis ; Multivariate Analysis ; Myelitis ; Neurology ; Neuromyelitis ; Recurrence ; Regression analysis ; ROC Curve ; Scientific imaging ; Tissues</subject><ispartof>PloS one, 2016-11, Vol.11 (11), p.e0166277-e0166277</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Park et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016 Park et al 2016 Park et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c725t-dc3d8db107a5260b3aaf95fe3a42a11c87535c7d7dcbe5a2d1caccc0b8a1fc1d3</citedby><cites>FETCH-LOGICAL-c725t-dc3d8db107a5260b3aaf95fe3a42a11c87535c7d7dcbe5a2d1caccc0b8a1fc1d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5113962/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5113962/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27855220$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Klein, Robyn S</contributor><creatorcontrib>Park, Soo Jin</creatorcontrib><creatorcontrib>Jeong, In Hye</creatorcontrib><creatorcontrib>Kong, Byung Soo</creatorcontrib><creatorcontrib>Lee, Jung-Eun</creatorcontrib><creatorcontrib>Kim, Kyoung Heon</creatorcontrib><creatorcontrib>Lee, Do Yup</creatorcontrib><creatorcontrib>Kim, Ho Jin</creatorcontrib><title>Disease Type- and Status-Specific Alteration of CSF Metabolome Coordinated with Clinical Parameters in Inflammatory Demyelinating Diseases of CNS</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Central nervous system (CNS) inflammatory demyelinating diseases (IDDs) are a group of disorders with different aetiologies, characterized by inflammatory lesions. These disorders include multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and idiopathic transverse myelitis (ITM). Differential diagnosis of the CNS IDDs still remains challenging due to frequent overlap of clinical and radiological manifestation, leading to increased demands for new biomarker discovery. Since cerebrospinal fluid (CSF) metabolites may reflect the status of CNS tissues and provide an interfacial linkage between blood and CNS tissues, we explored multi-component biomarker for different IDDs from CSF samples using gas chromatography mass spectrometry-based metabolite profiling coupled to multiplex bioinformatics approach. We successfully constructed the single model with multiple metabolite variables in coordinated regression with clinical characteristics, expanded disability status scale, oligoclonal bands, and protein levels. The multi-composite biomarker simultaneously discriminated four different immune statuses (a total of 145 samples; 54 MS, 49 NMOSD, 30 ITM, and 12 normal controls). Furthermore, systematic characterization of transitional metabolic modulation identified relapse-associated metabolites and proposed insights into the disease network underlying type-specific metabolic dysfunctionality. The comparative analysis revealed the lipids, 1-monopalmitin and 1-monostearin were common indicative for MS, NMOSD, and ITM whereas fatty acids were specific for the relapse identified in all types of IDDs.</description><subject>Amino acids</subject><subject>Aquaporins</subject><subject>Area Under Curve</subject><subject>Bioindicators</subject><subject>Bioinformatics</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Biomarkers - cerebrospinal fluid</subject><subject>Brain research</subject><subject>Cancer</subject><subject>Central nervous system</subject><subject>Central Nervous System - metabolism</subject><subject>Central Nervous System - pathology</subject><subject>Cerebrospinal fluid</subject><subject>Comparative analysis</subject><subject>Computer and Information Sciences</subject><subject>Demyelinating diseases</subject><subject>Demyelinating Diseases - cerebrospinal fluid</subject><subject>Demyelinating Diseases - complications</subject><subject>Demyelination</subject><subject>Differential diagnosis</subject><subject>Discriminant Analysis</subject><subject>Disease</subject><subject>Disorders</subject><subject>Fatty acids</subject><subject>Fermentation</subject><subject>Gas chromatography</subject><subject>Glycerol</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Inflammation - cerebrospinal fluid</subject><subject>Inflammation - complications</subject><subject>Inflammatory diseases</subject><subject>Least-Squares Analysis</subject><subject>Lesions</subject><subject>Lipid metabolism</subject><subject>Lipids</subject><subject>Mass spectrometry</subject><subject>Mass spectroscopy</subject><subject>Medicine and Health Sciences</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Metabolome</subject><subject>Models, Statistical</subject><subject>Multiple sclerosis</subject><subject>Multivariate Analysis</subject><subject>Myelitis</subject><subject>Neurology</subject><subject>Neuromyelitis</subject><subject>Recurrence</subject><subject>Regression analysis</subject><subject>ROC Curve</subject><subject>Scientific imaging</subject><subject>Tissues</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9Fu0zAUhiMEYmPwBggsISG4aLGdOHFukKqOQaXBEB3cWie203py7BI7QB-DN8bdsmlFu5hykejk-38f__bJsucET0lekXcXfugd2OnGOz3FpCxpVT3IDkmd00lJcf7w1vdB9iSEC4xZzsvycXZAK84Ypfgw-3tsgoag0fl2oycInELLCHEIk-VGS9MaiWY26h6i8Q75Fs2XJ-izjtB46zuN5t73yjiIWqHfJq7R3BpnJFj0FXrodJIGZBxauNZC10H0_RYd626r7U5l3AqNHYRL9y_Lp9mjFmzQz8b3Ufb95MP5_NPk9OzjYj47nciKsjhRMldcNQRXwGiJmxygrVmrcygoECJ5xXImK1Up2WgGVBEJUkrccCCtJCo_yl5e-W6sD2JMMwjCC0IYT2EmYnFFKA8XYtObDvqt8GDEZcH3KwF9NNJqwTCnueRMYlYXGIq6pISUTQ284YVkJHm9H1cbmk4rqV3swe6Z7v9xZi1W_pdghOTJLRm8GQ16_3PQIYrOBKmtBaf9cNl3WRDOaXkflFR1VXCe0Ff_oXcHMVIrSHs1rvWpRbkzFbOiIinqsmaJmt5BpUfpzsh0S1uT6nuCt3uCxET9J65gCEEslt_uz5792Gdf32LXGmxcB2-H3RUO-2BxBcreh9Dr9uY8CBa7IbtOQ-yGTIxDlmQvbp_ljeh6qvJ_CToivw</recordid><startdate>20161117</startdate><enddate>20161117</enddate><creator>Park, Soo Jin</creator><creator>Jeong, In Hye</creator><creator>Kong, Byung Soo</creator><creator>Lee, Jung-Eun</creator><creator>Kim, Kyoung Heon</creator><creator>Lee, Do Yup</creator><creator>Kim, Ho Jin</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20161117</creationdate><title>Disease Type- and Status-Specific Alteration of CSF Metabolome Coordinated with Clinical Parameters in Inflammatory Demyelinating Diseases of CNS</title><author>Park, Soo Jin ; Jeong, In Hye ; Kong, Byung Soo ; Lee, Jung-Eun ; Kim, Kyoung Heon ; Lee, Do Yup ; Kim, Ho Jin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c725t-dc3d8db107a5260b3aaf95fe3a42a11c87535c7d7dcbe5a2d1caccc0b8a1fc1d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Amino acids</topic><topic>Aquaporins</topic><topic>Area Under Curve</topic><topic>Bioindicators</topic><topic>Bioinformatics</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Biomarkers - cerebrospinal fluid</topic><topic>Brain research</topic><topic>Cancer</topic><topic>Central nervous system</topic><topic>Central Nervous System - metabolism</topic><topic>Central Nervous System - pathology</topic><topic>Cerebrospinal fluid</topic><topic>Comparative analysis</topic><topic>Computer and Information Sciences</topic><topic>Demyelinating diseases</topic><topic>Demyelinating Diseases - cerebrospinal fluid</topic><topic>Demyelinating Diseases - complications</topic><topic>Demyelination</topic><topic>Differential diagnosis</topic><topic>Discriminant Analysis</topic><topic>Disease</topic><topic>Disorders</topic><topic>Fatty acids</topic><topic>Fermentation</topic><topic>Gas chromatography</topic><topic>Glycerol</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Inflammation - cerebrospinal fluid</topic><topic>Inflammation - complications</topic><topic>Inflammatory diseases</topic><topic>Least-Squares Analysis</topic><topic>Lesions</topic><topic>Lipid metabolism</topic><topic>Lipids</topic><topic>Mass spectrometry</topic><topic>Mass spectroscopy</topic><topic>Medicine and Health Sciences</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>Metabolome</topic><topic>Models, Statistical</topic><topic>Multiple sclerosis</topic><topic>Multivariate Analysis</topic><topic>Myelitis</topic><topic>Neurology</topic><topic>Neuromyelitis</topic><topic>Recurrence</topic><topic>Regression analysis</topic><topic>ROC Curve</topic><topic>Scientific imaging</topic><topic>Tissues</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Park, Soo Jin</creatorcontrib><creatorcontrib>Jeong, In Hye</creatorcontrib><creatorcontrib>Kong, Byung Soo</creatorcontrib><creatorcontrib>Lee, Jung-Eun</creatorcontrib><creatorcontrib>Kim, Kyoung Heon</creatorcontrib><creatorcontrib>Lee, Do Yup</creatorcontrib><creatorcontrib>Kim, Ho Jin</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Park, Soo Jin</au><au>Jeong, In Hye</au><au>Kong, Byung Soo</au><au>Lee, Jung-Eun</au><au>Kim, Kyoung Heon</au><au>Lee, Do Yup</au><au>Kim, Ho Jin</au><au>Klein, Robyn S</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Disease Type- and Status-Specific Alteration of CSF Metabolome Coordinated with Clinical Parameters in Inflammatory Demyelinating Diseases of CNS</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2016-11-17</date><risdate>2016</risdate><volume>11</volume><issue>11</issue><spage>e0166277</spage><epage>e0166277</epage><pages>e0166277-e0166277</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Central nervous system (CNS) inflammatory demyelinating diseases (IDDs) are a group of disorders with different aetiologies, characterized by inflammatory lesions. These disorders include multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and idiopathic transverse myelitis (ITM). Differential diagnosis of the CNS IDDs still remains challenging due to frequent overlap of clinical and radiological manifestation, leading to increased demands for new biomarker discovery. Since cerebrospinal fluid (CSF) metabolites may reflect the status of CNS tissues and provide an interfacial linkage between blood and CNS tissues, we explored multi-component biomarker for different IDDs from CSF samples using gas chromatography mass spectrometry-based metabolite profiling coupled to multiplex bioinformatics approach. We successfully constructed the single model with multiple metabolite variables in coordinated regression with clinical characteristics, expanded disability status scale, oligoclonal bands, and protein levels. The multi-composite biomarker simultaneously discriminated four different immune statuses (a total of 145 samples; 54 MS, 49 NMOSD, 30 ITM, and 12 normal controls). Furthermore, systematic characterization of transitional metabolic modulation identified relapse-associated metabolites and proposed insights into the disease network underlying type-specific metabolic dysfunctionality. The comparative analysis revealed the lipids, 1-monopalmitin and 1-monostearin were common indicative for MS, NMOSD, and ITM whereas fatty acids were specific for the relapse identified in all types of IDDs.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27855220</pmid><doi>10.1371/journal.pone.0166277</doi><tpages>e0166277</tpages><oa>free_for_read</oa></addata></record> |
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| issn | 1932-6203 1932-6203 |
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| subjects | Amino acids Aquaporins Area Under Curve Bioindicators Bioinformatics Biology and Life Sciences Biomarkers Biomarkers - cerebrospinal fluid Brain research Cancer Central nervous system Central Nervous System - metabolism Central Nervous System - pathology Cerebrospinal fluid Comparative analysis Computer and Information Sciences Demyelinating diseases Demyelinating Diseases - cerebrospinal fluid Demyelinating Diseases - complications Demyelination Differential diagnosis Discriminant Analysis Disease Disorders Fatty acids Fermentation Gas chromatography Glycerol Hospitals Humans Inflammation - cerebrospinal fluid Inflammation - complications Inflammatory diseases Least-Squares Analysis Lesions Lipid metabolism Lipids Mass spectrometry Mass spectroscopy Medicine and Health Sciences Metabolism Metabolites Metabolome Models, Statistical Multiple sclerosis Multivariate Analysis Myelitis Neurology Neuromyelitis Recurrence Regression analysis ROC Curve Scientific imaging Tissues |
| title | Disease Type- and Status-Specific Alteration of CSF Metabolome Coordinated with Clinical Parameters in Inflammatory Demyelinating Diseases of CNS |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T19%3A23%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Disease%20Type-%20and%20Status-Specific%20Alteration%20of%20CSF%20Metabolome%20Coordinated%20with%20Clinical%20Parameters%20in%20Inflammatory%20Demyelinating%20Diseases%20of%20CNS&rft.jtitle=PloS%20one&rft.au=Park,%20Soo%20Jin&rft.date=2016-11-17&rft.volume=11&rft.issue=11&rft.spage=e0166277&rft.epage=e0166277&rft.pages=e0166277-e0166277&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0166277&rft_dat=%3Cgale_plos_%3EA471875695%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1841158277&rft_id=info:pmid/27855220&rft_galeid=A471875695&rft_doaj_id=oai_doaj_org_article_50823c85c05940a4962116b9a8b84c51&rfr_iscdi=true |