Utility of Novel Plasma Metabolic Markers in the Diagnosis of Pediatric Tuberculosis: A Classification and Regression Tree Analysis Approach
Although tuberculosis (TB) has been the greatest killer due to a single infectious disease, pediatric TB is still hard to diagnose because of the lack of sensitive biomarkers. Metabolomics is increasingly being applied in infectious diseases. But little is known regarding metabolic biomarkers in chi...
Saved in:
Published in | Journal of proteome research Vol. 15; no. 9; pp. 3118 - 3125 |
---|---|
Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
American Chemical Society
02.09.2016
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Although tuberculosis (TB) has been the greatest killer due to a single infectious disease, pediatric TB is still hard to diagnose because of the lack of sensitive biomarkers. Metabolomics is increasingly being applied in infectious diseases. But little is known regarding metabolic biomarkers in children with TB. A combination of a NMR-based plasma metabolic method and classification and regression tree (CART) analysis was used to provide a broader range of applications in TB diagnosis in our study. Plasma samples obtained from 28 active TB children and 37 non-TB controls (including 21 RTIs and 16 healthy children) were analyzed by an orthogonal partial least-squares discriminant analysis (OPLS-DA) model, and 17 metabolites were identified that can separate children with TB from non-TB controls. CART analysis was then used to choose 3 of the markers, l-valine, pyruvic acid, and betaine, with the least error. The sensitivity, specificity, and area under the curve (AUC) of the 3 metabolites is 85.7% (24/28, 95% CI, 66.4%, 95.3%), 94.6% (35/37, 95% CI, 80.5%, 99.1%), and 0.984(95% CI, 0.917, 1.000), respectively. The 3 metabolites demonstrated sensitivity of 82.4% (14/17, 95% CI, 55.8%, 95.3%) and specificity of 83.9% (26/31, 95% CI, 65.5%, 93.9%), respectively, in 48 blinded subjects in an independent cohort. Taken together, the novel plasma metabolites are potentially useful for diagnosis of pediatric TB and would provide insights into the disease mechanism. |
---|---|
AbstractList | Although tuberculosis (TB) has been the greatest killer due to a single infectious disease, pediatric TB is still hard to diagnose because of the lack of sensitive biomarkers. Metabolomics is increasingly being applied in infectious diseases. But little is known regarding metabolic biomarkers in children with TB. A combination of a NMR-based plasma metabolic method and classification and regression tree (CART) analysis was used to provide a broader range of applications in TB diagnosis in our study. Plasma samples obtained from 28 active TB children and 37 non-TB controls (including 21 RTIs and 16 healthy children) were analyzed by an orthogonal partial least-squares discriminant analysis (OPLS-DA) model, and 17 metabolites were identified that can separate children with TB from non-TB controls. CART analysis was then used to choose 3 of the markers, l-valine, pyruvic acid, and betaine, with the least error. The sensitivity, specificity, and area under the curve (AUC) of the 3 metabolites is 85.7% (24/28, 95% CI, 66.4%, 95.3%), 94.6% (35/37, 95% CI, 80.5%, 99.1%), and 0.984(95% CI, 0.917, 1.000), respectively. The 3 metabolites demonstrated sensitivity of 82.4% (14/17, 95% CI, 55.8%, 95.3%) and specificity of 83.9% (26/31, 95% CI, 65.5%, 93.9%), respectively, in 48 blinded subjects in an independent cohort. Taken together, the novel plasma metabolites are potentially useful for diagnosis of pediatric TB and would provide insights into the disease mechanism. Although tuberculosis (TB) has been the greatest killer due to a single infectious disease, pediatric TB is still hard to diagnose because of the lack of sensitive biomarkers. Metabolomics is increasingly being applied in infectious diseases. But little is known regarding metabolic biomarkers in children with TB. A combination of a NMR-based plasma metabolic method and classification and regression tree (CART) analysis was used to provide a broader range of applications in TB diagnosis in our study. Plasma samples obtained from 28 active TB children and 37 non-TB controls (including 21 RTIs and 16 healthy children) were analyzed by an orthogonal partial least-squares discriminant analysis (OPLS-DA) model, and 17 metabolites were identified that can separate children with TB from non-TB controls. CART analysis was then used to choose 3 of the markers, l-valine, pyruvic acid, and betaine, with the least error. The sensitivity, specificity, and area under the curve (AUC) of the 3 metabolites is 85.7% (24/28, 95% CI, 66.4%, 95.3%), 94.6% (35/37, 95% CI, 80.5%, 99.1%), and 0.984(95% CI, 0.917, 1.000), respectively. The 3 metabolites demonstrated sensitivity of 82.4% (14/17, 95% CI, 55.8%, 95.3%) and specificity of 83.9% (26/31, 95% CI, 65.5%, 93.9%), respectively, in 48 blinded subjects in an independent cohort. Taken together, the novel plasma metabolites are potentially useful for diagnosis of pediatric TB and would provide insights into the disease mechanism. |
Author | Qi, Hui Jiao, Wei-wei Song, Wen-qi Shen, A-dong Li, Jie-qiong Dong, Fang Ren, Na Shen, Chen Sun, Lin Xiao, Jing Xu, Fang |
AuthorAffiliation | Capital Medical University Key Laboratory of Major Diseases in Children, Ministry of Education, National Key Discipline of Pediatrics (Capital Medical University), Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Beijing Pediatric Research Institute, Beijing Children’s Hospital |
AuthorAffiliation_xml | – name: Key Laboratory of Major Diseases in Children, Ministry of Education, National Key Discipline of Pediatrics (Capital Medical University), Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Beijing Pediatric Research Institute, Beijing Children’s Hospital – name: Capital Medical University |
Author_xml | – sequence: 1 givenname: Lin surname: Sun fullname: Sun, Lin – sequence: 2 givenname: Jie-qiong surname: Li fullname: Li, Jie-qiong – sequence: 3 givenname: Na surname: Ren fullname: Ren, Na – sequence: 4 givenname: Hui surname: Qi fullname: Qi, Hui – sequence: 5 givenname: Fang surname: Dong fullname: Dong, Fang – sequence: 6 givenname: Jing surname: Xiao fullname: Xiao, Jing – sequence: 7 givenname: Fang surname: Xu fullname: Xu, Fang – sequence: 8 givenname: Wei-wei surname: Jiao fullname: Jiao, Wei-wei – sequence: 9 givenname: Chen surname: Shen fullname: Shen, Chen – sequence: 10 givenname: Wen-qi surname: Song fullname: Song, Wen-qi email: songwenqi1218@163.com – sequence: 11 givenname: A-dong surname: Shen fullname: Shen, A-dong email: shenad16@hotmail.com |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27451809$$D View this record in MEDLINE/PubMed |
BookMark | eNqFUctu2zAQJIoUzaP9hBY89mJnKZqW1J4M9xUgSYPCORMrapUwpUSHpAr4H_rRpWO7h1wCECCXOzOLnTllR4MfiLH3AqYCCnGOJk4f1sEn8j1N5w1AUVSv2IlQUk1kDeXR4V3V8pidxvgAIFQJ8g07LsqZEhXUJ-zvbbLOpg33Hb_2f8jxG4exR35FCRvvrOFXGH5TiNwOPN0T_2LxbvDRxi3lhlqLKWTUamwomNFtO5_4gi-zTLSdNZisHzgOLf9Fd4HyZy5XgYgvBnSbrdBinRdBc_-Wve7QRXq3v8_Y7bevq-WPyeXP7xfLxeUEpRJpUoEUdYEzmhuQbVHMAPJRhma1VC3KslRN0YJsatU1QB1UsqHWmGwbkOiEPGMfd7p57ONIMeneRkPO4UB-jFpUYj6XpSxkhn7YQ8emp1avg-0xbPTBwQz4vAOY4GMM1Glj09POKaB1WoDe5qVzXvp_XnqfV2arZ-zDgJd4Ysd7avsxZCvjC5x_QGKxRQ |
CitedBy_id | crossref_primary_10_1016_j_phymed_2019_152966 crossref_primary_10_1186_s12879_022_07694_8 crossref_primary_10_3390_metabo10120492 crossref_primary_10_1002_nbm_4941 crossref_primary_10_3389_fneur_2022_804838 crossref_primary_10_3389_fmolb_2023_1099654 crossref_primary_10_1038_s41598_020_64413_6 crossref_primary_10_1111_tbed_14365 crossref_primary_10_1038_s41598_021_91545_0 crossref_primary_10_1038_s41598_022_08201_4 crossref_primary_10_1515_cclm_2018_0380 crossref_primary_10_1016_j_micres_2024_128038 crossref_primary_10_1016_j_prrv_2020_05_003 crossref_primary_10_1371_journal_pone_0199618 crossref_primary_10_2217_bmm_2018_0050 crossref_primary_10_1016_j_jmii_2024_07_011 crossref_primary_10_1038_s41598_020_60669_0 crossref_primary_10_1038_s41598_020_75513_8 crossref_primary_10_1155_2023_8111355 crossref_primary_10_1016_j_meomic_2024_100033 crossref_primary_10_1371_journal_pone_0204029 crossref_primary_10_1016_j_bj_2021_07_006 crossref_primary_10_1038_s41598_020_78999_4 |
Cites_doi | 10.1007/s00216-006-0979-z 10.4103/0366-6999.149188 10.1042/cs0940321 10.1002/hep.24412 10.1093/clinchem/44.7.1529 10.1079/NRR200493 10.1097/BCR.0b013e31815f5984 10.1016/j.cmet.2007.08.003 10.1016/j.jchromb.2004.09.032 10.1111/hae.12778 10.1007/s00125-006-0201-z 10.1016/j.tube.2015.02.038 10.1016/j.jadohealth.2009.03.010 10.1021/pr3000317 10.1289/ehp.112-a410 10.1007/s10545-010-9088-4 10.1021/pr4007359 10.1093/nar/gkv380 10.4155/bio.12.61 10.1128/JCM.01568-15 10.1016/j.febslet.2008.06.040 10.1016/S2214-109X(14)70245-1 10.1021/pr200173q 10.1371/journal.pone.0143820 10.1371/journal.pone.0122929 10.1093/nar/gkq329 10.1039/b209155k 10.1021/pr060594q 10.1371/journal.pone.0040221 10.1093/nar/27.1.29 10.1021/pr4010579 10.1186/1471-2334-14-53 10.1038/ng.507 10.1021/pr2010082 10.1007/s10334-006-0054-y 10.1371/journal.pone.0056850 10.1021/cb200348m |
ContentType | Journal Article |
Copyright | Copyright © 2016 American Chemical Society |
Copyright_xml | – notice: Copyright © 2016 American Chemical Society |
DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
DOI | 10.1021/acs.jproteome.6b00228 |
DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
DatabaseTitleList | MEDLINE MEDLINE - Academic |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Chemistry |
EISSN | 1535-3907 |
EndPage | 3125 |
ExternalDocumentID | 27451809 10_1021_acs_jproteome_6b00228 b054145667 |
Genre | Research Support, Non-U.S. Gov't Journal Article |
GroupedDBID | - 53G 55A 5GY 7~N AABXI ABMVS ABUCX ACGFS ACS AEESW AENEX AFEFF ALMA_UNASSIGNED_HOLDINGS AQSVZ CS3 DU5 EBS ED ED~ EJD F5P GNL IH9 IHE JG JG~ P2P RNS ROL UI2 VF5 VG9 W1F ZA5 --- 4.4 5VS AAHBH AAYXX ABBLG ABJNI ABLBI ABQRX ADHLV AHGAQ BAANH CITATION CUPRZ GGK CGR CUY CVF ECM EIF NPM 7X8 |
ID | FETCH-LOGICAL-a351t-803192a4e6c03d224004005ce4935da3775b2d03b95fb0ef083bedcc0210e1f13 |
IEDL.DBID | ACS |
ISSN | 1535-3893 |
IngestDate | Fri Jul 11 09:12:18 EDT 2025 Thu Jan 02 23:09:06 EST 2025 Tue Jul 01 01:36:35 EDT 2025 Thu Apr 24 23:04:16 EDT 2025 Thu Aug 27 13:43:13 EDT 2020 |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 9 |
Keywords | diagnosis NMR plasma metabolomics children tuberculosis |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-a351t-803192a4e6c03d224004005ce4935da3775b2d03b95fb0ef083bedcc0210e1f13 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
PMID | 27451809 |
PQID | 1816637323 |
PQPubID | 23479 |
PageCount | 8 |
ParticipantIDs | proquest_miscellaneous_1816637323 pubmed_primary_27451809 crossref_citationtrail_10_1021_acs_jproteome_6b00228 crossref_primary_10_1021_acs_jproteome_6b00228 acs_journals_10_1021_acs_jproteome_6b00228 |
ProviderPackageCode | JG~ 55A AABXI GNL VF5 7~N VG9 W1F ACS AEESW AFEFF ABMVS ABUCX IH9 AQSVZ ED~ UI2 CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2016-09-02 |
PublicationDateYYYYMMDD | 2016-09-02 |
PublicationDate_xml | – month: 09 year: 2016 text: 2016-09-02 day: 02 |
PublicationDecade | 2010 |
PublicationPlace | United States |
PublicationPlace_xml | – name: United States |
PublicationTitle | Journal of proteome research |
PublicationTitleAlternate | J. Proteome Res |
PublicationYear | 2016 |
Publisher | American Chemical Society |
Publisher_xml | – name: American Chemical Society |
References | ref9/cit9 ref6/cit6 ref36/cit36 ref3/cit3 ref27/cit27 ref18/cit18 ref11/cit11 ref25/cit25 ref16/cit16 ref29/cit29 ref32/cit32 ref23/cit23 ref14/cit14 Messana I. (ref8/cit8) 1998; 44 ref5/cit5 ref31/cit31 ref2/cit2 ref34/cit34 ref37/cit37 ref28/cit28 ref20/cit20 ref17/cit17 ref10/cit10 ref26/cit26 ref35/cit35 ref19/cit19 ref21/cit21 ref12/cit12 ref15/cit15 ref22/cit22 ref13/cit13 ref33/cit33 ref4/cit4 ref30/cit30 ref1/cit1 ref24/cit24 ref7/cit7 |
References_xml | – ident: ref6/cit6 doi: 10.1007/s00216-006-0979-z – ident: ref19/cit19 doi: 10.4103/0366-6999.149188 – ident: ref12/cit12 doi: 10.1042/cs0940321 – ident: ref32/cit32 doi: 10.1002/hep.24412 – volume: 44 start-page: 1529 issue: 7 year: 1998 ident: ref8/cit8 publication-title: Clin.Chem. doi: 10.1093/clinchem/44.7.1529 – ident: ref35/cit35 doi: 10.1079/NRR200493 – ident: ref29/cit29 doi: 10.1097/BCR.0b013e31815f5984 – ident: ref33/cit33 doi: 10.1016/j.cmet.2007.08.003 – ident: ref4/cit4 doi: 10.1016/j.jchromb.2004.09.032 – ident: ref24/cit24 doi: 10.1111/hae.12778 – ident: ref34/cit34 doi: 10.1007/s00125-006-0201-z – ident: ref28/cit28 doi: 10.1016/j.tube.2015.02.038 – ident: ref30/cit30 doi: 10.1016/j.jadohealth.2009.03.010 – ident: ref10/cit10 doi: 10.1021/pr3000317 – ident: ref2/cit2 doi: 10.1289/ehp.112-a410 – ident: ref36/cit36 doi: 10.1007/s10545-010-9088-4 – ident: ref17/cit17 doi: 10.1021/pr4007359 – ident: ref22/cit22 doi: 10.1093/nar/gkv380 – ident: ref13/cit13 doi: 10.4155/bio.12.61 – ident: ref31/cit31 doi: 10.1128/JCM.01568-15 – ident: ref21/cit21 doi: 10.1016/j.febslet.2008.06.040 – ident: ref1/cit1 doi: 10.1016/S2214-109X(14)70245-1 – ident: ref5/cit5 doi: 10.1021/pr200173q – ident: ref20/cit20 doi: 10.1371/journal.pone.0143820 – ident: ref25/cit25 doi: 10.1371/journal.pone.0122929 – ident: ref23/cit23 doi: 10.1093/nar/gkq329 – ident: ref9/cit9 doi: 10.1039/b209155k – ident: ref27/cit27 doi: 10.1021/pr060594q – ident: ref18/cit18 doi: 10.1371/journal.pone.0040221 – ident: ref26/cit26 doi: 10.1093/nar/27.1.29 – ident: ref15/cit15 doi: 10.1021/pr4010579 – ident: ref14/cit14 doi: 10.1186/1471-2334-14-53 – ident: ref3/cit3 doi: 10.1038/ng.507 – ident: ref11/cit11 doi: 10.1021/pr2010082 – ident: ref7/cit7 doi: 10.1007/s10334-006-0054-y – ident: ref37/cit37 doi: 10.1371/journal.pone.0056850 – ident: ref16/cit16 doi: 10.1021/cb200348m |
SSID | ssj0015703 |
Score | 2.3027823 |
Snippet | Although tuberculosis (TB) has been the greatest killer due to a single infectious disease, pediatric TB is still hard to diagnose because of the lack of... |
SourceID | proquest pubmed crossref acs |
SourceType | Aggregation Database Index Database Enrichment Source Publisher |
StartPage | 3118 |
SubjectTerms | Betaine - analysis Biomarkers - blood Case-Control Studies Child Discriminant Analysis Humans Magnetic Resonance Spectroscopy Metabolome Metabolomics - methods Plasma - metabolism Pyruvic Acid - analysis Sensitivity and Specificity Tuberculosis - diagnosis Valine - analysis |
Title | Utility of Novel Plasma Metabolic Markers in the Diagnosis of Pediatric Tuberculosis: A Classification and Regression Tree Analysis Approach |
URI | http://dx.doi.org/10.1021/acs.jproteome.6b00228 https://www.ncbi.nlm.nih.gov/pubmed/27451809 https://www.proquest.com/docview/1816637323 |
Volume | 15 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Li9RAEG7W9aAX34_xRQmehGSTfmRmvA2jyyLssugM7C30o3oZnU1kJhH0N_ij7eokAyLDKuSUUE26u5r6mvrqK8beYOGFcFmWeKLQSIUm0Vr6AOSUsVN0plBUKHx6Vpws5ccLdXHAjvZk8Hl-pO02_RJFC-orTIsYdSY32E1eTMZ025rNP-_SBiQn1QmkqoQi8VCys28YCkl2-2dI2oMzY7w5vsvOh6qdjmbyNW0bk9qff4s4_utU7rE7PfaEWecs99kBVg_YrfnQ8u0h-7VsiCr7A2oPZ_V3XMN5ANdXGk6xCc6yXlmg2p6AGGFVQYCO8L5j6q22ZLJr-wGL1uDGtmv68g5mEFtvEikp-gHoysEnvOwouBUsNogwqKPArFc5f8SWxx8W85Okb9eQaKHyJsS6cJy5lljYTLhITg2PsiinQjktaPu5y4SZKm8y9AH8GXTW0q0Tc5-Lx-ywqit8yiAvrPBCWT7xQpqAKbwj2UbLpy5YyfGIvQ1rWfbHbVvGTDrPy_hyWOCyX-ARk8P2lrYXPqf-G-vrzNKd2bdO-eM6g9eD75Rh3yjxoius2_B7lJwVY8HFiD3pnGo3JB9LRRpqz_5nSs_Z7QDcish14y_YYbNp8WUAR415FQ_Eb2uGDkk |
linkProvider | American Chemical Society |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwzV3bbtQwELVKeSgv3C_L1UjwgpQlseNsF4mH1ZZqS7srBLtS30LsjNG22wRtElD5Br6DX-G3mHEuCKSq4qESUp4cjeV4xvGx5vgMY88gslKmvu9ZotCECrSXJKFFIKe0GUKqI0UXhaezaLII3x6qww32o70Lg4MosKfCJfF_qwsEL6ntyGkX5CfQj9zms92QKffh9Cse1YrXezvo1-dC7L6ZjydeU03AS6QKSvwVY7SJJITI-DJ13El8lIFwKFWaSBqdSH2ph8pqHyxiEw2pMXQogsAGEvu9xC4jABJ0yBuNP3TZClKxqnVZlUcAoL0pdNawaSc0xZ874Rnw1m1zu9fYz26CHLvluF-Vum--_aUd-f_P4HV2tUHafFQvjRtsA7KbbGvcFri7xb4vSiIGn_Lc8ln-BVb8HR4lThI-hRKXxmppON1kQnzMlxlHoMx3al7isiCTrsgJn1ca1qZa0ZtXfMRdoVGiYLmo50mW8vfwqSYcZ3y-BuCtFgwfNZrut9niQibjDtvM8gzuMR5ERlqpjNi2MtSIoGxKIpVGDFO0Cgc99gJ9Fzc_lyJ2vAERxK6xdWjcOLTHwjaqYtPIvFO1kdV5Zv3O7HOtc3KewdM2ZGP0G6WZkgzyCodHqWg5kEL22N06lrsuxSBUpBh3_18-6QnbmsynB_HB3mz_AbuCkDVyLD_xkG2W6woeISws9WO3Jjn7eNEh_Atrwm7J |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3bbtQwELVKkYAX7pflaiR4QcqSxHG2W4mH1S6rltJVBbtS30Jsj9HCNqk2Cah8Q7-EX-GnmHEuEkhVxUMfkPLkaCzHM46PNcdnGHsBsRXC-L5niUITSVBemkYWgZxUeghGxZIuCu_P4p1F9O5QHm6wn-1dGBxEgT0VLolPq_rY2EZhIHhN7V-cfkF-BP3YbUBbDaFyD06-43GteLM7Qd--DMPp2_l4x2sqCnipkEGJv2OMuDCNINa-MI4_iY_UEA2FNKmgEYbGF2oorfLBIj5RYLSmgxEENhDY7yV2mVKFdNAbjT92GQtSsqq1WaVHIKC9LXTWsGk31MWfu-EZENdtddMb7Fc3SY7h8rVflaqvf_ylH_l_zOJNdr1B3HxUL5FbbAOy2-zquC10d4edLkoiCJ_w3PJZ_g1W_ACPFEcp34cSl8hqqTndaEKczJcZR8DMJzU_cVmQSVfshM8rBWtdrejNNh9xV3CUqFgu-nmaGf4BPtfE44zP1wC81YTho0bb_S5bXMhk3GObWZ7BA8aDWAsrpA63rIgUIilrSKxSh0ODVtGgx16h75LmJ1Mkjj8QBolrbB2aNA7tsaiNrEQ3cu9UdWR1nlm_Mzuu9U7OM3jehm2CfqN0U5pBXuHwKCUtBiIUPXa_jueuy3AQSVKOe_gvn_SMXTmYTJP3u7O9R-waItfYkf3Cx2yzXFfwBNFhqZ66ZcnZp4uO4N9i-XFM |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Utility+of+Novel+Plasma+Metabolic+Markers+in+the+Diagnosis+of+Pediatric+Tuberculosis%3A+A+Classification+and+Regression+Tree+Analysis+Approach&rft.jtitle=Journal+of+proteome+research&rft.au=Sun%2C+Lin&rft.au=Li%2C+Jie-Qiong&rft.au=Ren%2C+Na&rft.au=Qi%2C+Hui&rft.date=2016-09-02&rft.eissn=1535-3907&rft.volume=15&rft.issue=9&rft.spage=3118&rft_id=info:doi/10.1021%2Facs.jproteome.6b00228&rft_id=info%3Apmid%2F27451809&rft.externalDocID=27451809 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1535-3893&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1535-3893&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1535-3893&client=summon |