{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T07:13:31Z","timestamp":1780989211634,"version":"3.54.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,6,2]],"date-time":"2024-06-02T00:00:00Z","timestamp":1717286400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,2]],"date-time":"2024-06-02T00:00:00Z","timestamp":1717286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["1P01HL110869"],"award-info":[{"award-number":["1P01HL110869"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R00HL128787"],"award-info":[{"award-number":["R00HL128787"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Cedars-Sinai Leon Fine award for Translational Research"},{"name":"NIH NIGMS","award":["R35 GM142502"],"award-info":[{"award-number":["R35 GM142502"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clin Proteom"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using fivefold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Of the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p\u2009&lt;\u20090.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6\u20131 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4\u20134 (IGHV4-4). All proteins from the top 10 most important groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>We conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12014-024-09487-4","type":"journal-article","created":{"date-parts":[[2024,6,2]],"date-time":"2024-06-02T17:01:44Z","timestamp":1717347704000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures"],"prefix":"10.1186","volume":"21","author":[{"given":"Amanda","family":"Momenzadeh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simion","family":"Kreimer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongchuan","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Ayres","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Berman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuang-Yuh","family":"Chyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prediman K.","family":"Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dianna","family":"Milewicz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Azizzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jesse G.","family":"Meyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Parker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,2]]},"reference":[{"issue":"6","key":"9487_CR1","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1161\/01.CIR.0000154569.08857.7A","volume":"111","author":"EM Isselbacher","year":"2005","unstructured":"Isselbacher EM. Thoracic and abdominal aortic aneurysms. Circulation. 2005;111(6):816\u201328. https:\/\/doi.org\/10.1161\/01.CIR.0000154569.08857.7A.","journal-title":"Circulation"},{"issue":"7","key":"9487_CR2","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1001\/jama.283.7.897","volume":"283","author":"PG Hagan","year":"2000","unstructured":"Hagan PG, Nienaber CA, Isselbacher EM, Bruckman D, Karavite DJ, Russman PL, Evangelista A, Fattori R, Suzuki T, Oh JK, Moore AG, Malouf JF, Pape LA, Gaca C, Sechtem U, Lenferink S, Deutsch HJ, Diedrichs H, Marcos Robles J, Llovet A, Gilon D, Das SK, Armstrong WF, Deeb GM, Eagle KA. The International Registry of Acute Aortic Dissection (IRAD): new insights into an old disease. JAMA. 2000;283(7):897\u2013903. https:\/\/doi.org\/10.1001\/jama.283.7.897.","journal-title":"JAMA"},{"issue":"6","key":"9487_CR3","first-page":"1103","volume":"92","author":"LK Bickerstaff","year":"1982","unstructured":"Bickerstaff LK, Pairolero PC, Hollier LH, Melton LJ, Van Peenen HJ, Cherry KJ, Joyce JW, Lie JT. Thoracic aortic aneurysms: a population-based study. Surgery. 1982;92(6):1103\u20138.","journal-title":"Surgery"},{"issue":"24","key":"9487_CR4","doi-asserted-by":"publisher","first-page":"2611","DOI":"10.1161\/CIRCULATIONAHA.106.630400","volume":"114","author":"C Olsson","year":"2006","unstructured":"Olsson C, Thelin S, St\u00e5hle E, Ekbom A, Granath F. Thoracic aortic aneurysm and dissection: increasing prevalence and improved outcomes reported in a nationwide population-based study of more than 14,000 cases from 1987 to 2002. Circulation. 2006;114(24):2611\u20138. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.106.630400.","journal-title":"Circulation"},{"issue":"21","key":"9487_CR5","doi-asserted-by":"publisher","first-page":"2226","DOI":"10.1161\/CIRCULATIONAHA.106.622340","volume":"114","author":"TT Tsai","year":"2006","unstructured":"Tsai TT, Fattori R, Trimarchi S, Isselbacher E, Myrmel T, Evangelista A, Hutchison S, Sechtem U, Cooper JV, Smith DE, Pape L, Froehlich J, Raghupathy A, Januzzi JL, Eagle KA, Nienaber CA, International Registry of Acute Aortic Dissection. Long-term survival in patients presenting with type B acute aortic dissection: insights from the international registry of acute aortic dissection. Circulation. 2006;114(21):2226\u201331. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.106.622340.","journal-title":"Circulation"},{"issue":"1","key":"9487_CR6","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/s11748-017-0855-0","volume":"67","author":"D Balmforth","year":"2019","unstructured":"Balmforth D, Harky A, Adams B, Yap J, Shipolini A, Roberts N, Uppal R, Bashir M. Is there a role for biomarkers in thoracic aortic aneurysm disease? Gen Thorac Cardiovasc Surg. 2019;67(1):12\u20139. https:\/\/doi.org\/10.1007\/s11748-017-0855-0.","journal-title":"Gen Thorac Cardiovasc Surg"},{"issue":"3\u20134","key":"9487_CR7","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1053\/j.semvascsurg.2015.01.001","volume":"27","author":"S Shalhub","year":"2014","unstructured":"Shalhub S, Dua A, Brooks J. Biomarkers in descending thoracic aortic dissection. Semin Vasc Surg. 2014;27(3\u20134):196\u20139. https:\/\/doi.org\/10.1053\/j.semvascsurg.2015.01.001.","journal-title":"Semin Vasc Surg"},{"key":"9487_CR8","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.jvssci.2022.01.001","volume":"3","author":"L Saddic","year":"2022","unstructured":"Saddic L, Orosco A, Guo D, Milewicz DM, Troxlair D, Heide RV, Herrington D, Wang Y, Azizzadeh A, Parker SJ. Proteomic analysis of descending thoracic aorta identifies unique and universal signatures of aneurysm and dissection. JVS-Vasc Sci. 2022;3:85\u2013181. https:\/\/doi.org\/10.1016\/j.jvssci.2022.01.001.","journal-title":"JVS-Vasc Sci"},{"issue":"12","key":"9487_CR9","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1089\/omi.2013.0017","volume":"17","author":"AL Swan","year":"2013","unstructured":"Swan AL, Mobasheri A, Allaway D, Liddell S, Bacardit J. Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology. OMICS J Integr Biol. 2013;17(12):595\u2013610. https:\/\/doi.org\/10.1089\/omi.2013.0017.","journal-title":"OMICS J Integr Biol"},{"issue":"4","key":"9487_CR10","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1038\/nmeth.4642","volume":"15","author":"D Bzdok","year":"2018","unstructured":"Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15(4):233\u20134. https:\/\/doi.org\/10.1038\/nmeth.4642.","journal-title":"Nat Methods"},{"key":"9487_CR11","doi-asserted-by":"publisher","first-page":"100083","DOI":"10.1016\/j.mcpro.2021.100083","volume":"20","author":"Z Shi","year":"2021","unstructured":"Shi Z, Wen B, Gao Q, Zhang B. Feature selection methods for protein biomarker discovery from proteomics or multiomics data. Mol Cell Proteomics. 2021;20:100083. https:\/\/doi.org\/10.1016\/j.mcpro.2021.100083.","journal-title":"Mol Cell Proteomics"},{"issue":"1","key":"9487_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.cels.2020.05.012","volume":"11","author":"CB Messner","year":"2020","unstructured":"Messner CB, Demichev V, Wendisch D, Michalick L, White M, Freiwald A, Textoris-Taube K, Vernardis SI, Egger A-S, Kreidl M, Ludwig D, Kilian C, Agostini F, Zelezniak A, Thibeault C, Pfeiffer M, Hippenstiel S, Hocke A, von Kalle C, Campbell A, Hayward C, Porteous DJ, Marioni RE, Langenberg C, Lilley KS, Kuebler WM, M\u00fclleder M, Drosten C, Suttorp N, Witzenrath M, Kurth F, Sander LE, Ralser M. Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection. Cell Syst. 2020;11(1):11-24.e4. https:\/\/doi.org\/10.1016\/j.cels.2020.05.012.","journal-title":"Cell Syst"},{"issue":"1","key":"9487_CR13","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.cell.2020.05.032","volume":"182","author":"B Shen","year":"2020","unstructured":"Shen B, Yi X, Sun Y, Bi X, Du J, Zhang C, Quan S, Zhang F, Sun R, Qian L, Ge W, Liu W, Liang S, Chen H, Zhang Y, Li J, Xu J, He Z, Chen B, Wang J, Yan H, Zheng Y, Wang D, Zhu J, Kong Z, Kang Z, Liang X, Ding X, Ruan G, Xiang N, Cai X, Gao H, Li L, Li S, Xiao Q, Lu T, Zhu Y, Liu H, Chen H, Guo T. Proteomic and metabolomic characterization of COVID-19 patient sera. Cell. 2020;182(1):59-72.e15. https:\/\/doi.org\/10.1016\/j.cell.2020.05.032.","journal-title":"Cell"},{"issue":"5","key":"9487_CR14","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1016\/j.immuni.2020.10.008","volume":"53","author":"T Shu","year":"2020","unstructured":"Shu T, Ning W, Wu D, Xu J, Han Q, Huang M, Zou X, Yang Q, Yuan Y, Bie Y, Pan S, Mu J, Han Y, Yang X, Zhou H, Li R, Ren Y, Chen X, Yao S, Qiu Y, Zhang D-Y, Xue Y, Shang Y, Zhou X. Plasma proteomics identify biomarkers and pathogenesis of COVID-19. Immunity. 2020;53(5):1108-1122.e5. https:\/\/doi.org\/10.1016\/j.immuni.2020.10.008.","journal-title":"Immunity"},{"issue":"1","key":"9487_CR15","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.cels.2020.10.003","volume":"12","author":"KA Overmyer","year":"2021","unstructured":"Overmyer KA, Shishkova E, Miller IJ, Balnis J, Bernstein MN, Peters-Clarke TM, Meyer JG, Quan Q, Muehlbauer LK, Trujillo EA, He Y, Chopra A, Chieng HC, Tiwari A, Judson MA, Paulson B, Brademan DR, Zhu Y, Serrano LR, Linke V, Drake LA, Adam AP, Schwartz BS, Singer HA, Swanson S, Mosher DF, Stewart R, Coon JJ, Jaitovich A. Large-scale multi-omic analysis of COVID-19 severity. Cell Syst. 2021;12(1):23-40.e7. https:\/\/doi.org\/10.1016\/j.cels.2020.10.003.","journal-title":"Cell Syst"},{"issue":"6","key":"9487_CR16","doi-asserted-by":"publisher","first-page":"2002591","DOI":"10.1183\/13993003.02591-2020","volume":"57","author":"Y Bauer","year":"2021","unstructured":"Bauer Y, de Bernard S, Hickey P, Ballard K, Cruz J, Cornelisse P, Chadha-Boreham H, Distler O, Rosenberg D, Doelberg M, Roux S, Nayler O, Lawrie A. identifying early pulmonary arterial hypertension biomarkers in systemic sclerosis: machine learning on proteomics from the DETECT cohort. Eur Respir J. 2021;57(6):2002591. https:\/\/doi.org\/10.1183\/13993003.02591-2020.","journal-title":"Eur Respir J"},{"key":"9487_CR17","doi-asserted-by":"publisher","first-page":"100077","DOI":"10.1016\/j.mcpro.2021.100077","volume":"20","author":"F Yu","year":"2021","unstructured":"Yu F, Haynes SE, Nesvizhskii AI. IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs. Mol Cell Proteomics MCP. 2021;20:100077. https:\/\/doi.org\/10.1016\/j.mcpro.2021.100077.","journal-title":"Mol Cell Proteomics MCP"},{"issue":"3","key":"9487_CR18","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1038\/nmeth.3255","volume":"12","author":"C-C Tsou","year":"2015","unstructured":"Tsou C-C, Avtonomov D, Larsen B, Tucholska M, Choi H, Gingras A-C, Nesvizhskii AI. DIA-umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods. 2015;12(3):258\u201364. https:\/\/doi.org\/10.1038\/nmeth.3255.","journal-title":"Nat Methods"},{"key":"9487_CR19","doi-asserted-by":"publisher","DOI":"10.1101\/2020.03.17.995431","author":"A Behdenna","year":"2020","unstructured":"Behdenna A, Haziza J, Azencott C-A, Nordor A. pyComBat, a python tool for batch effects correction in high-throughput molecular data using empirical bayes methods. bioRxiv. 2020. https:\/\/doi.org\/10.1101\/2020.03.17.995431.","journal-title":"bioRxiv"},{"key":"9487_CR20","unstructured":"Scikit-learn: Machine Learning in Python, Pedregosa, et al. JMLR 12 (2011), p. 2825\u201330."},{"issue":"3","key":"9487_CR21","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","volume":"17","author":"P Virtanen","year":"2020","unstructured":"Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, Polat \u0130, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P. SciPy 1.0: fundamental algorithms for scientific computing in python. Nat Methods. 2020;17(3):261\u201372. https:\/\/doi.org\/10.1038\/s41592-019-0686-2.","journal-title":"Nat. Methods"},{"issue":"60","key":"9487_CR22","doi-asserted-by":"publisher","first-page":"3021","DOI":"10.21105\/joss.03021","volume":"6","author":"ML Waskom","year":"2021","unstructured":"Waskom ML. Seaborn: statistical data visualization. J Open Source Softw. 2021;6(60):3021. https:\/\/doi.org\/10.21105\/joss.03021.","journal-title":"J Open Source Softw"},{"issue":"3","key":"9487_CR23","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter JD. Matplotlib: a 2D graphics environment. Comput Sci Eng. 2007;9(3):90\u20135. https:\/\/doi.org\/10.1109\/MCSE.2007.55.","journal-title":"Comput Sci Eng"},{"key":"9487_CR24","unstructured":"Plotly: Low-Code Data App Development. https:\/\/plotly.com\/. Accessed 03 Aug 2022."},{"key":"9487_CR25","doi-asserted-by":"crossref","unstructured":"Seabold S, Perktold J. Statsmodels: econometric and statistical modeling with python. In: Proceedings of the 9th Python in science conference. 2010","DOI":"10.25080\/Majora-92bf1922-011"},{"issue":"9","key":"9487_CR26","doi-asserted-by":"publisher","first-page":"2071","DOI":"10.1021\/acs.jproteome.2c00117","volume":"21","author":"H Desaire","year":"2022","unstructured":"Desaire H. How (Not) to generate a highly predictive biomarker panel using machine learning. J Proteome Res. 2022;21(9):2071\u20134. https:\/\/doi.org\/10.1021\/acs.jproteome.2c00117.","journal-title":"J Proteome Res"},{"key":"9487_CR27","unstructured":"sklearn.ensemble.GradientBoostingClassifier \u2014 scikit-learn 1.1.1 documentation. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.GradientBoostingClassifier.html. Accessed 03 Aug 2022."},{"key":"9487_CR28","unstructured":"sklearn.svm.SVC. scikit-learn. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.svm.SVC.html. Accessed 03 Aug 2022."},{"key":"9487_CR29","unstructured":"sklearn.ensemble.RandomForestClassifier. scikit-learn. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClassifier.html. Accessed 03 Aug 2022."},{"key":"9487_CR30","unstructured":"sklearn.ensemble.ExtraTreesClassifier. scikit-learn. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.ExtraTreesClassifier.html. Accessed 03 Aug 2022."},{"key":"9487_CR31","unstructured":"sklearn.linear_model.LogisticRegression. scikit-learn. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.LogisticRegression.html. Accessed 03 Aug 2022."},{"key":"9487_CR32","unstructured":"sklearn.neighbors.KNeighborsClassifier. scikit-learn. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.neighbors.KNeighborsClassifier.htmlAccessed 03 Aug 2022."},{"issue":"8","key":"9487_CR33","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1093\/bioinformatics\/btp101","volume":"25","author":"G Bindea","year":"2009","unstructured":"Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman W-H, Pag\u00e8s F, Trajanoski Z, Galon J. ClueGO: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25(8):1091\u20133. https:\/\/doi.org\/10.1093\/bioinformatics\/btp101.","journal-title":"Bioinformatics"},{"issue":"11","key":"9487_CR34","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1101\/gr.1239303","volume":"13","author":"P Shannon","year":"2003","unstructured":"Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498\u2013504. https:\/\/doi.org\/10.1101\/gr.1239303.","journal-title":"Genome Res"},{"issue":"5","key":"9487_CR35","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1097\/MOL.0000000000000208","volume":"26","author":"NU Mehta","year":"2015","unstructured":"Mehta NU, Reddy ST. Role of hemoglobin\/heme scavenger protein hemopexin in atherosclerosis and inflammatory diseases. Curr Opin Lipidol. 2015;26(5):384\u20137. https:\/\/doi.org\/10.1097\/MOL.0000000000000208.","journal-title":"Curr Opin Lipidol"},{"issue":"6","key":"9487_CR36","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1161\/ATVBAHA.115.306991","volume":"36","author":"NU Mehta","year":"2016","unstructured":"Mehta NU, Grijalva V, Hama S, Wagner A, Navab M, Fogelman AM, Reddy ST. Apolipoprotein E \u2212\/\u2212 mice lacking hemopexin develop increased atherosclerosis via mechanisms that include oxidative stress and altered macrophage function. Arterioscler Thromb Vasc Biol. 2016;36(6):1152\u201363. https:\/\/doi.org\/10.1161\/ATVBAHA.115.306991.","journal-title":"Arterioscler Thromb Vasc Biol"},{"issue":"6","key":"9487_CR37","doi-asserted-by":"publisher","first-page":"e007750","DOI":"10.1161\/JAHA.117.007750","volume":"7","author":"A Furusho","year":"2018","unstructured":"Furusho A, Aoki H, Ohno-Urabe S, Nishihara M, Hirakata S, Nishida N, Ito S, Hayashi M, Imaizumi T, Hiromatsu S, Akashi H, Tanaka H, Fukumoto Y. Involvement of B cells, immunoglobulins, and Syk in the pathogenesis of abdominal aortic aneurysm. J Am Heart Assoc. 2018;7(6):e007750. https:\/\/doi.org\/10.1161\/JAHA.117.007750.","journal-title":"J Am Heart Assoc"},{"issue":"11","key":"9487_CR38","doi-asserted-by":"publisher","first-page":"e112407","DOI":"10.1371\/journal.pone.0112407","volume":"9","author":"FL Macrae","year":"2014","unstructured":"Macrae FL, Evans HL, Bridge KI, Johnson A, Scott DJA, Ari\u00ebns RAS. Common FXIII and fibrinogen polymorphisms in abdominal aortic aneurysms. PLoS ONE. 2014;9(11):e112407. https:\/\/doi.org\/10.1371\/journal.pone.0112407.","journal-title":"PLoS ONE"},{"issue":"1","key":"9487_CR39","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1186\/s12872-019-1146-8","volume":"19","author":"S Arati","year":"2019","unstructured":"Arati S, Chetan GK, Sibin MK, Bhat DI, Vazhayil V, Narasingarao KVL. Prognostic significance of factor XIIIA promoter methylation status in aneurysmal subarachnoid haemorrhage (aSAH). BMC Cardiovasc Disord. 2019;19(1):170. https:\/\/doi.org\/10.1186\/s12872-019-1146-8.","journal-title":"BMC Cardiovasc Disord"},{"issue":"1","key":"9487_CR40","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1186\/s12872-019-1267-0","volume":"19","author":"Y He","year":"2019","unstructured":"He Y, Ma C, Xing J, Wang S, Ji C, Han Y, Zhang J. Serum amyloid a protein as a potential biomarker in predicting acute onset and association with in-hospital death in acute aortic dissection. BMC Cardiovasc Disord. 2019;19(1):282. https:\/\/doi.org\/10.1186\/s12872-019-1267-0.","journal-title":"BMC Cardiovasc Disord"}],"container-title":["Clinical Proteomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12014-024-09487-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12014-024-09487-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12014-024-09487-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,2]],"date-time":"2024-06-02T17:01:56Z","timestamp":1717347716000},"score":1,"resource":{"primary":{"URL":"https:\/\/clinicalproteomicsjournal.biomedcentral.com\/articles\/10.1186\/s12014-024-09487-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,2]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["9487"],"URL":"https:\/\/doi.org\/10.1186\/s12014-024-09487-4","relation":{},"ISSN":["1542-6416","1559-0275"],"issn-type":[{"value":"1542-6416","type":"print"},{"value":"1559-0275","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,2]]},"assertion":[{"value":"16 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All patients included in this study provided informed consent and their recruitment and participation was approved by the institutional review boards of UT Houston.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"38"}}