Novel plasma protein biomarkers from critically ill sepsis patients

Rhodes A, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Crit Care Med. 2017;45:486–552.

Article  Google Scholar 

Singer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315:801–10.

Article  Google Scholar 

Blanco J, et al. Incidence, organ dysfunction and mortality in severe sepsis: a Spanish multicentre study. Crit Care. 2008;12:R158.

Article  Google Scholar 

Hotchkiss RS, et al. Apoptotic cell death in patients with sepsis, shock, and multiple organ dysfunction. Crit Care Med. 1999;27:1230–51.

Article  Google Scholar 

Wiersinga WJ, Leopold SJ, Cranendonk DR, van der Poll T. Host innate immune responses to sepsis. Virulence. 2014;5:36–44.

Article  Google Scholar 

Singer M, De Santis V, Vitale D, Jeffcoate W. Multiorgan failure is an adaptive, endocrine-mediated, metabolic response to overwhelming systemic inflammation. Lancet. 2004;364:545–8.

Article  Google Scholar 

Hotchkiss RS, Monneret G, Payen D. Sepsis-induced immunosuppression: from cellular dysfunctions to immunotherapy. Nat Rev Immunol. 2013;13:862–74.

Article  Google Scholar 

Deutschman CS, Tracey KJ. Sepsis: current dogma and new perspectives. Immunity. 2014;40:463–75.

Article  Google Scholar 

Angus DC, van der Poll T. Severe sepsis and septic shock. N Engl J Med. 2013;369:840–51.

Article  Google Scholar 

Fraser DD, et al. Novel Outcome Biomarkers Identified With Targeted Proteomic Analyses of Plasma From Critically Ill Coronavirus Disease 2019 Patients. Crit Care Explor. 2020;2: e0189.

Article  Google Scholar 

Cao Z, Robinson RA. (2014) The role of proteomics in understanding biological mechanisms of sepsis. PROTEOMICS–Clinical Applications 8: 35–52.

Shen Z, et al. Sepsis plasma protein profiling with immunodepletion, three-dimensional liquid chromatography tandem mass spectrometry, and spectrum counting. J Proteome Res. 2006;5:3154–60.

Article  Google Scholar 

Kalenka A, et al. Changes in the serum proteome of patients with sepsis and septic shock. Anesth Analg. 2006;103:1522–6.

Article  Google Scholar 

Paiva RAd, David CM, Domont GB. Proteomics in sepsis: a pilot study. Revista Brasileira de terapia intensiva. 2010;22:403–12.

Article  Google Scholar 

Triantafilou M, et al. Serum proteins modulate lipopolysaccharide and lipoteichoic acid-induced activation and contribute to the clinical outcome of sepsis. Virulence. 2012;3:136–45.

Article  Google Scholar 

Soares AJ, et al. Differential proteomics of the plasma of individuals with sepsis caused by Acinetobacter baumannii. J Proteomics. 2009;73:267–78.

Article  Google Scholar 

Cao Z, Yende S, Kellum JA, Angus DC, Robinson RA. Proteomics reveals age-related differences in the host immune response to sepsis. J Proteome Res. 2014;13:422–32.

Article  Google Scholar 

Lundberg M, Eriksson A, Tran B, Assarsson E, Fredriksson S. Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood. Nucleic Acids Res. 2011;39: e102.

Article  Google Scholar 

Assarsson E, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS ONE. 2014;9: e95192.

Article  Google Scholar 

Gillio-Meina C, Cepinskas G, Cecchini EL, Fraser DD. Translational research in pediatrics II: blood collection, processing, shipping, and storage. Pediatrics. 2013;131:754–66.

Article  Google Scholar 

Brisson AR, Matsui D, Rieder MJ, Fraser DD. Translational research in pediatrics: tissue sampling and biobanking. Pediatrics. 2012;129:153–62.

Article  Google Scholar 

Fraser DD, et al. Inflammation profiling of critically ill coronavirus disease 2019 patients. Crit Care Explor. 2020. https://doi.org/10.1097/CCE.0000000000000144.

Article  Google Scholar 

Van der Maaten L, Hinton G: Visualizing data using t-SNE. J Mach Learn Res. 2008; 9:2579–605.

Tang C, Garreau D, von Luxburg U: When do random forests fail? Proceedings of the 32nd International Conference on Neural Information Processing Systems. December 2018: 2987–997.

Pedregosa F, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–30.

Google Scholar 

Kursa MB, Rudnicki WR. Feature selection with the Boruta package. J Stat Softw. 2010;36:1–13.

Article  Google Scholar 

Goulden R, et al. qSOFA, SIRS and NEWS for predicting inhospital mortality and ICU admission in emergency admissions treated as sepsis. Emerg Med J. 2018;35:345–9.

Article  Google Scholar 

Ginde AA, et al. Age-related differences in biomarkers of acute inflammation during hospitalization for sepsis. Shock (Augusta, Ga). 2014;42:99.

Article  Google Scholar 

Scumpia PO, Moldawer LL. Biology of interleukin-10 and its regulatory roles in sepsis syndromes. Crit Care Med. 2005;33:S468–71.

Article  Google Scholar 

Oberholzer A, Oberholzer C, Moldawer LL. Interleukin-10: a complex role in the pathogenesis of sepsis syndromes and its potential as an anti-inflammatory drug. Crit Care Med. 2002;30:S58–63.

Article  Google Scholar 

Madtes DK, et al. Elevated transforming growth factor-α levels in bronchoalveolar lavage fluid of patients with acute respiratory distress syndrome. Am J Respir Crit Care Med. 1998;158:424–30.

Article  Google Scholar 

Deneault E, et al. CNTN5-/+ or EHMT2-/+ human iPSC-derived neurons from individuals with autism develop hyperactive neuronal networks. Elife. 2019. https://doi.org/10.7554/eLife.40092.

Article  Google Scholar 

Dauar MT, et al. CNTN5 is associated with disease risk and pathology throughout the Alzheimer’s disease continuum. Alzheimers Dement. 2021;17: e052359.

Google Scholar 

Vogt L, et al. VSIG4, a B7 family–related protein, is a negative regulator of T cell activation. J Clin Investig. 2006;116:2817–26.

Article  Google Scholar 

Zanotti S, Kumar A, Kumar A. Cytokine modulation in sepsis and septic shock. Expert Opin Investig Drugs. 2002;11:1061–75.

Article  Google Scholar 

Montoya-Ruiz C, et al. Variants in LTA, TNF, IL1B and IL10 genes associated with the clinical course of sepsis. Immunol Res. 2016;64:1168–78.

Article  Google Scholar 

Gyawali B, Ramakrishna K, Dhamoon AS. Sepsis: The evolution in definition, pathophysiology, and management. SAGE Open Med. 2019;7:2050312119835043.

Article  Google Scholar 

Abraham E, Singer M. Mechanisms of sepsis-induced organ dysfunction. Crit Care Med. 2007;35:2408–16.

Article  Google Scholar 

Wong DT, Gomez M, McGuire GP, Kavanagh B. Utilization of intensive care unit days in a Canadian medical-surgical intensive care unit. Crit Care Med. 1999;27:1319–24.

Article  Google Scholar 

Kemperman H, et al. Osteoprotegerin is higher in sepsis than in noninfectious SIRS and predicts 30-day mortality of SIRS patients in the intensive care. J Appl Lab Med. 2019;3:559–68.

Article  Google Scholar 

Baud’huin M, et al. Osteoprotegerin: multiple partners for multiple functions. Cytokine Growth Factor Rev. 2013;24:401–9.

Article  Google Scholar 

Schaalan M, Mohamed W. Predictive ability of circulating osteoprotegerin as a novel biomarker for early detection of acute kidney injury induced by sepsis. Eur Cytokine Netw. 2017;28:52–62.

Article  Google Scholar 

Steiner J, Guglin M. BNP or NTproBNP? A clinician’s perspective. Int J Cardiol. 2008;129:5–14.

Article  Google Scholar 

Hall C. NT-ProBNP: the mechanism behind the marker. J Card Fail. 2005;11:S81-83.

Article  Google Scholar 

Mueller C, Breidthardt T, Laule-Kilian K, Christ M, Perruchoud AP. The integration of BNP and NT-proBNP into clinical medicine. Swiss Med Wkly. 2007;137:4–12.

Google Scholar 

Zhao T, Su Z, Li Y, Zhang X, You Q. Chitinase-3 like-protein-1 function and its role in diseases. Signal Transduct Target Ther. 2020;5:1–20.

Google Scholar 

Kornblit B, et al. Plasma YKL-40 and CHI3L1 in systemic inflammation and sepsis—Experience from two prospective cohorts. Immunobiology. 2013;218:1227–34.

Article  Google Scholar 

Kronborg G, et al. Serum level of YKL-40 is elevated in patients with Streptococcus pneumoniae bacteremia and is associated with the outcome of the disease. Scand J Infect Dis. 2002;34:323–6.

Article  Google Scholar 

Varner JA, Cheresh DA. Integrins and cancer. Curr Opin Cell Biol. 1996;8:724–30.

Article  Google Scholar 

Khurana S, et al. Outside-in integrin signalling regulates haematopoietic stem cell function via Periostin-Itgav axis. Nat Commun. 2016;7:1–14.

Article  Google Scholar 

Morandi EM, et al. ITGAV and ITGA5 diversely regulate proliferation and adipogenic differentiation of human adipose derived stem cells. Sci Rep. 2016;6:28889.

Article  Google Scholar 

Yamaji Y, et al. TEM7 (PLXDC1) in neovascular endothelial cells of fibrovascular membranes from patients with proliferative diabetic retinopathy. Invest Ophthalmol Vis Sci. 2008;49:3151–7.

Article  Google Scholar 

Cheng G, et al. Identification of PLXDC1 and PLXDC2 as the transmembrane receptors for the multifunctional factor PEDF. Elife. 2014;3: e05401.

Article  Google Scholar 

Herridge MS. Prognostication and intensive care unit outcome: the evolving role of scoring systems. Clin Chest Med. 2003;24:751–62.

Article  Google Scholar 

Murphy SM, et al. GCP5 and GCP6: two new members of the human gamma-tubulin complex. Mol Biol Cell. 2001;12:3340–52.

Article  Google Scholar 

Cosgrove GP, et al. Pigment epithelium–derived factor in idiopathic pulmonary fibrosis: a role in aberrant angiogenesis. Am J Respir Crit Care Med. 2004;170:242–51.

Article  Google Scholar 

Shin ES, Sorenson CM, Sheibani N. PEDF expression regulates the proangiogenic and proinflammatory phenotype of the lung endothelium. Am J Physiol Lung Cell Mol Physiol. 2014;306:L620–34.

Article  Google Scholar 

Li L, et al. Pigment epithelial-derived factor (PEDF)-triggered lung cancer cell apoptosis relies on p53 protein-driven Fas ligand (Fas-L) up-regulation and Fas protein cell surface translocation. J Biol Chem. 2014;289:30785–99.

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