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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1016/j.clim.2022.109179
|2 doi
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|a pubmed24n1162.xml
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|a (PII)S1521-6616(22)00260-1
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Chen, Zhen
|e verfasserin
|4 aut
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|a Comprehensive characterization of costimulatory molecule gene for diagnosis, prognosis and recognition of immune microenvironment features in sepsis
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 25.11.2022
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|a Date Revised 26.12.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.
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|a The present study, which involved 10 GEO datasets and 3 ArrayExpress datasets, comprehensively characterized the potential effects of CMGs in sepsis. Based on machine learning algorithms (Lasso, SVM and ANN), the CMG classifier was constructed by integrating 6 hub CMGs (CD28, CD40, LTB, TMIGD2, TNFRSF13C and TNFSF4). The CMG classifier exhibit excellent diagnostic values across multiple datasets and time points, and was able to distinguish sepsis from other critical diseases. The CMG classifier performed better in predicting mortality than other clinical characteristics or endotypes. More importantly, from clinical specimens, the CMG classifier showed more superior diagnostic values than PCT and CRP. Alternatively, the CMG classifier/hub CMGs is significantly correlated with immune cells infiltration (B cells, T cells, Tregs, and MDSC), pivotal immune and molecular pathways (inflammation-promoting, complement and coagulation cascades), and several cytokines. Collectively, CMG classifier was a robust tool for diagnosis, prognosis and recognition of immune microenvironment features in sepsis
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Costimulatory molecule
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|a Immune microenvironment
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|a Machine learning approach
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|a Model
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|a Multi-transcriptome
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|a sepsis
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|a CD40 Antigens
|2 NLM
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|a CD28 Antigens
|2 NLM
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|a TNFSF4 protein, human
|2 NLM
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|a OX40 Ligand
|2 NLM
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1 |
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|a Dong, Xinhuai
|e verfasserin
|4 aut
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1 |
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|a Liu, Genglong
|e verfasserin
|4 aut
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1 |
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|a Ou, Yangpeng
|e verfasserin
|4 aut
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1 |
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|a Lu, Chuangang
|e verfasserin
|4 aut
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1 |
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|a Yang, Ben
|e verfasserin
|4 aut
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1 |
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|a Zhu, Xuelian
|e verfasserin
|4 aut
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700 |
1 |
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|a Zuo, Liuer
|e verfasserin
|4 aut
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773 |
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|i Enthalten in
|t Clinical immunology (Orlando, Fla.)
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|g 245(2022) vom: 10. Dez., Seite 109179
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|g volume:245
|g year:2022
|g day:10
|g month:12
|g pages:109179
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|u http://dx.doi.org/10.1016/j.clim.2022.109179
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