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References from Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning. Local targets link to admitted publications; unresolved targets remain external evidence.
The third international consensus definitions for sepsis and septic shock (Sepsis-3)
10.1001/jama.2016.0287 · 2016 · External reference
10.1016/s0140-6736(19)32989-7
10.1016/s0140-6736(19)32989-7 · External reference
10.4161/viru.27372
10.4161/viru.27372 · External reference
Sepsis: pathophysiology and clinical management
10.1136/bmj.i1585 · 2016 · External reference
10.1056/nejmra1208623
10.1056/nejmra1208623 · External reference
10.1186/cc8872
10.1186/cc8872 · External reference
Biomarkers for sepsis: more than just fever and leukocytosis-a narrative review
10.1186/s13054-021-03862-5 · 2022 · External reference
Clinical relevance of procalcitonin and C-reactive protein as infection markers in renal impairment: a cross-sectional study
10.1186/s13054-014-0640-8 · 2014 · External reference
Clinical value of procalcitonin for patients with suspected bloodstream infection
10.1309/ajcp4gv7zfdtangc · 2014 · External reference
C-reactive protein as an indicator of bacterial infection of adult patients in the emergency department
2002 · External reference
Biomarkers of sepsis
10.3109/10408363.2013.764490 · 2013 · External reference
Nanotools for sepsis diagnosis and treatment
10.1002/adhm.202001378 · 2021 · External reference
Machine learning in haematological malignancies
10.1016/s2352-3026(20)30121-6 · 2020 · External reference
10.1097/ccm.0000000000002936
10.1097/ccm.0000000000002936 · External reference
10.1007/s40121-022-00671-3
10.1007/s40121-022-00671-3 · External reference
Interpretable machine learning for early prediction of prognosis in sepsis: a discovery and validation study
10.1007/s40121-022-00628-6 · 2022 · External reference
Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost
10.1186/s12967-020-02620-5 · 2020 · External reference
Source-specific host response and outcomes in critically ill patients with sepsis: a prospective cohort study
10.1007/s00134-021-06574-0 · 2022 · External reference
The leukocyte non-coding RNA landscape in critically ill patients with sepsis
2020 · External reference
Predicting secretory proteins with signalP
10.1007/978-1-4939-7015-5_6 · 2017 · External reference
10.1086/421997
10.1086/421997 · External reference
The diagnostic accuracy of procalcitonin and C-reactive protein for sepsis: a systematic review and meta-analysis
10.1002/jcb.27870 · 2019 · External reference
Diagnostic value of procalcitonin and presepsin for sepsis in critically ill adult patients: a systematic review and meta-analysis
10.1186/s40560-019-0374-4 · 2019 · External reference
Identification of potential early diagnostic biomarkers of sepsis
10.2147/jir.s298604 · 2021 · External reference
Identification of potential biomarkers and immune features of sepsis using bioinformatics analysis
2020 · External reference
Gene co-expression analysis identified preserved and survival-related modules in severe blunt trauma, burns, sepsis, and systemic inflammatory response syndrome
10.2147/ijgm.s336785 · 2021 · External reference
Interleukin-1 receptor 2: a new biomarker for sepsis diagnosis and gram-negative/gram-positive bacterial differentiation
10.1097/shk.0000000000000714 · 2017 · External reference
10.1016/j.molcel.2009.09.016
10.1016/j.molcel.2009.09.016 · External reference
10.1016/j.bbamcr.2011.07.001
10.1016/j.bbamcr.2011.07.001 · External reference
10.15252/embj.201695148
10.15252/embj.201695148 · External reference
The VCP/p97 and YOD1 proteins have different substrate-dependent activities in endoplasmic reticulum-associated degradation (ERAD)
10.1074/jbc.m115.656660 · 2015 · External reference
Cell signalling: cell survival and a Gadd45-factor deficiency
10.1038/424741b · 2003 · External reference
10.1083/jcb.200602149
10.1083/jcb.200602149 · External reference
10.1158/0008-5472.can-07-0867
10.1158/0008-5472.can-07-0867 · External reference
10.1007/978-1-4614-8289-5_1
10.1007/978-1-4614-8289-5_1 · External reference
10.1016/s0092-8674(00)81619-0
10.1016/s0092-8674(00)81619-0 · External reference
10.1128/mcb.22.13.4544-4555.2002
10.1128/mcb.22.13.4544-4555.2002 · External reference
TIMP2 mediates endoplasmic reticulum stress contributing to sepsis-induced acute kidney injury
10.1096/fj.202101555rr · 2022 · External reference
GADD45A inhibits autophagy by regulating the interaction between BECN1 and PIK3C3
10.1080/15548627.2015.1112484 · 2015 · External reference
10.3389/fncel.2017.00089
10.3389/fncel.2017.00089 · External reference
10.1021/pr901096u
10.1021/pr901096u · External reference
CTIP2, a multifunctional protein: cellular physiopathology and therapeutic implications
2014 · External reference
Genomics of neonatal sepsis: has-miR-150 targeting BCL11B functions in disease progression
10.1186/s13052-018-0575-9 · 2018 · External reference
IL1R2 blockade suppresses breast tumorigenesis and progression by impairing USP15-dependent BMI1 stability
10.1002/advs.201901728 · 2020 · External reference
Microarray analysis identifies IL-1 receptor type 2 as a novel candidate biomarker in patients with acute respiratory distress syndrome
10.1186/s12931-015-0190-x · 2015 · External reference
Immunoregulatory protein profiles of necrotizing enterocolitis versus spontaneous intestinal perforation in preterm infants
10.1371/journal.pone.0036977 · 2012 · External reference
High circulating levels of the IL-1 type II decoy receptor in critically ill patients with sepsis: association of high decoy receptor levels with glucocorticoid administration
10.1189/jlb.72.4.643 · 2002 · External reference
