Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling
수학적 모델링을 통한 COVID-19 심각도 및 후유증의 면역학적 및 임상적 예측인자 식별
Article
[키워드] absolute neutrophil count
Analysis
Aritficial Intelligence
biochemical
biochemical markers
Biochemical parameters
Blood urea nitrogen
C-reactive protein
clinical
Cohort
control subject
control subjects
coronavirus
coronavirus disease
COVID-19
COVID-19 patient
COVID-19 patients
COVID-19 severity
Critical
curated
cytokine
Cytokines
dataset
differential expression
disease severities
Efficacy
exploratory
ferritin
granzyme B
highlight
identify
identifying
IFN-γ
IL-1
IL-10
IL-15
IL-1ra
IL-6
immunological
Increased
Initially
interferon
Interferon-gamma
interleukin
investigated
Lactate
lactate dehydrogenase
Liver injury
machine
machine learning
methodology
modeling
multiplex
Multiplex assay
Nasopharyngeal swab
nasopharyngeal swab sample
Nasopharyngeal swab samples
Necrosis
neutrophil
nitrogen
outcome
pandemic
pathophysiology
pathway
Patient
PD-L1
potential therapeutic targets
predict
Predictive
predictive models
Predictive value
predictor
radiological
receptor
RNA
RNA seq
ROC
ROC analysis
serum
severe cases
severity
the disease
transcriptomics
tumor necrosis
urea
validation cohort
variant
[DOI] 10.3389/fimmu.2022.865845 PMC 바로가기 [Article Type] Article
[DOI] 10.3389/fimmu.2022.865845 PMC 바로가기 [Article Type] Article