Monocyte Subsets, Stanford-A Acute Aortic Dissection, and Carotid Artery Stenosis: New Evidences.
Noemi CifaniMaria ProiettaMaurizio TaurinoLuigi TritapepeFlavia Del PortoPublished in: Journal of immunology research (2019)
Monocytes are a heterogeneous cell population distinguished into three subsets with distinctive phenotypic and functional properties: "classical" (CD14++CD16-), "intermediate" (CD14++CD16+), and "nonclassical" (CD14+CD16++). Monocyte subsets play a pivotal role in many inflammatory systemic diseases including atherosclerosis (ATS). Only a low number of studies evaluated monocyte behavior in patients affected by cardiovascular diseases, and data about their role in acute aortic dissection (AAD) are lacking. Thus, the aim of this study was to investigate CD14++CD16-, CD14++CD16+, and CD14+CD16++ cells in patients with Stanford-A AAD and in patients with carotid artery stenosis (CAS). Methods. 20 patients with carotid artery stenosis (CAS group), 17 patients with Stanford-A AAD (AAD group), and 17 subjects with traditional cardiovascular risk factors (RF group) were enrolled. Monocyte subset frequency was determined by flow cytometry. Results. Classical monocytes were significantly increased in the AAD group versus CAS and RF groups, whereas intermediate monocytes were significantly decreased in the AAD group versus CAS and RF groups. Conclusions. Results of this study identify in AAD patients a peculiar monocyte array that can partly explain depletion of T CD4+ lymphocyte subpopulations observed in patients affected by AAD.
Keyphrases
- aortic dissection
- peripheral blood
- dendritic cells
- end stage renal disease
- cardiovascular disease
- ejection fraction
- crispr cas
- newly diagnosed
- cardiovascular risk factors
- chronic kidney disease
- endothelial cells
- prognostic factors
- genome editing
- peritoneal dialysis
- type diabetes
- flow cytometry
- oxidative stress
- machine learning
- induced apoptosis
- single cell
- signaling pathway
- hepatitis b virus
- bone marrow
- drug induced
- big data
- cell death
- cell cycle arrest
- artificial intelligence
- pi k akt