Thrombosis as an intravascular effector of innate immunity.

Nat Rev Immunol

Institut für Laboratoriumsmedizin, Klinikum der Universität, Ludwig-Maximilians-Universität, Marchioninistrasse 15, 81377 Munich, Germany.

Published: January 2013

Thrombosis is the most frequent cause of mortality worldwide and is closely linked to haemostasis, which is the biological mechanism that stops bleeding after the injury of blood vessels. Indeed, both processes share the core pathways of blood coagulation and platelet activation. Here, we summarize recent work suggesting that thrombosis under certain circumstances has a major physiological role in immune defence, and we introduce the term immunothrombosis to describe this process. Immunothrombosis designates an innate immune response induced by the formation of thrombi inside blood vessels, in particular in microvessels. Immunothrombosis is supported by immune cells and by specific thrombosis-related molecules and generates an intravascular scaffold that facilitates the recognition, containment and destruction of pathogens, thereby protecting host integrity without inducing major collateral damage to the host. However, if uncontrolled, immunothrombosis is a major biological process fostering the pathologies associated with thrombosis.

Download full-text PDF

Source
http://dx.doi.org/10.1038/nri3345DOI Listing

Publication Analysis

Top Keywords

blood vessels
8
thrombosis
4
thrombosis intravascular
4
intravascular effector
4
effector innate
4
innate immunity
4
immunity thrombosis
4
thrombosis frequent
4
frequent mortality
4
mortality worldwide
4

Similar Publications

In-stent restenosis (ISR) following interventional therapy is a fatal clinical complication. Current evidence indicates that neointimal hyperplasia driven by uncontrolled proliferation of vascular smooth muscle cells (VSMC) is a major cause of restenosis. This implies that inhibiting VSMC proliferation may be an attractive approach for preventing in-stent restenosis.

View Article and Find Full Text PDF

Klebsiella pneumoniae-derived extracellular vesicles impair endothelial function by inhibiting SIRT1.

Cell Commun Signal

January 2025

Beijing An Zhen Hospital, Capital Medical University, The Key Laboratory of Remodeling Cardiovascular Diseases, Ministry of Education; Collaborative Innovation Center for Cardiovascular Disorders, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing, 100029, China.

Background: The potential role of Klebsiella pneumoniae (K.pn) in hypertension development has been emphasized, although the specific mechanisms have not been well understood. Bacterial extracellular vesicles (BEVs) released by Gram-negative bacteria modulate host cell functions by delivering bacterial components to host cells.

View Article and Find Full Text PDF

Successful engraftment of skin grafts highly depends on the quality of the wound bed. Good quality of blood vessels near the surface is critical to support the viability of the graft. Ischemic, irradiated scar tissue, bone and tendons will not have the sufficient blood supply.

View Article and Find Full Text PDF

Background: Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by reader experience and prone to interobserver variability. This study evaluated a novel algorithm for coronary stenosis quantification (atherosclerosis imaging quantitative CT, AI-QCT).

Methods: The study included 208 patients with suspected coronary artery disease (CAD) undergoing CCTA in Perfusion Imaging and CT Coronary Angiography With Invasive Coronary Angiography-1.

View Article and Find Full Text PDF

Purpose: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending artery (LAD), right coronary artery (RCA) or left circumflex artery (LCX) in iodine contrast-enhanced ECG-gated coronary CT angiography (CCTA) scans.

Methods: From a database of 6293 CCTA scans, we used pre-existing curved multiplanar reformations (CMR) images of the LAD, RCA and LCX arteries to create end-to-end deep-learning models for the detection of moderate or severe stenoses. We preprocessed the images by exploiting domain knowledge and employed a transfer learning approach using EfficientNet, ResNet, DenseNet and Inception-ResNet, with a class-weighted strategy optimised through cross-validation.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!