Dear Colleagues,

Aortic disease may be diagnosed after a long period of subclinical development or they may have an acute presentation. Aortic diseases contribute to the wide spectrum of arterial diseases: aortic aneurysms (AA), aortic dissection (AD), carotid atherosclerotic and inflammatory affections. Thoracic- and abdominal aortic aneurysms (TAA and AAA, respectively) are often incidental findings, genetic variants predispose individuals to these aortic diseases. About biomarkers, ranging from plasma to serum markers, that could lead to a better understanding of the development and progression of AAA, TAA, and AD associated with other detection of vascular injury. Damage of intima and weakness of the media were the key pathological changes, and smooth muscle cells (SMCs) death is an important inducer of thoracic aorta disease, abdominal aorta disease, and carotid diseases. Simultaneously, media regression is also responsible for aortic aneurysm and aortic dissection progression, including loss of SMCs and degradation of the ECM. Many regulated cell deaths (RCD, also called programmed cell death), such as apoptosis, ferroptosis, autophagic cell death, pyroptosis, and necroptosis, involved in the progression of AAA, TAA, and AD, and the targeting of RCD pathways shows promise as an approach for attenuating and interrupting the occurrence of aortic disease. Meanwhile, vascular aging is another process characterized by the progressive loss of physiological functions, leading to an increased challenges in aortic diseases. Recently, single-cell RNA sequencing (scRNA-seq) has been developed and widely used in vascular biological fields to reveal biological mechanisms at the cellular level and solve the problems of cellular heterogeneity. With more researchers focusing on single-cell atlas of aortic disease reveals immune-SMCs crosstalk and communication between them during AAA, TAA, and AD. For clinical practice, artificial intelligence (AI) applications for the diagnosis and risk evaluation of aortic disease have been an active research area in recent years. The machine learning or deep learning, as a product of this era, has achieved promising success in many fields including genetic systems biology and aortic disease diagnosis.

We would like to investigate novel biomolecular mechanism (e.g. RCD, and vascular aging) in the progression of aortic disease, which would provide a new perspective for drug research and development. We also would like to shed some light on the diagnosis of AAA, TAA, and AD where existing diagnostic consistently fails to impact its progression. We would recommend the discovery of pathogenic mechanism form the standpoint of aortic disease spectrum which ranges from bulk RNA-seq to scRNA-seq. Every step of your research will advance treatment of aortic disease. This Special Issue will also cover recent developments in machine learning methods for detecting aortic diseases disease using imaging and clinical data.

We welcome Original Research, Review, and Mini-review covering, but not limited to, the following topics:

1. Machine learning or deep learning for the diagnosis and risk management of aortic diseases;
2. New models for handling complex genetic/genomic data sets integrating bulk and single-cell RNA-seq data in AAA, TAA, and AD;
3. New biomolecular mechanism which regulates RCD and aging of aortic disease;
4. Discovery of circulating biomarkers indicating the progression of AAA and AD;
5. The interaction of different types of endothelial cells (ECs), smooth muscle cells (SMCs), and immune cells, and communication between them in the process of AAA and AD;
6. 6. Discovery of candidate biomarkers, such as metabolomics and proteomics, to gain a lot of attention in AAA, TAA, and AD.

Dr. Jian Zhang
Guest Editor

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