ALDH2 knockout protects against aortic dissection

Background

The incidence and mortality of aortic dissection (AD) are increasing. In pathological studies, macrophages, T lymphocytes and dendritic cells were found in the tunica media of the aorta. Acetaldehyde dehydrogenase 2 (ALDH₂) gene polymorphisms are associated with a high incidence of hypertension in Asian populations. However, there is no clear evidence of the relationship between ALDH₂ and aortic dissection in Asians. The aim of this study was to investigate the incidence of aortic dissection in different ALDH₂ genotypes and explore changes in the vasculature.

Materials and methods

Three-week-old male mice were administered freshly prepared β-aminopropionitrile solution dissolved in drinking water (1 g/kg/d) for 28 days to induce TAD. An animal ultrasound imaging system was used to observe the formation of arterial dissection and changes in cardiac function. Subsequently, mice were euthanized by cervical dislocation. The aortas were fixed for HE staining and EVG staining to observe aortic elastic fiber tears and pseudoluma formation under a microscope.

Results

Knockout of ALDH₂ mitigated β-aminopropionitrile-induced TAD formation in animal studies. Ultrasound results showed that ALDH₂ knockout reduced the degree of ascending aortic widening and the incidence of aortic dissection rupture. Pathological sections of multiple aortic segments showed that the protective effect of ALDH₂ knockout was observed in not only the ascending aorta but also the aortic arch and descending aorta. The expression levels of genes related to NK CD56bright cells, Th17 cells, T cells and T helper cells were decreased in ALDH₂ knockout mice treated with β-aminopropionitrile for 28 days.

Conclusion

ALDH₂ knockout protects against aortic dissection by altering the inflammatory response and immune response and protecting elastic fibers.

Dissected aorta segmentation using convolutional neural networks

BACKGROUND AND OBJECTIVE

Aortic dissection is a severe cardiovascular pathology in which an injury of the intimal layer of the aorta allows blood flowing into the aortic wall, forcing the wall layers apart. Such situation presents a high mortality rate and requires an in-depth understanding of the 3-D morphology of the dissected aorta to plan the right treatment. An accurate automatic segmentation algorithm is therefore needed.

METHOD

In this paper, we propose a deep-learning-based algorithm to segment dissected aorta on computed tomography angiography (CTA) images. The algorithm consists of two steps. Firstly, a 3-D convolutional neural network (CNN) is applied to divide the 3-D volume into two anatomical portions. Secondly, two 2-D CNNs based on pyramid scene parsing network (PSPnet) segment each specific portion separately. An edge extraction branch was added to the 2-D model to get higher segmentation accuracy on intimal flap area.

RESULTS

The experiments conducted and the comparisons made show that the proposed solution performs well with an average dice index over 92%. The combination of 3-D and 2-D models improves the aorta segmentation accuracy compared to 3-D only models and the segmentation robustness compared to 2-D only models. The edge extraction branch improves the DICE index near aorta boundaries from 73.41% to 81.39%.

CONCLUSIONS

The proposed algorithm has satisfying performance for capturing the aorta structure while avoiding false positives on the intimal flaps.

Prospective study of a non-restrictive decision rule for acute aortic syndrome

OBJECTIVES

To determine the impact of a non-restrictive clinical decision rule on CT utilization for Emergency Department patients suspected of having an acute aortic syndrome (AAS).

METHODS

We prospectively assessed the performance of a previously described, collaboratively designed, non-restrictive clinical decision rule for AAS. Emergency Department patients with suspected AAS were stratified into low and high-risk groups based on decision rule results, from July 2013-August 2014. Patients with acute trauma, prior AAS or aortic surgery were excluded. CT dose reduction protocols were concurrently implemented as a quality improvement measure. Bivariate analysis was performed to compare the prospective cohort with the historical derivation cohort for CT utilization rates, results of CT, AAS incidence and radiation exposure. The performance of the clinical decision rule was evaluated.

RESULTS

Compared with the historic cohort, the study cohort demonstrated a lower CT utilization rate [0.344% (427/124,093) versus 0.477% (1465/306,961), (p<0.001)], a trend toward higher CT diagnostic yield [4.4% (19/427) versus 2.7% (40/1465), (p=0.08)]. AAS incidence was similar [0.015% (19/124,093) versus 0.013% (40/306,961), (p=0.57)]. The mean effective radiation dose was markedly lower [12±5.5mSv versus 43±20mSv, (p<0.0001)]. The clinical decision rule correctly stratified only 56% (10/18) of patients with AAS as high-risk.

CONCLUSIONS

A non-restrictive, collaboratively designed, clinical decision rule for Emergency Department patients with suspected AAS performed poorly in risk-stratifying patients for AAS. However, its implementation was associated with a significant and safe decrease in CT utilization.