The objective of this study was to investigate the effect of transcatheter arterial embolization (TAE) with N-butyl-2-cyanoacrylate (NBCA)-Lipiodol mixture in patients with bronchial artery aneurysm (BAA). From January 2005 to January 2010, five patients presenting hemoptysis with six BAAs were treated with NBCA-Lipiodol mixture, including intra-aneurysm embolization (IAE) in one patient. Adjuvant embolization with spherical polyvinyl alcohol (PVA) embolic microparticles or NBCA was first performed to embolize the distal engorged bronchiectatic arteries. Bronchial arterial angiography showed six BAAs (four in the right lobe and two in the left lobe) and some engorged, tortuous bronchial arteries. TAE through microcatheter was successful in all cases. Postembolization angiogram demonstrated the NBCA cast and total occlusion of BAAs and bronchiectatic engorged vessels. After these procedures, hemoptysis completely disappeared in all patients. Follow-up computed tomography (CT) scan was performed at an average of 3 months (range 2 to 6), which showed no enhancement of BAAs and accumulation of NBCA. TAE is a minimally invasive, effective, and reliable approach for treatment for patients with BAA. NBCA-Lipiodol mixture provides a good choice for treatment of BAA, especially when catheterization of the efferent branches is impossible.

Objective: Stenting is the preferred treatment for iliac vein lesions. For the treatment of occlusions in the junction of the iliac vein and the inferior vena cava (IVC), the stent needs to be positioned in the IVC to cover the lesion. However, the pathological changes in the contralateral iliac vein due to stent coverage on its ostium remain unclear. We observed the patency of the contralateral iliac vein via animal experiments. Methods: The stents were placed in the left iliac vein and extended into the IVC in 8 beagle dogs. Doppler ultrasonography, angiography, and histopathological examination were used to assess the patency and histopathological changes in the contralateral iliac vein. Results: Angiography showed patency of the contralateral iliac vein and no sign of thrombosis or stenosis. Twelve months after stenting, Doppler ultrasonography showed a stenotic change in the ostium of the contralateral iliac vein. The histopathological examination showed that the stent strut at the ostium of the contralateral iliac vein was mostly covered by the intima, and the cross-sectional stenosis rate was greater than 60%. Conclusions: The coverage of the iliac vein stent on the ostium of the contralateral iliac vein does not cause complete occlusion of the contralateral vein but can cause significant stenosis at the ostium of the contralateral iliac vein, which is considered to be a potential risk factor for thrombosis.

In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four layers stack structure is designed to automatically extract the deep features of the segmented dynamic video image shot, and six categories of normal, atrial premature, ventricular premature, right bundle branch block, left bundle branch block, and pacing are achieved through hierarchical training and optimization. Accurate recognition of heartbeats with an average accuracy of 99.5%. It provides technical assistance for the intelligent prediction of high-risk cardiovascular diseases like ventricular fibrillation. An intelligent prediction algorithm for sudden cardiac death based on the echolocation network was proposed. By designing an echolocation network with a multilayer serial structure, an intelligent distinction between sudden cardiac death signal and non-sudden death signal was realized, and the signal was predicted 5 min before sudden death occurred, with an average prediction accuracy of 94.32%. Using the self-learning capability of stack sparse auto-coding network, a large amount of label-free data is designed to train the stack sparse auto-coding deep neural network to automatically extract deep representations of plaque features. A small amount of labeled data then introduced to micro-train the entire network. Through the automatic analysis of the fiber cap thickness in the plaques, the automatic identification of thin fiber cap-like vulnerable plaques was achieved, and the average overlap of vulnerable regions reached 87%. The overall time for the automatic plaque and vulnerable plaque recognition algorithm was 0.54 s. It provides theoretical support for accurate diagnosis and endogenous analysis of high-risk cardiovascular diseases.