Transcatheter embolization for duodenal ulcer bleeding originating from cystic artery erosion.

BACKGROUND: Ulcer erosion into the cystic artery is a rare cause of bleeding in duodenal ulcers, with only a limited number of cases described in the literature. Historically, treatment has predominantly involved surgical intervention. We present three cases of duodenal ulcer bleeding due to cystic artery erosion, which were successfully managed with cystic artery embolization. CASE PRESENTATION: This case series includes three male patients with duodenal ulcer bleeding, aged 90, 81, and 82 years, respectively, and no prior history of biliary system disorders. The ulcer locations were identified as two in the post-bulbar region and one in the anterior bulb. After the failure of medical and endoscopic treatment, transcatheter arterial embolization was adopted. Initial angiography did not reveal any contrast medium extravasation. Empirical embolization of the gastroduodenal artery using gelatin sponge particles and coils failed to achieve hemostasis. Super-selective cystic artery angiography confirmed the source of bleeding as the cystic artery. One patient was embolized with gelatin sponge particles and coils, while the other two patients were embolized with N-butyl-cyanoacrylate. All patients achieved successful hemostasis without gallbladder infraction. CONCLUSIONS: Cystic artery embolization proved to be a minimally invasive technique for achieving hemostasis in these cases, indicating that it may be a safe and effective alternative to surgery for this uncommon cause of upper gastrointestinal bleeding. Validation through further studies is warranted.

Millimeter-Wave Radar-Based Identity Recognition Algorithm Built on Multimodal Fusion.

Millimeter-wave radar-based identification technology has a wide range of applications in persistent identity verification, covering areas such as security production, healthcare, and personalized smart consumption systems. It has received extensive attention from the academic community due to its advantages of being non-invasive, environmentally insensitive and privacy-preserving. Existing identification algorithms mainly rely on a single signal, such as breathing or heartbeat. The reliability and accuracy of these algorithms are limited due to the high similarity of breathing patterns and the low signal-to-noise ratio of heartbeat signals. To address the above issues, this paper proposes an algorithm for multimodal fusion for identity recognition. This algorithm extracts and fuses features derived from phase signals, respiratory signals, and heartbeat signals for identity recognition purposes. The spatial features of signals with different modes are first extracted by the residual network (ResNet), after which these features are fused with a spatial-channel attention fusion module. On this basis, the temporal features are further extracted with a time series-based self-attention mechanism. Finally, the feature vectors of the user's vital sign modality are obtained to perform identity recognition. This method makes full use of the correlation and complementarity between different modal signals to improve the accuracy and reliability of identification. Simulation experiments show that the algorithm identity recognition proposed in this paper achieves an accuracy of 94.26% on a 20-subject self-test dataset, which is much higher than that of the traditional algorithm, which is about 85%.

Identification of Clinical Heterogeneity and Construction of Prediction Models for Novel Subtypes in Patients with Abdominal Aortic Aneurysm: An Unsupervised Machine Learning Study.

BACKGROUND: Abdominal aortic aneurysm (AAA) is one of the most common diseases in vascular surgery. Endovascular aneurysm repair (EVAR) can effectively treat AAA. It is essential to accurately classify patients with AAA who need EVAR. METHODS: We enrolled 266 patients with AAA who underwent EVAR. Unsupervised machine learning algorithms (UMLAs) were used to cluster subjects according to similar clinical characteristics. To verify UMLA's accuracy, the operative and postoperative results of the 2 clusters were analyzed. Finally, a prediction model was developed using binary logistic regression analysis. RESULTS: UMLAs could correctly classify patients based on their clinical characteristics. Patients in Cluster 1 were older, had a higher BMI, and were more likely than patients in Cluster 2 to develop pneumonia, chronic obstructive pulmonary disease, and cerebrovascular disease. The aneurysm diameter, neck angulation, diameter and angulation of bilateral common iliac arteries, and incidence of iliac artery aneurysm were significantly higher in cluster 1 patients than in cluster 2. Cluster 1 had a longer operative time, a longer length of stay in the intensive care unit and hospital, a higher medical expense, and a higher incidence of reintervention. A nomogram was established based on the BMI, neck angulation, left common iliac artery (LCIA) diameter and angulation, and right common iliac artery (RCIA) diameter and angulation. The nomogram was evaluated using receiver operating characteristic curve analysis, with an area under the curve of 0.933 (95% confidence interval, 0.902-0.963) and a C-index of 0.927. CONCLUSIONS: Our findings demonstrate that UMLAs can be used to rationally classify a heterogeneous cohort of patients with AAA effectively, and the analysis of postoperative variables also verified the accuracy of UMLAs. We established a prediction model for new subtypes of AAA, which can improve the quality of management of patients with AAA.