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Vulnerable carotid atherosclerotic plaque (CAP) significantly contributes to ischemic stroke. Neovascularization within plaques is an emerging biomarker linked to plaque vulnerability that can be detected using contrast-enhanced ultrasound (CEUS). Computed tomography angiography (CTA) is a common method used in clinical cerebrovascular assessments that can be employed to evaluate the vulnerability of CAPs. Radiomics is a technique that automatically extracts radiomic features from images. This study aimed to identify radiomic features associated with the neovascularization of CAP and construct a prediction model for CAP vulnerability based on radiomic features. CTA data and clinical data of patients with CAPs who underwent CTA and CEUS between January 2018 and December 2021 in Beijing Hospital were retrospectively collected. The data were divided into a training cohort and a testing cohort using a 7:3 split. According to the examination of CEUS, CAPs were dichotomized into vulnerable and stable groups. 3D Slicer software was used to delineate the region of interest in CTA images, and the Pyradiomics package was used to extract radiomic features in Python. Machine learning algorithms containing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perception (MLP) were used to construct the models. The confusion matrix, receiver operating characteristic (ROC) curve, accuracy, precision, recall, and f-1 score were used to evaluate the performance of the models. A total of 74 patients with 110 CAPs were included. In all, 1,316 radiomic features were extracted, and 10 radiomic features were selected for machine-learning model construction. After evaluating severalmodels on the testing cohorts, it was discovered thatmodel_RF outperformed the others, achieving an AUC value of 0.93 (95% CI: 0.88-0.99). The accuracy, precision, recall, and f-1 score of model_RF in the testing cohort were 0.85, 0.87, 0.85, and 0.85, respectively. Radiomic features associated with the neovascularization of CAP were obtained. Our study highlights the potential of radiomics-based models for improving the accuracy and efficiency of diagnosing vulnerable CAP. In particular, the model_RF, utilizing radiomic features extracted from CTA, provides a noninvasive and efficient method for accurately predicting the vulnerability status of CAP. This model shows great potential for offering clinical guidance for early detection and improving patient outcomes. [ABSTRACT FROM AUTHOR]

Background and aims: Secondary embolization (SE) during mechanical thrombectomy (MT) for cerebral large vessel occlusion (LVO) could reduce the anterior blood flow and worsen clinical outcomes. The current SE prediction tools have limited accuracy. In this study, we aimed to develop a nomogram to predict SE following MT for LVO based on clinical features and radiomics extracted from computed tomography (CT) images. Materials and methods: A total of 61 patients with LVO stroke treated by MT at Beijing Hospital were included in this retrospective study, of whom 27 developed SE during the MT procedure. The patients were randomly divided (7:3) into training (n = 42) and testing (n = 19) cohorts. The thrombus radiomics features were extracted from the pre-interventional thin-slice CT images, and the conventional clinical and radiological indicators associated with SE were recorded. A support vector machine (SVM) learning model with 5-fold cross-verification was used to obtain the radiomics and clinical signatures. For both signatures, a prediction nomogram for SE was constructed. The signatures were then combined using the logistic regression analysis to construct a combined clinical radiomics nomogram. Results: In the training cohort, the area under the receiver operating characteristic curve (AUC) of the nomograms was 0.963 for the combined model, 0.911 for the radiomics, and 0.891 for the clinical model. Following validation, the AUCs were 0.762 for the combined model, 0.714 for the radiomics model, and 0.637 for the clinical model. The combined clinical and radiomics nomogram had the best prediction accuracy in both the training and test cohort. Conclusion: This nomogram could be used to optimize the surgical MT procedure for LVO based on the risk of developing SE. [ABSTRACT FROM AUTHOR]

With the development of the census and monitoring of national geographical conditions in China, the availability of information has sharply increased. Progress in data mining methods and social application tools has provided a way for solving the problems of low resource allocation and high uncertainty in decision-making regarding planning. To relieve non-capital functions and serve the healthy development of the Beijing Metropolitan Area, we propose a new model of self-adaptive cellular automaton based on ensemble learning (EL-CA). The method is based on the data collected by monitoring geographical conditions and is guided by complex geocomputing that simulates city-scale evolution in Beijing. A comparison of predicted and real data for Beijing in 2015 demonstrated that the predictions made by the EL-CA model proposed significantly outperformed those by traditional cellular automaton (CA) models based on empirical statistics. Data on the geographical conditions in Beijing in 2007 and 2015 were employed in model simulation and training to predict the scale of the city in 2023. The urban agglomeration points in Beijing tended to be dense, the overall construction land tended to be saturated, and the growth rate of land use areas slowed. Results from the model also established that the construction land in Beijing is close to saturation from a quantitative perspective, and the potential urban expansion hotspots in the future are mainly concentrated in the Tongzhou District, the Daxing District, the Fangshan District, the south side of the fourth and fifth ring roads, and the southwest side of Pinggu District. These results can provide decision-makers in urban planning with supporting data and support Beijing to relieve Beijing of functions nonessential to its role as China's capital. [ABSTRACT FROM AUTHOR]

Self-employed migrants who have worked in Beijing for two decades have settled in an intersecting area between rural and urban areas. Based on extensive field research and in-depth interviews, this article examines the workers' attempts to define their urban living space and resist discrimination. The research shows that veteran migrants re-unite with their children and wives in an outlying, "sub-urban" area of the city, where cheap accommodations, public education, and an informal economy are present. The area is where migrants make claims on citizenship through asserting their parental responsibility and identity. [ABSTRACT FROM AUTHOR]