Polyamines are known to increase in plant cells in response to a variety of stress conditions. However, the physiological roles of elevated polyamines are not understood well. Here we investigated the effects of polyamines on ion channel activities by applying patch-clamp techniques to protoplasts derived from barley (Hordeum vulgare) seedling root cells. Extracellular application of polyamines significantly blocked the inward Na 1 and K 1 currents (especially Na 1 currents) in root epidermal and cortical cells. These blocking effects of polyamines were increased with increasing polycation charge. In root xylem parenchyma, the inward K 1 currents were blocked by extracellular spermidine, while the outward K 1 currents were enhanced. At the whole-plant level, the root K 1 content, as well as the root and shoot Na 1 levels, was decreased significantly by exogenous spermidine. Together, by restricting Na 1 influx into roots and by preventing K 1 loss from shoots, polyamines were shown to improve K

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The rupture of aneurysms is the main cause of spontaneous subarachnoid hemorrhage (SAH), which is a serious life-threatening disease with high mortality and permanent disability rates. Therefore, it is highly desirable to evaluate the rupture risk of aneurysms. In this study, we proposed a novel semiautomatic prediction model for the rupture risk estimation of aneurysms based on the CADA dataset, including 108 datasets with 125 annotated aneurysms. The model consisted of multidimensional feature fusion, feature selection, and the construction of classification methods. For the multidimensional feature fusion, we extracted four kinds of features and combined them into the feature set, including morphological features, radiomics features, clinical features, and deep learning features. Specifically, we applied the feature extractor 3D EfficientNet-B0 to extract and analyze the classification capabilities of three different deep learning features, namely, no-sigmoid features, sigmoid features, and binarization features. In the experiment, we constructed five distinct classification models, among which the k-nearest neighbor classifier showed the best performance for aneurysm rupture risk estimation, reaching an F2-score of 0.789. Our results suggest that the full use of multidimensional feature fusion can improve the performance of aneurysm rupture risk assessment. Compared with other methods, our method achieves the state-of-the-art performance for aneurysm rupture risk assessment methods based on CADA 2020.