The aim of the present study was to compare the diagnostic values of ultrasound micro-flow imaging (SMI) and contrast-enhanced ultrasound (CEUS) for neovascularization in carotid plaques, and to investigate their capacities for predicting the risks of cerebral stroke. A total of 39 patients (64 carotid plaques) with severe carotid artery stenosis undergoing carotid endarterectomy were selected between February 2015 and February 2016, and SMI and CEUS were used to detect neovascularization in plaques. According to the CEUS dynamic graph of plaques, the enhanced intensity visual scales and contrast parameters were obtained. Carotid atherosclerotic plaques were divided into 4 groups. The differences in the enhanced intensity visual scales, contrast parameters, and gray-scale median (GSM) values among the 4 groups were analyzed. Carotid plaque tissue samples from patients were stained for CD34, and the consistency of the methods for the diagnosis of neovascularization in plaques was analyzed. The differences in GSM values, enhanced intensities, and enhanced densities among the 4 groups of plaques were statistically significant (F=29.365, χ2=29.025, χ2=30.871, P<0.001); the differences in enhanced intensities of carotid atherosclerotic plaques with different echo types were statistically significant (χ2=17.951, P<0.001). The enhanced intensity of plaques was negatively correlated with the GSM value (r=−0.376, P<0.01), and the enhanced density of plaques was negatively correlated with the GSM value (r=−0.252, P<0.01). SMI and CEUS grading had good consistency (κ=0.860>0), there were statistically significant differences in new vessel densities with different SMI gradings (P<0.001), and the clinical symptoms and severity were positively correlated with SMI grading (rs=0.592>0). In conclusion, SMI and CEUS have good consistency for evaluating neovascularization in carotid plaques, and have good clinical value for evaluating neovascularization in carotid plaques.

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An extrapolation method is usually applied when Ab initio molecular dynamics (AIMD) is applied to studying ionic conductivity in solid-state electrolytes in lithium-ion batteries. As the ions move slowly in solid-state electrolytes, the first-principles method typically involves computationally intensive calculations, and it can take significant time to obtain accurate results. First-principles method is too expensive for the time scale required at room temperature. The classical molecular dynamics method is typically applied to systems containing thousands of atoms and can simulate the system’s behavior over nanoseconds. During the simulation, the positions and velocities of the atoms are updated at discrete time intervals, allowing the system’s behavior to be studied over time. However, its accuracy depends on the empirical force-field libraries. Limited by the computational resource, the previous studies applied the extrapolation method to obtain the room temperature ionic conductivity, which was not accurate because the linear relationship in the Arrhenius equation was not valid in a wide range of temperatures. Deepmd-Kit is a tool that integrates these two different computational approaches. The extrapolation and Deepmd methods were applied to the materials Li10GeP2S12, Li10SiP2S12, Li10GePS12Cl, and Li10SiPS12Cl, respectively. Both methods showed that the lithium ions favor the c direction when diffusing in the LGPS-type solid-state electrolytes. The ionic conductivity is more accurate with the dependent method compared with experiments.