Purpose:Novel panax notoginsenoside-loaded core-shell hybrid liposomal vesicles (PNS-HLV) were developed to resolve the restricted bioavailability of PNS and to enhance its protective effects in vivo on oral administration.Methods:Physicochemical characterizations of PNS-HLV included assessment of morphology, particle size and zeta potential, encapsulation efficiency (EE%), stability and in vitro release study. In addition, to evaluate its oral treatment potential, we compared the effect of PNS-HLV on global cerebral ischemia/reperfusion and acute myocardial ischemia injury with those of PNS solution, conventional PNS-loaded nanoparticles, and liposomes.Results:In comparison with PNS solution, conventional PNS-loaded nanoparticles and liposomes, PNS-HLV was stable for at least 12 months at 4°C. Satisfactory improvements in the EE% of notoginsenoside R1, ginsenoside Rb1, and ginsenoside Rg1 were shown with the differences in EE% shortened and the greater controlled drug release profiles were exhibited from PNS-HLV. The improvements in the physicochemical properties of HLV contributed to the results that PNS-HLV was able to significantly inhibit the edema of brain and reduce the infarct volume, while it could markedly inhibit H2O2, modified Dixon agar, and serum lactate dehydrogenase, and increase superoxide dismutase (P < 0.05).Conclusion:The results of the present study imply that HLV has promising prospects for improving free drug bioactivity on oral administration.
To avoid the blurred edges, noise, and halos caused by guided image filtering algorithm, this paper proposed a nonlinear gradient domain-guided image filtering algorithm for image dehazing. To dynamically adjust the edge preservation and smoothness of dehazed images, this paper proposed a fractional-order gradient descent with momentum RBF neural network to optimize the nonlinear gradient domain-guided filtering (NGDGIF-FOGDMRBF). Its convergence is proved. In order to speed up the convergence process, an adaptive learning rate is used to adjust the training process reasonably. The results verify the theoretical results of the proposed algorithm such as its monotonicity and convergence. The descending curve of error values by FOGDM is smoother than gradient descent and gradient descent with momentum method. The influence of regularization parameter is analyzed and compared. Compared with dark channel prior, histogram equalization, homomorphic filtering, and multiple exposure fusion, the halo and noise generated are significantly reduced with higher peak signal-to-noise ratio and structural similarity index.
Human vision is sensitive to the changes of local image details, which are actually image gradients. To enhance faint infrared image details, this article proposes a gradient field specification algorithm. First we define the image gradient field and gradient histogram. Then, by analyzing the characteristics of the gradient histogram, we construct a Gaussian function to obtain the gradient histogram specification and therefore obtain the transform gradient field. In addition, subhistogram equalization is proposed based on the histogram equalization to improve the contrast of infrared images. The experimental results show that the algorithm can effectively improve image contrast and enhance weak infrared image details and edges. As a result, it can give qualified image information for different applications of an infrared image. In addition, it can also be applied to enhance other types of images such as visible, medical, and lunar surface.
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