Immobilized thrombin on X-ray radiopaque polyvinyl alcohol/chitosan embolic microspheres for precise localization and topical blood coagulation

Trans-catheter arterial embolization(TAE)plays an important role in treating various diseases.The available embolic agents lack X-ray visibility and do not prevent the reflux phenomenon,thus hindering their application for TAE therapy.Herein,we aim to develop a multifunctional embolic agent that combines the X-ray radiopacity with local procoagulant activity.The barium sulfate nanoparticles(BaSO4 NPs)were synthesized and loaded into the polyvinyl alcohol/chitosan(PVA/CS)to prepare the radiopaque BaSO4/PVA/CS microspheres(MS).Thereafter,thrombin was immobilized onto the BaSO4/PVA/CS MS to obtain the thrombin@BaSO4/PVA/CS MS.The prepared BaSO4/PVA/CS MS were highly spherical with diameters ranging from 100 to 300μm.In vitro CT imaging showed increased X-ray visibility of BaSO4/PVA/CS MS with the increased content of BaSO4 NPs in the PVA/CS MS.The biocompatibility assessments demonstrated that the MS were non-cytotoxic and possessed permissible hemolysis rate.The biofunctionalized thrombin@BaSO4/PVA/CS MS showed improved hemostatic capacity and facilitated hemostasis in vitro.Additionally,in vivo study performed on a rabbit ear embolization model confirmed the excellent X-ray radiopaque stability of the BaSO4/PVA/CS MS.Moreover,both the BaSO4/PVA/CS and thrombin@BaSO4/PVA/CS MS achieved superior embolization effects with progressive ischemic necrosis on the ear tissue and induced prominent ultrastructural changes in the endothelial cells.The findings of this study suggest that the developed MS could act as a radiopaque and hemostatic embolic agent to improve the embolization efficiency.

How to accurately extract large-scale urban land?Establishment of an improved fully convolutional neural network model

Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development.In this paper,an improved fully convolution neural network was provided for perceiving large-scale urban change,by modifying network structure and updating network strategy to extract richer feature information,and to meet the requirement of urban construction land extraction under the background of large-scale low-resolution image.This paper takes the Yangtze River Economic Belt of China as an empirical object to verify the practicability of the network,the results show the extraction results of the improved fully convolutional neural network model reached a precision of kappa coefficient of 0.88,which is better than traditional fully convolutional neural networks,it performs well in the construction land extraction at the scale of small and medium-sized cities.