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Most chemical medicines have polymorphs. The difference of medicine polymorphs in physicochemical properties directly affects the stability, efficacy, and safety of solid medicine products. Polymorphs is incomparably important to pharmaceutical chemistry, manufacturing, and control. Meantime polymorphs is a key factor for the quality of high-end drug and formulations. Polymorph prediction technology can effectively guide screening of trial experiments, and reduce the risk of missing stable crystal form in the traditional experiment. Polymorph prediction technology was firstly based on theoretical calculations such as quantum mechanics and computational chemistry, and then was developed by the key technology of machine learning using the artificial intelligence. Nowadays, the popular trend is to combine the advantages of theoretical calculation and machine learning to jointly predict crystal structure. Recently, predicting medicine polymorphs has still been a challenging problem. It is expected to learn from and integrate existing technologies to predict medicine polymorphs more accurately and efficiently.
RESUMO
Objective To propose a cerebrovascular image segmentation method for magnetic resonance angiography(MRA)based on improved UNet.Methods Firstly,the UNet network was used as the basic segmentation model and the residual neural network was incorporated to effectively alleviate the training pressure of the deep network and promote information transfer;secondly,the compression and excitation modules were added to improve the sensitivity of the network to cerebrovascular features;finally,the atrous spatial pyramidal pooling(ASPP)module was appended to obtain multi-scale feature information to further enhance the segmentation accuracy.The model based on improved UNet was tested on the TOF-MRA public dataset and compared with the models of UNet,ResNet and ResUNet++.Results The model based on improved UNet had a Dice similarity coefficient of 0.75 and an accuracy of 0.72,which were both higher than those of the models of UNet,ResNet and ResUNet++.Conclusion The method proposed segments MRA cerebrovascular images effectively,and thus can assist clinicians in disease diagnosis.[Chinese Medical Equipment Journal,2023,44(10):7-12]
RESUMO
Objective To establish a regional remote imaging diagnosis platform to solve the problems in medical treatment of the population in remote areas and etc as well as the non-balanced medical resources distribution.Methods Standardization transform of non-standard PACS images and texts was executed with PACS platform,DICOM and HL7 heterogeneous module. KM-SES remote diagnosis system was used to integrate the components of clinical operation, communication network, database and etc so as to construct a regional imaging platform.Results The platform standardized the imaging process and quality control inside and outside the hospital,and contributed to shortening the treatment time and reducing the vacancy rate. Conclusion The platform implements remote consultation,diagnosis and examination appointment,and facilitates the medical service to the population in remote areas.[Chinese Medical Equipment Journal,2018,39(5):50-54,67]