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Bo Li,1,2 Li Jiang,1,2 Ni Wu,1,2 Yao Chen,1,2 Zhi Xu,3 Fengcheng Xu,2,4 Heping Chen,1 Tianhu Liu2,4 1Clinical Pharmacy Department, The Third Affiliated Hospital of Chengdu Medical College, Pidu District People’s Hospital, Chengdu, Sichuan, People’s Republic of China; 2Drug Clinical Trial Management Center, The Third Affiliated Hospital of Chengdu Medical College, Pidu District People’s Hospital, Chengdu, Sichuan, People’s Republic of China; 3Department of Respiratory and Critical Care Medicine, The Third Affiliated Hospital of Chengdu Medical College, Pidu District People’s Hospital, Chengdu, Sichuan, People’s Republic of China; 4Department of Cardiovascular Medicine, The Third Affiliated Hospital of Chengdu Medical College, Pidu District People’s Hospital, Chengdu, Sichuan, People’s Republic of ChinaCorrespondence: Bo Li, Clinical Pharmacy Department, The Third Affiliated Hospital of Chengdu Medical College, Pidu District People’s Hospital, Chengdu, Sichuan, People’s Republic of China, Email lbydcg@163.com Tianhu Liu, Department of Cardiovascular Medicine, The Third Affiliated Hospital of Chengdu Medical College, Pidu District People’s Hospital, Chengdu, Sichuan, People’s Republic of China, Email lthzgl@163.comBackground: In 2021, National Health Commission of the People’s Republic of Chinese issued a document that no longer recommended the routine skin test for cephalosporin (RSTC). However, there is still resistance to the cancellation of RSTC in a primary hospital. The study aimed to explore the potential factors for hindering the abolition of the RSTC in a county-level hospital based on the PRECEDE model.Methods: The cross-sectional study was conducted on healthcare workers in the Pidu District People’s Hospital, Chengdu, by online questionnaire from September 10 to September 25 in the 2021.The PRECEDE model was used to divide the potential factors of healthcare professionals in hindering the abolition of the RSTC into predisposing f

Laser speckle contrast imaging (LSCI) is a full-field, high spatiotemporal resolution and low-cost optical technique for measuring blood flow, which has been successfully used for neurovascular imaging. However, due to the low signal-noise ratio and the relatively small sizes, segmenting the cerebral vessels in LSCI has always been a technical challenge. Recently, deep learning has shown its advantages in vascular segmentation. Nonetheless, ground truth by manual labeling is usually required for training the network, which makes it difficult to implement in practice. In this manuscript, we proposed a deep learning-based method for real-time cerebral vessel segmentation of LSCI without ground truth labels, which could be further integrated into intraoperative blood vessel imaging system. Synthetic LSCI images were obtained with a synthesis network from LSCI images and public labeled dataset of Digital Retinal Images for Vessel Extraction, which were then used to train the segmentation network. Using matching strategies to reduce the size discrepancy between retinal images and laser speckle contrast images, we could further significantly improve image synthesis and segmentation performance. In the testing LSCI images of rodent cerebral vessels, the proposed method resulted in a dice similarity coefficient of over 75%., QC 20220111