1.DiaLecticaL nursing abiLity and training among practice nursing students of traditionaL Chinese medicine coLLeges and universities
Qixuan SUN ; Wenjing TU ; Lu HUANG ; Sixuan HAN
Chinese Journal of Modern Nursing 2019;25(7):811-814
Objective? To expLore the diaLecticaL nursing abiLity and training among practice nursing students of TraditionaL Chinese Medicine coLLeges and universities so as to provide a reference for education reform and practice management of coLLeges and universities. Methods? In March 2018, we seLected 308 practice nursing undergraduates of schooL of nursing in Nanjing University of Chinese Medicine practicing at designated hospitaLs as subjects by purposive sampLing. ALL of them were investigated with the seLf-designed diaLecticaL nursing abiLity and training of practice nursing students of TraditionaL Chinese Medicine coLLeges and universities questionnaire. In this study, a totaL of 308 questionnaires were sent out and 254 vaLid questionnaires were coLLected. ResuLts? The scores of the diaLecticaL nursing abiLity of nursing students seLf-evaLuation questionnaire before and after practice were (4.91±1.90) and (4.72±1.92) respectiveLy with no statisticaL difference (t=1.379, P=0.170). Practice nursing students' master of diaLecticaL nursing basic knowLedge ranged from 69.29% to 89.76%. ConcLusions? Practice nursing students have the Low abiLity of diaLecticaL nursing. Practice hospitaLs shouLd improve the cuLtivate awareness and teaching LeveL, and satisfy nursing students' training needs so as to improve the diaLecticaL nursing abiLity of them.
2.Construction of Aβ1-42 plasmid and its binding to calmodulin
Shuang QI ; Xuanxuan SUN ; Qixuan WANG ; Yiting HE ; Jiarui LI ; Jingyang SU ; Liying HAO
Journal of China Medical University 2024;53(6):495-500
Objective To investigate the involvement of calmodulin(CaM)in the pathogenesis of Alzheimer disease(AD)and the mechanism by which CaM binds to amyloid-β(Aβ).Methods The hub genes expressed in AD and predicted to be the target proteins for AD prevention and treatment were obtained using bioinformatics methods.The GST-Aβ1-42 recombinant plasmid was constructed through genetic recombination and was then sequenced.The recombinant plasmids were identified using agarose gel electrophoresis,while the extracted and purified GST-Aβ1-42 fusion protein was confirmed using SDS-PAGE gel electrophoresis.GST pull-down assay was used to detect the interaction between GST-Aβ1-42 protein and CaM,expressed in the plasmid.Results The top 20 hub genes in degree ranking were obtained.The DNA sequencing results of the plasmid proved that the recombinant plasmid was successfully constructed.The agarose gel electrophoresis results indicated that the fragment digested by the enzyme was similar to the molecular weight of the Aβ1-42 gene seg-ments,further proving the successful construction of the recombinant plasmid.Binding of GST-Aβ1-42 protein to CaM in a concentration dependent manner was revealed through the GST pull down experiment.Conclusion The GST-Aβ1-42 recombinant plasmid is success-fully constructed and is shown to bind to CaM.
3.Research progress of multimodal medical image fusion methods
Wei CHEN ; Kangkang SUN ; Qixuan LI ; Kai XIE ; Xinye NI
Chinese Journal of Radiological Health 2023;32(5):580-585
In the current clinical diagnosis, medical images have become an important basis for diagnosis, and different modes of medical images provide different tissue information and functional information. Single-mode images can only provide single diagnostic information, by which difficult and complicated diseases cannot be diagnosed, and comprehensive and accurate diagnostic results can be obtained only with the help of multiple diagnostic information. The multimodal fusion technology fuses multiple modes of medical images into single-mode images, and thus the single-mode images contain complementary information between multiple modes of images, so that sufficient information for clinical diagnosis can be obtained in a single image. In this paper, the multimodal medical image fusion methods are sorted into two types, namely the traditional fusion method and the fusion method based on deep learning.