目的:为了探讨肝硬化放腹水后应用右旋糖酐40代替人血白蛋白治疗顽固性腹水的临床疗效及其经济性。方法:将216例肝硬化顽固性腹水患者随机分为A,B,C三组。A组:定期放腹水后应用右旋糖酐40;B组:定期放腹水后应用人血白蛋白或血浆;C组:传统治疗方法,限钠和不断增加利尿剂用量。结果:A组分别与B组,C组相比较,其腹水消退时间,ALT复常率,输血不良反应,住院费用,平均住院日,好转治愈率,死亡率,以上各项对比均有显著性差异(Plt;0.05)。血清蛋白量的对比无显著性差异(Pgt;0.05)。结论:肝硬化放腹水后应用右旋糖酐40治疗顽固性腹水,能缩短病程,减少住院日,降低医疗费用,降低死亡率。
ObjectiveTo investigate the risk of myocarditis caused by immune checkpoint inhibitors (ICI). MethodsThe adverse reaction (ADR) reports on myocarditis caused by atelizumab, duvalizumab, pabolizumab, and navulizumab were downloaded from the FDA Adverse Event Reporting System (FAERS) from January 1, 2014 to September 30, 2022. The relevant analysis was conducted on the gender, age, medication dosage, and occurrence time of ICI related myocarditis patients. ResultsA total of 1 892 reports of myocarditis induced by ICI were included. The proportion of myocarditis caused by ICI was higher in males than in females (1.9∶1). The incidence of myocarditis in patients with basic diseases such as diabetes and heart disease, and in the age group 65-75 was relatively high. The incidence of myocarditis caused by navulizumab was high within 30 days with the use of conventional doses, and that of the other three drugs were high within 31 to 90 days. And the incidence of myocarditis is higher when used in combination than when used alone. ConclusionDifferent varieties of ICI can lead to the occurrence of myocarditis, and male, elderly, underlying diseases, and combination therapy may be risk factors for myocarditis caused by ICI.
【Abstract】ObjectiveTo construct eukaryotic expression vector pSecTag2/HygroB-CD59 of human CD59 and transfect NIH3T3 cells after encapsulated by chitosan. MethodsThe human CD59 fragments were obtained by PCR form CD59-pGEM-T Easy Vector, cloned into the eukaryotic expression vector pSecTag2/HygroB, identified by restriction endonuclease’s digestion and DNA sequencing. After the particles of pSecTag2/HygroB-CD59 were encapsulated by chitosan, the NIH3T3 cells were transfected by chitosanCD59 nanoparticles and detected CD59 expression by immunohistochemistry stain. ResultsThe CD59 fragment was 312 bp. Its sequence was as same as CD59 cDNA in Genbank. After having been transfected by chitosan-CD59 nanoparticles in 24 hours, the 3T3 cells showed diffusely positive in the cytoplasms by anti-CD59 immunohistochemistry. ConclusionThe eukaryotic expression vector of human CD59 is constructed and transfected to NIH3T3 cells after encapsulated by chitosan. It will be very helpful for further study on transgenic livers.
Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.
Human society has entered the age of artificial intelligence(AI). Medical practice and education are undergoing profound changes. The government strongly advocates the application of AI in the field of education and it has been incorporated into the national strategy. The integration of medical education and AI technology is changing the paradigm of modern medical education. This paper introduces the current application status of AI in medical education, and analyzes the existing problems and proposes corresponding resolutions, so as to lay a foundation for promoting the integration of medical education and AI.