Medical simulation teaching is a bridge course from theoretical knowledge to clinical practice. At present, the medical simulation teaching is facing many problems. The iSIM is a systematic method to optimize medical simulation teaching. It aims to maximize the effect of medical simulation teaching by various teaching methods and assistant technologies. The combination of iSIM and medical simulation teaching can develop the correct clinical thinking, improve the clinical skills and strengthen the communication skills, so as to improve the medical quality in the real clinical environment. Based on experience Center of Experimental Teaching on Clinical Skills of West China Hospital , this paper introduces how to use iSIM to optimize medical simulation teaching.
Based on the current study of the influence of mechanical factors on cell behavior which relies heavily on experiments in vivo, a culture chamber with a large uniform strain area containing a linear motor-powered, up-to-20-Hz cell stretch loading device was developed to exert mechanical effects on cells. In this paper, using the strain uniformity as the target and the substrate thickness as the variable, the substrate bottom of the conventional incubation chamber is optimized by using finite element technique, and finally a new three-dimensional model of the incubation chamber with “M” type structure in the section is constructed, and the distribution of strain and displacement fields are detected by 3D-DIC to verify the numerical simulation results. The experimental results showed that the new cell culture chamber increased the accuracy and homogeneous area of strain loading by 49.13% to 52.45% compared with that before optimization. In addition, the morphological changes of tongue squamous carcinoma cells under the same strain and different loading times were initially studied using this novel culture chamber. In conclusion, the novel cell culture chamber constructed in this paper combines the advantages of previous techniques to deliver uniform and accurate strains for a wide range of cell mechanobiology studies.
Colorectal cancer (CRC) is a common malignant tumor that seriously threatens human health. CRC presents a formidable challenge in terms of accurate identification due to its indistinct boundaries. With the widespread adoption of convolutional neural networks (CNNs) in image processing, leveraging CNNs for automatic classification and segmentation holds immense potential for enhancing the efficiency of colorectal cancer recognition and reducing treatment costs. This paper explores the imperative necessity for applying CNNs in clinical diagnosis of CRC. It provides an elaborate overview on research advancements pertaining to CNNs and their improved models in CRC classification and segmentation. Furthermore, this work summarizes the ideas and common methods for optimizing network performance and discusses the challenges faced by CNNs as well as future development trends in their application towards CRC classification and segmentation, thereby promoting their utilization within clinical diagnosis.