Objective:To investigate the acceptance of advance care planning and its influencing factors in heart failure patients.Methods:A total of 208 patients with heart failure were surveyed by general data questionnaires and advance care planning acceptance questionnaires.Results:The total score of advance care planning acceptance of heart failure patients was (44.26 ± 11.73), the score of feeling dimension was (13.67 ± 5.72), the score of attitude dimension was (30.59 ± 6.33). 53.4%(111/208) of patients were willing to accept the talking about advance care planning. Regression analysis results showed that education level, New York Heart Association (NYHA) classification, communication status with medical staff and whether they had received life-sustaining treatment were important factors influencing of the acceptance of advance care planning in patients with heart failure.Conclusion:Patients with heart failure had higher acceptance of advance care planning. In clinical work, it is necessary to strengthen the scientific popularization of advance care planning in patients with low education level, low NYHA grade and no exposure to life-sustaining treatment. And strengthen the daily communication with patients to prepare for the follow-up advance care planning related communication.

Clinical microbiology examination technology experiment is an important part of clinical microbiology examination technology teaching. In the experimental teaching of clinical microbiology examination technology, the virtual simulation technology was combined with traditional teaching to give full play to the advantages of the virtual experimental platform. As to experimental projects that couldn't be carried out in traditional teaching and some important experimental projects, students could learn on the virtual experimental platform, and after learning, they would participate in the corresponding assessment. The perfect combination of the two can solve the problem of high experimental cost and limited experimental content in the current experimental class, make up for the shortcomings of traditional teaching, realize the sharing of teaching resources. Besides, it can strengthen the students' experimental operation skills and enhance the interest of learning for cultivation of application-oriented medical talents.

The new training standards of medical laboratory technology demand the graduates of medical laboratory technology should have the basic scientific research ability and quality. In order to com-plete the teaching work better and achieve the above training objectives, the members of the research group have made some attempts, adopting PBL, CBL and other teaching methods in theoretical teaching, develop-ing open experiments and comprehensive design experiments in experimental teaching, organizing students to study extracurricular undergraduate subject in spare time, in order to provide some basis for the training of students majoring in medical laboratory technology.

Objective:To propose a model that could improve image quality of cone-beam computed tomography(CBCT),which based on region-discriminative generative adversarial networks(GAN),in radiotherapy for cervical cancer,so as to meet the requirements of self-adaptive radiotherapy for image quality.Methods:We employed a region-discriminative strategy and a generative adversarial networks idea to construct a model of improving CBCT image quality that could focus on local details of the images of radiotherapy for cervical cancer,which discriminator could improve the quality of generating local details of images.This model of image quality was applied to CBCT images of radiotherapy for cervical cancer.And then,the effects of processing image were evaluated through quantitative indicators and visualization.Results:Both texture clarity and contrast were significantly enhanced after CBCT image quality was improved.The signal to noise ratio of peak value of images was increased by 47.2%,and the indicator of similarity of structure was enhanced to>0.838.Compared with other model,both visualization and indicators can appear better efficiency of model.Compared with Unet network and CycleGAN network,the similarities of structure were respectively increased by 11.88% and 19.54%,and the signal to noise ratios were respectively increased by 19.75% and 25.99%.Conclusion:The GAN bases on region-discrimination can significantly improve the quality of generating integral and detailed CBCT image of radiotherapy for cervical cancer,which can provide new technical pathway for image quality of CBCT with low dose,and can play an important role for improving safety and effectiveness of radiotherapy.It has importantly clinical value for formulating and executing radiotherapy plan.

Objective:To develop a machine learning algorithm based on habitat imaging(HI),which can be used in the grading of gliomas by using multimodal magnetic resonance imaging(MRI),so as to construct the model of support vector machine(SVM)and the visualized heterogeneous regions of gliomas.Methods:A total of 335 glioma patients were collected from the 2019 brain tumor segmentation(BraTS)challenge competition of World Health Organization(WHO),which included 259 cases with high-grade gliomas(HGG)and 76 cases with low-grade gliomas(LGG).Subregions were divided based on HI technology.The PyRadiomics open-source package was used to extract the image features of region of interest(ROI),and to screen the features that stronger correlated with the high and low-grade gliomas.An SVM model was used to classify and predict the screened feature data.The heterogeneity of gliomas in images was analyzed through visualized characterization.The efficacy of glioma grading was assessed by using the area under curve(AUC)of the receiver operating characteristic(ROC)curve.Results:The AUC of test set exceeded 90%.The average accuracy of the performance indicators of test set was(92.74±2.88)%,and the average sensitivity was(93.90±2.10)%,and the average specificity was(90.36±4.59)%,and the average F1 score was(95.24±0.66)%when the tumors were divided into six habitat regions.The SVM model could showed important sub-regions in glioma grading in three-dimensional space.Conclusion:The study method based on HI has significant advantages in glioma grading,which can effectively realize visualized heterogeneity of tumor and construct model of the heterogeneity of tumor.