Objective To investigate the effect of image registration on quantitative measurements of free breathing diffusion kurtosis imaging (DKI) in normal human kidney. Methods Twenty healthy volunteers were prospectively enrolled to undergo DKI imaging with a 3.0 T MR scanner. Three b values (0, 500, and 1 000 s/mm2) were adopted,with image registration performed after image acquisition. Acquired images were fitted using the DKI fitting model to generate the DKI metric maps,which were performed on both the pre-registration images and post-registration images. Image quality of the derived metric maps (before and after image registration,respectively) was assessed by two radiologists. Measurements of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (D|), axial diffusivity (D⊥), mean kurtosis (MK), radial kurtosis (K|) and axial kurtosis (K⊥) were conducted. The inter-observer reproducibility of the image quality assessment was analyzed using intra-class correlation coefficient(ICC). Wilcoxon signed-rank test was used to evaluate the difference in the subjective scores of the metric maps between those obtained before registration and those after registration. While paired t test or Wilcoxon signed-rank test was performed to analyze the difference in the quantitative measurements of DKI metrics of the renal cortex and medulla between those obtained before registration and those after registration.Results For the inter-observer reproducibility, satisfactory ICCs were obtained for the quantitative metric measurements (pre-registration:0.784 to 0.821;post-registration:0.836 to 0.934). Significant difference was notice between subjective scores for the quality of metric maps (P<0.05 for each comparison). In both the renal cortex and medulla, significant difference was noticed between each metric value obtained with pre-registration images and that with post-registration images (P<0.05 for each comparison). Conclusion Image registration can not only offer higher quality DKI metric maps,but also has effect on the quantitative measurements of obtained metric maps.

Objective:To explore the application of experiential teaching combined with case-based learning (CBL) in the teaching of nursing students in intensive care unit (ICU).Methods:A total of 63 nurses who had internship in the ICU of our hospital from April 2016 to March 2017 were selected as group A, 63 nurses from April 2017 to March 2018 were selected as group B, 63 nurses from April 2018 to March 2019 were selected as group C, and 63 nurses from April 2019 to March 2020 were selected as group D. Group A adopted conventional teaching method, group B adopted conventional teaching method + CBL, group C adopted conventional teaching method + experiential teaching method, and group D adopted conventional teaching method + CBL + experiential teaching method. All of them had been taught for 3 months. The scores of theory and skills examination, humanistic care, supportive communication and critical thinking ability before and after teaching, and satisfaction with teaching mode were compared among the four groups. SPSS 26.0 was used for one-way variance analysis, SNK -q test and χ2 test. Results:The scores of theory and skill examination in group D were higher than those in the other three groups, and those in group B and C were higher than those in group A ( P<0.05). After teaching, the scores of humanistic care, supportive communication and critical thinking ability of the four groups were higher than those before teaching ( P<0.05). After teaching, the scores of humanistic care, supportive communication and critical thinking ability of group D were higher than those of the other three groups ( P<0.05), and those of group B and group C were higher than those of group A ( P<0.05). The satisfaction scores of teaching skills, teaching content and teaching effect in group D were higher than those in the other three groups ( P<0.05), and those in group B and C were higher than those in group A ( P<0.05). Conclusion:On the basis of conventional teaching method, experiential teaching and CBL can improve the performance of nursing students, improve the ability of humanistic care, supportive communication and critical thinking, and improve the satisfaction of nursing students. The combination of the two has a better effect.

Objective To develop and validate a CT-based radiomics predictive model for preoperative predicting the stage of non-small cell lung cancer (NSCLC). Methods In this retrospective study, 657 patients with histologically confirmed was collected from October 2007 to December 2014.The primary dataset consisted of patients with histologically confirmed NSCLC from October 2007 to April 2012, while independent validation was conducted from May 2012 to December 2014.All the patients underwent non-enhanced and contrast-enhanced CT images scan with a standard protocol. The pathological stage (PTNM) of patients with NSCLC were determined by the intraoperative and postoperative pathological findings,and were divided into early stage(Ⅰ,Ⅱstage)and advanced stage(Ⅲ,Ⅳstage).A list of radiomics features were extracted using the software Matlab 2014a and the corresponding radiomics signature was constructed. Multivariable logistic regression analysis was performed with radiomics signature and clinical variables for developing the prediction model. The model performance was assessed with respect to discrimination using the area under the curve (AUC) of receiver operating characteristic(ROC) analysis. Results The discrimination performance of radiomics signature yielded a AUC of 0.715[95% confidence interval (CI):0.709 to 0.721] in the primary dataset and a AUC of 0.724(95% CI:0.717 to 0.731) in the validation dataset. On multivariable logistic regression, radiomics signature, tumor diameter, carcinoembryonic antigen (CEA) level, and cytokeratin 19 fragment (CYFRA21-1) level were showed independently associated with the stage ( Ⅰ,Ⅱ stage vs. Ⅲ, Ⅳ stage) of NSCLC. The prediction model showed good discrimination in both primary dataset (AUC=0.787, 95%CI:0.781 to 0.793;sensitivity=73.4%, specificity=72.2% ,positive predictive value=0.707,negative predictive value=0.868) and independent validation dataset (AUC=0.777, 95% CI:0.771 to 0.783,sensitivity=91.3% ,specificity=67.3% ,positive predictive value=0.607, negative predictive value=0.946). Conclusion The radiomics predictive model, which integrated with the radiomics signature and clinical characteristics can be used as a promising and applicable adjunct approach for preoperatively predicting the clinical stage (Ⅰ,Ⅱ stage vs. Ⅲ,Ⅳ stage) of patients with NSCLC.