Objective To understand the real experience of kinesiophobia in patients after bone transport technique,providing references for taking targeted nursing interventions to alleviate kinesiophobia of patients.Methods Purposive sampling method was employed to select 15 patients who underwent bone transport technique in the Department of Traumatic Orthopedics in a tertiary A hospital in Guangdong Province from October to December 2023 as the research subjects.Phenomenological research method was utilized to conduct semi-structured interviews with the patients,and Colaizzi 7-step analysis method was applied for data analysis and theme extraction.Results A total of 3 themes and 11 sub-themes were extracted,including the existence of negative psychological experience(fear and concern regarding exercise,excessive alarm in response to pain,helplessness and sadness about the change of life,persistent reflection on past experiences,anxiety and confusion about the future),facing the dilemma of physiological symptoms(pain and discomfort,fatigue and disturbed sleep),taking diversified coping approaches(selecting avoidance strategies,conducting self-adjustment,seeking kinesiophobia related knowledge and exercise guidance,acquiring social support).Conclusion The experience of kinesiophobia in patients after bone transport technique is complex and varied.Medical and nursing staff should prioritize the psychological relief of patients after bone transport technique,pay attention to the assessment and management of kinesiophobia related symptom,provide professional guidance and assist with multi-dimensional support to help patients reduce the experience of kinesiophobia and promote recovery of patients.

Objective To investigate the value of renal CT volumetric texture analysis with machine learning radiomics in assessment of pathological grade of clear cell renal cell carcinoma(ccRCC). Methods Thirty-four biopsy-confirmed ccRCC subjects who had four-phase CT scanning (NC:non-contrast, CM: Corticomedullary, N: Nephrographic, E: Excretory) were collected retrospectively from June 2013 to October 2017 for the study.Non-rigid registration was performed on multi-phase CT images in reference to CM-phase.Each lesion was segmented on CM-phase CT images using our in-house volumetric image analysis platform,"3DQI".A set of fifty-nine volumetric textures,including histogram,gradient,gray level co-occurrence matrix(GLCM),run-length(RL),moments,and shape,was calculated for each segment lesion in each phase as parameters for the training/testing of Random Forest (RF) classifier. Four groups according to pathological Fuhrman grade on a scaleⅠtoⅣ,these tumors were then divided into low(Ⅰ+Ⅱ) and high grade ( Ⅲ + Ⅳ) groups. Feature selection was performed by Boruta algorithm. A 10-fold cross-validation method was applied to validate the RF performance by receiver operating characteristic (ROC) curves analysis to determine the diagnostic accuracy of the model. Results Subjects were divided into four groups by Fuhrman grade on a scaleⅠtoⅣ:3 cases gradeⅠ,19 cases gradeⅡ,8 cases gradeⅢand 4 cases gradeⅣ.In CM-phase,kurtosis and long-run-emphasis(RLE)were selected the most important textures for ccRCC staging among 59 features. The area under curve (AUC) of ROC was 0.88 (79% sensitivity and 82% specificity)by using kurtosis and RLE textures.The mean values of kurtosis and RLE were(-20.00±22.00)×10-2and(3.00±0.40)×10-2for low group,whereas(31.00±32.00)×10-2and(5.00± 0.02)×10-2for high group.Within the mean±SD range of statistics,radiomics can distinguish between low and high grade tumors.In multi-phase analysis,three most important features were selected among 236(59× 4) textures: kurtosis (CM-phase), GLCM homogeneity I (HOMO 1) (E-phase), and GLCM homogeneity 2 (HOMO2)(E-phase).The mean values of HOMO 1(E-phase)and HOMO 2(E-phase)were(19.00±0.03)× 10-2and(11.00±0.02)×10-2for low group,whereas(22.00±0.03)×10-2and(14.00±0.02)×10-2for high group. The AUC was 0.92(93% sensitivity and 87% specificity)by using these three textures. Conclusion This study has demonstrated that renal CT volumetric texture analysis with machine learning radiomics could preoperative accurately perform cancer staging for ccRCC.