Objective:To construct a combined radiomics model based on preoperative enhanced computed tomography (CT) examination for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC), and provide biological explanations for the radiomics model.Methods:The retrospective cohort study was conducted. The messenger RNA (mRNA) of 424 HCC patients, the clinicopathological data of 39 HCC patients entered into the Cancer Genome Atlas database from its establishment until January 2023, and the clinicopathological data of 53 HCC patients who were admitted to the Gansu Provincial People′s Hospital from January 2020 to January 2023 were collected. The 92 HCC patients were randomly divided into a training dataset of 64 cases and a test dataset of 28 cases with a ratio of 7∶3 based on a random number table method. The CT images of patients in the arterial phase and portal venous phase as well as the corresponding clinical data were analyzed. The 3Dslicer software (version 5.0.3) was used to register the CT images in the arterial phase and portal venous phase and delineate the three-dimensional regions of interest. The original images were preprocessed and the corresponding features were extracted by the open-source software FAE (version 0.5.5). After selecting features using the Least Absolute Shrinkage and Selection Operator, the radiomics model was constructed and the radiomics score (R-score) was calculated. The nomogram was constructed by integrating clinical parameters, imaging features and R-score based on Logistic regression. The gene modules related to radiomics model were obtained and subjected to enrichment analysis by conducting weighted gene co-expression network analysis and correlation analysis. Observation indicators: (1) comparison of clinical characteristics of patients with different MVI properties; (2) establishment of MVI risk model; (3) evaluation of MVI risk model; (4) clustering of gene modules; (5) functional enrichment of feature-correlated gene modules. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the independent sample t test. Measurement data with skewed distribution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Comparison of count data was conducted using the chi-square test. The intra-/inter-class correlation coefficient (ICC) was used to assess the inter-observer consistency of radiomics feature extracted by different observers. ICC >0.75 indicated a good consistency in feature extraction. The Logistic regression model was used for univariate and multivariate analyses. The receiver operating characteristic curve was drawn, and the area under curve (AUC), the decision curve and the calibration curve were used to evaluate the diagnostic efficacy and clinical practicality of the model. Results:(1) Comparison of clinical characteristics of patients with different MVI properties. Of 92 HCC patients, there were 47 cases with MVI-positive and 45 cases with MVI-negative, and there were significant differences in hepatitis, tumor diameter, peritumoral enhancement, intratumoral arteries, pseudocapsule and smoothness of tumor margin between them ( χ2=5.308, 9.977, 47.370, 32.368, 21.105, 31.711, P<0.05). (2) Establishment of MVI risk model. A total of 1 781 features were extrac-ted from arterial and portal venous phases of the intratumoral and peritumoral regions. After feature dimension reduction, 8 radiomics features were selected from arterial and portal venous phases to construct the combined model. Results of multivariate analysis showed that peritumoral enhancement, intratumoral arteries, pseudocapsule, smoothness of tumor margins, and R-score were independent risk factors for MVI in patients with HCC [ hazard ratio=0.049, 0.017, 0.017, 0.021, 2.539, 95% confidence interval ( CI) as 0.005-0.446, 0.001-0.435, 0.001-0.518, 0.001-0.473, 1.220-5.283, P<0.05]. A nomogram model was constructed incorporating peritumoral enhancement, intratumoral arteries, pseudocapsule, smoothness of tumor margins, and R-score. (3) Evaluation of the MVI risk model. The AUC of radiomics model was 0.923 (95% CI as 0.887-0.944) and 0.918 (95% CI as 0.894-0.945) in the training dataset and test dataset, respectively. The AUC of nomogram model, incorpora-ting both the R-score and radiomics features, was 0.973 (95% CI as 0.954-0.988) and 0.962 (95% CI as 0.942-0.987) in the training dataset and test dataset, respectively. Results of decision curve showed that the nomogram had better clinical utility compared to the R-score. Results of calibration curve showed good consistency between the actual observed outcomes and the nomogram or the R-score. (4) Clustering of gene module. Results of weighted gene co-expression network analysis showed that 8 gene modules were obtained. (5) Functional enrichment of feature-related gene modules. Results of correlation analysis showed 4 gene modules were significantly associated with radiomics features. The radiomics features predicting of MVI may be related to pathways such as the cell cycle, neutrophil extracellular trap formation, and PPAR signaling pathway. Conclusions:The combined radiomics model based on preoperative enhanced CT imaging can predict the MVI status of HCC. By obtaining mRNA gene expression profiles associated with radiomics features, a biological interpretation of the radiomics model is provided.

OBJECTIVETo investigate the quality of life of elderly diabetic patients and its influencing factors.

METHODSBy randomized cluster sampling, we conducted a survey in 1450 elderly residents (over 60 years old) living in urban, suburban and rural areas in south and north Anhui province. We evaluated the quality of life of the elderly diabetic patients using a demographic information questionnaire and full items on Short Form (36) Health Survey (SF-36).

RESULTSThe elderly diabetic patients had lower scores in all dimensions of quality of life than the elderly without diabetes. Multiple linear regression analysis showed a linear regression in the quality of life among the elderly diabetic patients in terms of geographic regions, education, personality, sleep quality, and age.

CONCLUSIONElderly diabetic patients have generally poor quality of life, which was subjected to the influences by geographic regions, education, personality, sleep quality, and age, suggesting the necessity of corresponding interventions to improve the quality of life of these patients.

Aged ; Aged, 80 and over ; China ; epidemiology ; Diabetes Mellitus, Type 2 ; epidemiology ; Humans ; Middle Aged ; Quality of Life ; Regression Analysis ; Surveys and Questionnaires

Objective? To investigate the status of caregiver preparedness and adult attachment among the spouse of young and middle-aged stroke patients,and to analyze the correlation between them. Methods? From January 2017 to May 2018,110 spouses of stroke patients who were treated in Affiliated Hangzhou First People＇s Hospital, Zhejiang University School of Medicine were selected as subjects by convenience sampling. The General Information Questionnaire,Care Preparedness Scale (CPS),and Experience in Close Relationship Scale (ECR) were used in the survey. Totally 110 questionnaires were distributed, and 102 valid ones were retrieved, yielding an effective recovery rate of 92.73%. Results? The total CPS score of 102 young and middle-aged stroke patients was (12.87±5.39). The scores of attachment anxiety and attachment avoidance in ECR were (57.96 ±11.69) and (48.06 ±12.10). There were statistically significant differences in the scores of preparedness of caregivers among spouse caregivers of different ages, self reported health status and nursing experience (P＜ 0.05). Attachment anxiety dimension was negatively correlated with caregiver preparedness score (r=-0.318,P＜0.01), and attachment avoidance dimension was negatively correlated with caregiver preparedness score (r=-0.233,P＜ 0.01). Conclusions? The preparedness status of the spouses caregivers of young and middle-aged stroke patients are at a low level. Medical workers can improve their care preparedness by giving them home care knowledge guidance,reducing their attachment avoidance and attachment anxiety so as to improve their preparedness.

AIM:To study the effect of NEDD8-conjugating enzyme UBE2F on lung adenocarcino-ma metastasis.METHODS:The expression of UBE2F in lung adenocarcinoma was analyzed using TIMER2.0,UALCAN and HPA databases.Kaplan-Mei-er Plotter database was used to analyze the rela-tionship between UBE2F expression and survival rate of lung adenocarcinoma.A UBE2F-knockout lung adenocarcinoma cell line was constructed us-ing CRISPR/Cas9 technology,and a UBE2F-knockout lung adenocarcinoma metastasis model was con-structed in nude mice to verify the effect of UBE2F knockout on lung adenocarcinoma metastasis.The effects of UBE2F knockout on invasion and migra-tion of lung adenocarcinoma cells were examined by cell scratch assay and Transwell invasion and mi-gration assays.The effect of down-regulated UBE2F expression on snail expression,a key marker of epi-thelial-mesenchymal transition(EMT),was detect-ed by Western blot and Real time PCR.RESULTS:Multiple database analysis showed that UBE2F was highly expressed in lung cancer,and Kaplan-Meier Plotter analysis showed that high expression of UBE2F in lung adenocarcinoma had better progno-sis than low expression.In vivo experiments showed that compared with control group,the number of nodules metastasized on the lung sur-face of nude mice after UBE2F knockout was signifi-cantly increased(P<0.05).Cell scratch assay and Transwell assay showed that UBE2F enhanced the migration and invasion ability of lung cancer cells after knockout,and the difference were statistically significant(P<0.05).Western blot and Real time PCR results indicated that the level of EMT tran-scription factor snail protein and mRNA increased after UBE2F knockout.CONCLUSION:In lung ade-nocarcinoma cells,UBE2F down-regulation leads to Snail accumulation and promotes invasion and me-tastasis of lung adenocarcinoma cells.

Objective To explore the value of model established with radiomics features based on contrast enhanced arterial phase CT and model with radiogenomics for predicting prognosis of ovarian serous cystadenocarcinoma(OSC).Methods Enhanced arterial phase CT images of 110 OSC patients were retrospectively collected from 2 centers and The Cancer Imaging Archive(TCIA)database.The radiomics features were extracted,among those related to prognosis were selected to establish a radiomics Cox regression model.Genes data of 399 OSC patients were obtained from The Cancer Genome Atlas(TCGA)database,and genes related to the radiomics features included in the above radiomics model were identified with high Pearson correlation coefficient,and then enrichment gene analyses were performed.For 57 OSC cases with complete enhanced CT and gene data,the hub genes which had the highest connectivity with radiomics prognosis predicting model were detected using Cox regression and protein-protein interaction(PPI).Furthermore,a radiogenomics prognosis predicting model was established with the hub genes.The efficiencies of these 2 models for predicting prognosis of OSC patients were analyzed.Results Finally,the radiomics model included 5 OSC prognosis-related radiomics features,with C-index of 0.782 and 0.735 in corresponding training and test set,respectively.Meanwhile,the radiogenomics model included 30 prognostic hub genes,with C-index of 0.673 and 0.659 in corresponding training and test set,respectively.The survival rates of patients with better predicted prognosis according to radiomics model and radiogenomics model were both higher compared with the others(both P＜0.05).Totally 1 135 mRNA genes were found being associated with radiomics model,including biological behaviors such as cell adhesion,and signaling pathways such as PI3K-Akt,extracellular matrix receptor interaction pathway and type 1 diabetes pathway.Conclusion The radiomics model was effective for predicting prognosis of OSC patients.Analysis of mRNA bioinformatics in OSC patients might provide biological interpretations for the radiomics model.

Objective To investigate the quality of life of elderly diabetic patients and its influencing factors. Methods By randomized cluster sampling, we conducted a survey in 1450 elderly residents (over 60 years old) living in urban, suburban and rural areas in south and north Anhui province. We evaluated the quality of life of the elderly diabetic patients using a demographic information questionnaire and full items on Short Form (36) Health Survey (SF-36). Results The elderly diabetic patients had lower scores in all dimensions of quality of life than the elderly without diabetes. Multiple linear regression analysis showed a linear regression in the quality of life among the elderly diabetic patients in terms of geographic regions, education, personality, sleep quality, and age. Conclusion Elderly diabetic patients have generally poor quality of life, which was subjected to the influences by geographic regions, education, personality, sleep quality, and age, suggesting the necessity of corresponding interventions to improve the quality of life of these patients.

Objective To investigate the quality of life of elderly diabetic patients and its influencing factors. Methods By randomized cluster sampling, we conducted a survey in 1450 elderly residents (over 60 years old) living in urban, suburban and rural areas in south and north Anhui province. We evaluated the quality of life of the elderly diabetic patients using a demographic information questionnaire and full items on Short Form (36) Health Survey (SF-36). Results The elderly diabetic patients had lower scores in all dimensions of quality of life than the elderly without diabetes. Multiple linear regression analysis showed a linear regression in the quality of life among the elderly diabetic patients in terms of geographic regions, education, personality, sleep quality, and age. Conclusion Elderly diabetic patients have generally poor quality of life, which was subjected to the influences by geographic regions, education, personality, sleep quality, and age, suggesting the necessity of corresponding interventions to improve the quality of life of these patients.