AIM OF THE STUDY: To investigate the anti-angiogenic mechanism of EC and its anti-tumor effect by suppressing angiogenesis.
MATERIALS AND METHODS: The in vitro anti-angiogenic effect was evaluated using HUVECs model induced by VEGF and zebrafish model in vivo. The influence of the EC on phosphorylation of VEGFR2 and its downstream signaling pathways were evaluated by western blotting assay. Molecule docking technology was conducted to explore the interaction between EC and VEGFR2. SPR assay was used for detecting the binding affinity between EC and VEGFR2. To further investigate the molecular mechanism of EC on anti-angiogenesis, VEGFR2 knockdown in HUVECs and examined the influence of the EC. Anti-tumor activity of EC was evaluated using colony formation assay and apoptosis assay. The inhibitory effect of EC on tumor growth was explored using HT29 colon cancer xenograft model.
RESULTS: EC obviously inhibited proliferation, migration, invasion and tube formation of VEGF-induced HUVECs. EC also induced apoptosis of HUVECs. Moreover, it inhibited the development of vessel formation in zebrafish. Further investigations demonstrated that EC could suppress the phosphorylation of VEGFR2, and its downstream signaling pathways were altered in VEGF-induced HUVECs. EC formed a hydrogen bond to bind with the ATP binding site of the VEGFR2, and EC-VEGFR2 interaction was shown in SPR assay. The suppressive effect of EC on angiogenesis was abrogated after VEGFR2 knockdown in HUVECs. EC inhibited the colon cancer cells colony formation and induced apoptosis. In addition, EC suppressed tumor growth in colon cancer xenograft model, and no detectable hepatotoxicity and nephrotoxicity. In addition, it inhibited the phosphorylation of VEGFR2, and its downstream signal pathways in tumor.
CONCLUSIONS: EC could inhibit tumor growth in colon cancer by suppressing angiogenesis via VEGFR2 signaling pathway, and suggested EC as a promising candidate for colon cancer treatment.
METHODS: Case information from 192 children was collected from outpatient and inpatient clinics using a survey questionnaire. These included 90 pediatric burn cases and 102 controls who were children without burns. A stepwise logistic regression analysis was used to determine the risk factors for pediatric burns in order to establish a model. The goodness-of-fit for the model was assessed using the Hosmer and Lemeshow test as well as receiver operating characteristic and internal calibration curves. A nomogram was then used to analyze the contribution of each influencing factor to the pediatric burns model.
RESULTS: Seven variables, including gender, age, ethnic minority, the household register, mother's employment status, mother's education and number of children, were analyzed for both groups of children. Of these, age, ethnic minority, mother's employment status and number of children in a household were found to be related to the occurrence of pediatric burns using univariate logistic regression analysis (p 0.2 and variance inflation factor <5 showed that age was a protective factor for pediatric burns [odds ratio (OR) = 0.725; 95% confidence interval (CI): 0.665-0.801]. Compared with single-child parents, those with two children were at greater risk of pediatric burns (OR = 0.389; 95% CI: 0.158-0.959). The ethnic minority of the child and the mother's employment status were also risk factors (OR = 6.793; 95% CI: 2.203-20.946 and OR = 2.266; 95% CI: 1.025-5.012, respectively). Evaluation of the model used was found to be stable. A nomogram showed that the contribution in the children burns model was age > mother's employment status > number of children > ethnic minority.
CONCLUSIONS: This study showed that there are several risk factors strongly correlated to pediatric burns, including age, ethnic minority, the number of children in a household and mother's employment status. Government officials should direct their preventive approach to tackling the problem of pediatric burns by promoting awareness of these findings.
METHODS: Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics.
RESULTS: The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models.
CONCLUSION: The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC.