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Chinese Journal of Radiology ; (12): 535-540, 2023.
Article in Chinese | WPRIM | ID: wpr-992984


Objective:To evaluate the value of preoperative prediction of vessel invasion (VI) of locally advanced gastric cancer by machine learning model based on the venous phase enhanced CT radiomics features.Methods:A retrospective analysis of 296 patients with locally advanced gastric cancer confirmed by pathology in the First Affiliated Hospital of Zhengzhou University from July 2011 to December 2020 was performed. The patients were divided into VI positive group ( n=213) and VI negative group ( n=83) based on pathological results. The data were divided into training set ( n=207) and test set ( n=89) according to the ratio of 7∶3 with stratification sampling. The clinical characteristics of patients were recorded, and the independent risk factors of gastric cancer VI were screened by multivariate logistic regression. Pyradiomics software was used to extract radiomic features from the venous phase enhanced CT images, and the minimum absolute shrinkage and selection algorithm (LASSO) was used to screen the features, obtain the optimal feature subset, and establish the radiomics signature. Four machine learning algorithms, including extreme gradient boosting (XGBoost), logistic, naive Bayes (GNB), and support vector machine (SVM) models, were used to build prediction models for the radiomics signature and the screened clinical independent risk factors. The efficacy of the model in predicting gastric cancer VI was evaluated by the receiver operating characteristic curve. Results:The degree of differentiation (OR=13.651, 95%CI 7.265-25.650, P=0.003), Lauren′s classification (OR=1.349, 95%CI 1.011-1.799, P=0.042) and CA199 (OR=1.796, 95%CI 1.406-2.186, P=0.044) were independent risk factors for predicting the VI of locally advanced gastric cancer. Based on the venous phase enhanced CT images, 864 quantitative features were extracted, and 18 best constructed radiomics signature were selected by LASSO. In the training set, the area under the curve (AUC) of XGBoost, logistic, GNB and SVM models for predicting gastric cancer VI were 0.914 (95%CI 0.875-0.953), 0.897 (95%CI 0.853-0.940), 0.880 (95%CI 0.832-0.928) and 0.814 (95%CI 0.755-0.873), respectively, and in the test set were 0.870 (95%CI 0.769-0.971), 0.877 (95%CI 0.788-0.964), 0.859 (95%CI 0.755-0.961) and 0.773 (95%CI 0.647-0.898). The logistic model had the largest AUC in the test set. Conclusions:The machine learning model based on the venous phase enhanced CT radiomics features has high efficacy in predicting the VI of locally advanced gastric cancer before the operation, and the logistic model demonstrates the best diagnostic efficacy.

Acta Pharmaceutica Sinica B ; (6): 1708-1720, 2021.
Article in English | WPRIM | ID: wpr-888831


Stroke is considered a leading cause of mortality and neurological disability, which puts a huge burden on individuals and the community. To date, effective therapy for stroke has been limited by its complex pathological mechanisms. Autophagy refers to an intracellular degrading process with the involvement of lysosomes. Autophagy plays a critical role in maintaining the homeostasis and survival of cells by eliminating damaged or non-essential cellular constituents. Increasing evidence support that autophagy protects neuronal cells from ischemic injury. However, under certain circumstances, autophagy activation induces cell death and aggravates ischemic brain injury. Diverse naturally derived compounds have been found to modulate autophagy and exert neuroprotection against stroke. In the present work, we have reviewed recent advances in naturally derived compounds that regulate autophagy and discussed their potential application in stroke treatment.

Chinese Journal of Microbiology and Immunology ; (12): 572-578, 2016.
Article in Chinese | WPRIM | ID: wpr-672377


Objective To investigate any potential and independent demographic and serologic risk factors contributing to bone destruction in patients with rheumatoid arthritis ( RA) . Methods A total of 445 patients with RA were recruited in this study. Three autoantibodies including rheumatoid factor ( RF) , anti-cyclic citrullinated peptide antibody ( anti-CCP antibody) and anti-citrullinated alpha-enolase peptide 1 antibody ( anti-CEP-1 antibody) were quantified by using specific ELISA kits. The hand radiographs of all subjects were graded by using the modified Sharp/van der Heijde score ( Sharp score) . The potential and in-dependent risk factors were assessed by using univariate linear regression analyses and the stepwise multiple regression analysis, respectively. Results Based upon the univariate regression analyses, 7 covariates were identified as the potential risk factors for bone destruction in patients with RA, which were female (β=0. 100, P=0. 035), longer disease duration (β=0. 498, P=3. 26×10-29), RF (β=0. 096, P=0. 042), younger age at onset (β=-0. 312, P=1. 60 × 10-11 ), anti-CCP antibody positive (β=0. 202, P=1.74×10-5), anti-CEP-1 antibody positive (β=0.148, P=0.017) and positive for either anti-CCP or anti-CEP-1 antibodies (β=0. 157, P=1. 42×10-3). However, smoking (β=-0. 121, P=0. 018) were identi-fied as the potential protective factors. The multiple regression analysis indicated that the longer disease du-ration (P=2. 24×10-15) and anti-CCP antibody positive (P=0. 012) were independent risk factors for bone destruction. Conclusion Female, longer disease duration, younger age at onset, RF, anti-CCP and anti-CEP-1antibodies are potential risk factors for bone damage in patients with RA. Moreover, longer disease du-ration and anti-CCP antibody are two independent risk factors contributing to bone destruction in RA.