ABSTRACT
@#Objective To explore the application value of machine learning models in predicting postoperative survival of patients with thoracic squamous esophageal cancer. Methods The clinical data of 369 patients with thoracic esophageal squamous carcinoma who underwent radical esophageal cancer surgery at the Department of Thoracic Surgery of Northern Jiangsu People's Hospital from January 2014 to September 2015 were retrospectively analyzed. There were 279 (75.6%) males and 90 (24.4%) females aged 41-78 years. The patients were randomly divided into a training set (259 patients) and a test set (110 patients) with a ratio of 7 : 3. Variable screening was performed by selecting the best subset of features. Six machine learning models were constructed on this basis and validated in an independent test set. The performance of the models' predictions was evaluated by area under the curve (AUC), accuracy and logarithmic loss, and the fit of the models was reflected by calibration curves. The best model was selected as the final model. Risk stratification was performed using X-tile, and survival analysis was performed using the Kaplan-Meier method with log-rank test. Results The 5-year postoperative survival rate of the patients was 67.5%. All clinicopathological characteristics of patients between the two groups in the training and test sets were not statistically different (P>0.05). A total of seven variables, including hypertension, history of smoking, history of alcohol consumption, degree of tissue differentiation, pN stage, vascular invasion and nerve invasion, were included for modelling. The AUC values for each model in the independent test set were: decision tree (AUC=0.796), support vector machine (AUC=0.829), random forest (AUC=0.831), logistic regression (AUC=0.838), gradient boosting machine (AUC=0.846), and XGBoost (AUC=0.853). The XGBoost model was finally selected as the best model, and risk stratification was performed on the training and test sets. Patients in the training and test sets were divided into a low risk group, an intermediate risk group and a high risk group, respectively. In both data sets, the differences in surgical prognosis among three groups were statistically significant (P<0.001). Conclusion Machine learning models have high value in predicting postoperative prognosis of thoracic squamous esophageal cancer. The XGBoost model outperforms common machine learning methods in predicting 5-year survival of patients with thoracic squamous esophageal cancer, and it has high utility and reliability.
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AIM:Todiscusstherelationshipbetweensmallubiquitin-relatedmodifier4(SUMO4)expression and papillary thyroid carcinoma (PTC).METHODS:The mRNA and protein levels of SUMO4 in PTC and normal thyroid tissues were determined by the methods of real-time PCR, Western blot and immunohistochemistry .The relationship be-tween SUMO4 expression and clinicopathologic parameters in PTC was evaluated .RESULTS:The results showed that both the mRNA and protein levels of SUMO 4 expression in PTC were obviously higher than those in normal thyroid tissues ( P<0.01).CONCLUSION:The high level of SUMO4 expression in PTC suggests that SUMO4 plays an important role in PTC development and it is probably a molecular mechanism of PTC .
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Objective To study hyperbaric oxygen on left ventricular ejection fraction preserved by the influence of left ventricular remodeling in patients with heart failure. Methods A total of 110 patients with heart failure and normal ejection fraction were randomly allocated into the control group (n=55) and the HBO group (n=55). The control group were given the routine therapy, the HBO group were treated with hyperbaric oxygen on the basis of conventional drug. The application of color doppler ultrasound before and after treatment for 3 months left ventricular structure indicators. Results Left ventricular structure indicators were significantly decreased (LVDd、IVSD、LVPWD、LVMI)(P<0.01). Compared with the control group the difference was statistically significant (P<0.05). Follow-up of 3 months, The treatment group composite cardiovascular events was fewer than the control group and had significant difference (P<0.05). Conclusion Hyperbaric oxygen therapy can significantly improve left ventricular ejection fraction preserved by heart failure of left ventricular diastolic and systolic function and reverse left ventricular remodeling,And can reduce the happening of cardiovascular events.
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Objective To study the effects of hyperbaric oxygen on the diastolic heart failure in hypertensive patients. Methods 60 hypertensive patients with diastolic heart failure were randomly divided into treatment group and control group. Conventional therapies were given to patients of both groups. In addition, hyperbaric oxygen therapy was applied to those of treatment group. After 3 months, Doppler ultrasound recordings were obtained from all patients to determine the left ventricular diastolic function. Results The indexes of left ventricular diastolic function were im-proved after treatment in both groups(P < 0.01) ,and treatment group was better than control group(P < 0. 05). Conclusion For hypertensive patients with diastolic heart failure, hyperbaric oxygen can improve heart diastolic function.