1.The expression of in the tissue of primary bronchogenic carcinoma and its sig nificance nificance//Jia Yanmin, Chen Mingwei, Ma Jianguang
Yanmin JIA ; Mingwei CHEN ; Jianguang MA
Journal of Xi'an Jiaotong University(Medical Sciences) 2000;21(5):428-230
ObjectiveTo détect in the tissue of primary bronchogenic carcinoma(PBC) and discuss its significance. Methods was assessed by immunohistochemical method ot SP in paraffin tissue sections of 40 PBC and paratumor normal lung tissue. Results The positive rate of TNF-a in cytoplasm of carcinoma cells was 47.5 % (19/40), and it was not found in all paratumor normal lung tissue(P< 0.05). ConclusionTNF-a gene expressed in some patients with broncho genic carcinoma. The growth of bronchogenic carcinoma is related to TNF-a gene.
2.A research about clinical effects of using different intervention methods to ameliorate the pains of neonatal infants
Yaping SHI ; Jiangqin LIU ; Jianguang WANG ; Zhenliang LIN ; Yushuang JIA
Chinese Journal of Practical Nursing 2006;0(19):-
Objective Compare the analgesia effects of using different intervention method among neonatal infants, and then find out the most effective method. Methods Divided 120 neonatal infants into the control group, the NNS group and the position group, there were 40 cases in every group. Using the N-PASS scale evaluated the pain degree at the points of 1 and 5 minutes respectively after stimulation among the 3 groups. Results There was significant difference between the 3 groups on the pain degree,P
3.Need-based design of medical mobile learning platform
Juanping WU ; Wenjie LI ; Xiaoyu WANG ; Wen ZHANG ; Jia XUE ; Peifeng HE ; Jianguang WU
Chinese Journal of Medical Library and Information Science 2014;(2):68-71
The medical mobile learning platform was constructed according to the information need of teachers and students in Shanxi Medical University, Changzhi Medical College, and Fenyang Medical College.The teaching and learning resources in Shanxi Medical University were integrated into the platform which could thus provide a variety of interactive learning ways for its users and users could rapidly find out their interested information resources. However, the platform construction needs the implementation of incentive measures, and regulations and rules for the protection of intellectual property rights.
4.Effects of laparoscope-assisted Ivor-Lewis surgery on perioperative stress, immune responses and intestinal barrier function in elderly patients with esophageal cancer
Bo XIE ; Jun QIAN ; Jing LI ; Jianguang JIA ; Zhixiang LI ; Chensong ZHANG
Chinese Journal of Geriatrics 2019;38(3):296-299
Objective To analyze the effects of laparoscope-assisted Ivor-Lewis surgery on perioperative stress,immune responses and intestinal barrier function in elderly patients with esophageal cancer.Methods A prospective study including 55 elderly esophageal cancer patients undergoing laparoscope-assisted Ivor Lewis surgery (the treatment group,n =25) and Ivor-Lewis surgery(the control group,n=25) was conducted.The white blood cell count,neutrophil-to-lymphocyte ratio,percentages of CD4 and CD8 cells,CD4/CD8 ratio,C reactive protein (CRP) and D-lactic acid levels were compared between the two groups before and at 1,4 and 7 d after operation.Results The white blood cell count(t =2.689,P =0.010) and neutrophil-to-lymphocyte ratio (t =3.300,P =0.002)were lower in the treatment group than in the control group at 1 d after operation.The percentage of CD4 cells was higher in the treatment group than in the control group at 1 d(t =2.242,P =0.029)and 4 d(t =2.031,P =0.047) after operation.The percentage of CD8 cells was higher in the treatment group than in the control group at 1 d after operation(t =2.041,P=0.046).The CD4/CD8 ratio was higher in the treatment group than in the control group at 1 d(t =2.833,P =0.007)and 4 d(t=2.111,P=0.036)after operation.The CRP level was lower in the treatment group than in the control group at 1 d(t=2.267,P=0.028)and 4 d(t =2.111,P =0.036)after operation.The D-lactic acid level was lower in the treatment group than in the control group at 1 d(t =2.267,P=0.028),4 d (t =7.967,P < 0.01) and 7 d (t =2.541,P =0.014) after operation.Conclusions Laparoscopeassisted Ivor-Lewis surgery has good protective effects on perioperative stress,immune responses and intestinal barrier function in elderly patients with esophageal cancer.
5.Efficacy of machine learning models versus Cox regression model for predicting prognosis of esophagogastric junction adenocarcinoma.
Kaiji GAO ; Yihao WANG ; Haikun CAO ; Jianguang JIA
Journal of Southern Medical University 2023;43(6):952-963
OBJECTIVE:
To compare the performance of machine learning models and traditional Cox regression model in predicting postoperative outcomes of patients with esophagogastric junction adenocarcinoma (AEG).
METHODS:
This study was conducted among 203 AEG patients with complete clinical and follow-up data, who were treated in our hospital between September, 2015 and October, 2020. The clinicopathological data of the patients were processed for analysis using R language package and divided into training and validation datasets at the ratio of 3:1. The Cox proportional hazards regression model and 4 machine learning models were constructed for analyzing the datasets. ROC curves, calibration curves and clinical decision curves (DCA) were plotted. Internal validation of the machine learning models was performed to assess their predictive efficacy. The predictive performance of each model was evaluated by calculating the area under the curve (AUC), and the model fitting was assessed using the calibration curve.
RESULTS:
For predicting 3-year survival based on the validation dataset, the AUC was 0.870 for Cox proportional hazard regression model, 0.901 for eXtreme Gradient Boosting (XGBoost), 0.791 for random forest, 0.832 for support vector machine, and 0.725 for multilayer perceptron; For predicting 5-year survival, the AUCs of these models were 0.915, 0.916, 0.758, 0.905, and 0.737, respectively. For internal validation, the AUCs of the 4 machine learning models decreased in the order of XGBoost (0.818), random forest (0.758), support vector machine (0.0.804), and multilayer perceptron (0.745).
CONCLUSION
The machine learning models show better predictive efficacy for survival outcomes of patients with AEG than Cox proportional hazard regression model, especially when proportional odds assumption or linear regression models are not applicable. XGBoost models have better performance than the other machine learning models, and the multi-layer perception model may have poor fitting results for a limited data volume.
Humans
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Adenocarcinoma
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Prognosis
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Machine Learning
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Esophagogastric Junction