1.Comparison of the Symptom Check List Test of the Guangzhou Medical Students with Three Regular Models of the Whole Country
Qian CHEN ; Jinhua CHEN ; Junyao HUANG ; Libing QIU
Chinese Journal of Medical Education Research 2006;0(11):-
Objective:To learn the mental health state of the Guangzhou medical students.Method:Symptom Check List 90(SCL-90) was selected to investigate 605 medical students in Guangzhou Medical College from the first year to the fifth year by stratified cluster sampling.The results were compared with the three regular models of the country.Conclusion:The mental health of the Guangzhou medical students is a little worse than that of the regular model of the adult in the country but is better than that of the regular models of the youth and the college students in the country.(except the body symptom)
2.A case report of extrarenal Wilms′ tumor
Xingning FU ; Yujie WANG ; Yan CUI ; Libing LIU ; Hongfang CHEN ; Daoxian QIU ; Gang FU ; Peijun ZONG
Chinese Journal of Urology 2020;41(6):472-473
Extrarenal Wilms′ tumor is extremely rare and has no characteristic clinical manifestations. Diagnosis is difficult before surgery, and is often confirmed by histopathology. Comprehensive treatment by surgery, chemotherapy and radiotherapy is currently adopted for such patients, and the overall survival rate can reach about 90%. Here we report a 2-year-old child with Wilms′ tumor in the left scrotum.
3.Recognition of fatigue status of pilots based on deep contractive auto-encoding network.
Shuang HAN ; Qi WU ; Libing SUN ; Xuyi QIU ; He REN ; Zhao LU
Journal of Biomedical Engineering 2018;35(3):443-451
We proposed a new deep learning model by analyzing electroencephalogram signals to reduce the complexity of feature extraction and improve the accuracy of recognition of fatigue status of pilots. For one thing, we applied wavelet packet transform to decompose electroencephalogram signals of pilots to extract the δ wave (0.4-3 Hz), θ wave (4-7 Hz), α wave (8-13 Hz) and β wave (14-30 Hz), and the combination of them was used as de-nosing electroencephalogram signals. For another, we proposed a deep contractive auto-encoding network-Softmax model for identifying pilots' fatigue status. Its recognition results were also compared with other models. The experimental results showed that the proposed deep learning model had a nice recognition, and the accuracy of recognition was up to 91.67%. Therefore, recognition of fatigue status of pilots based on deep contractive auto-encoding network is of great significance.
4. Efficacy of Hyper-CVAD/MA and CHALL-01 regimens in the treatment of Philadelphia chromosome-positive adult acute lymphoblastic leukemia patients under 60 years old
Aijie HUANG ; Libing WANG ; Juan DU ; Gusheng TANG ; Hui CHENG ; Shenglan GONG ; Lei GAO ; Huiying QIU ; Xiong NI ; Jie CHEN ; Li CHEN ; Weiping ZHANG ; Jianmin WANG ; Jianmin YANG ; Xiaoxia HU
Chinese Journal of Hematology 2019;40(8):625-632
Objective:
To compare the difference of efficacy between traditional Hyper-CVAD/MA regimen and the adolescents inspired chemotherapy regimen, CH ALL-01, in treatment of adult Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL) .
Methods:
In this study we retrospectively analyzed 158 Ph+ ALL patients receiving Hyper-CVAD/MA regimen (