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| 1 | Downregulated microRNA-200a promotes EMT and tumor growth through thewnt/β-catenin pathway by targeting the E-cadherin repressors ZEB1/ZEB2 in gastricadenocarcinoma显示文摘 | Ningning Cong Ping Du Anling Zhang Fajuan Shen Juan Su Peiyu Pu Tao Wang Jie Zjang Chunsheng Kang Qingyu Zhang | 2013 | Oncology Reports2013，，4： | 1 |
| 2 | A NOVEL SIMULTANEOUS NOISE AND INPUT VSWR MATCHING TECHNIQUE FOR BROADBAND LNA显示文摘The Simultaneous Noise and Input Voltage Standing Wave Ratio (VSWR) Matching (SNIM) condition for Low Noise Amplifier (LNA), in principle, can only be satisfied at a single fre-quency. In this paper, by analyzing the fundamental limitations of the narrowband SNIM technique for the broadband application, the authors present a broadband SNIM LNA systematic design technique. The designed LNA guided by the proposed methodology achieves 10 dB power gain with a low Noise Figure of 0.53 dB. Meanwhile, it provides wonderful input matching of 27 dB across the fre-quency range of 3~5 GHz. Therefore, broadband SNIM is realized. | Nie Zhaohui Bao Jingfu Lin Ping Cai Fajuan | 2010 | Journal of Electronics(China)2010，27，4： | 1 |
| 3 | Renaming NAFLD to MAFLD:Advantages and Potential Changes in Diagnosis,Pathophysiology,Treatment,and Management显示文摘In recent years,with the increasing incidence of obesity and other metabolic diseases,the prevalence of non-alcoholic fatty liver disease(NAFLD)has increased and it has become a major health problem affecting more than one quarter of the world’s population.Recently,experts reached a consensus that NAFLD does not reflect the current knowledge,and metabolic dysfunctionassociated fatty liver disease(MAFLD)was suggested as a more appropriate term.MAFLD is not just a simple renaming of NAFLD.The definition of MAFLD allows a patient to have dual(or more)etiologies for their liver disease,which will help to exclude more heterogeneous patients.In this review,we introduce the significant differences between the definitions of NAFLD and MAFLD.In addition,we also describe the advantages of the term MAFLD in the pathophysiology,therapy,and patient management. | Fajuan Rui Hongli Yang Xinyu Hu Qi Xue Yayun Xu Junping Shi Jie Li | 2022 | Infectious Microbes & Diseases2022，4，2： | 0 |
| 4 | A K-nearest Neighbor Model to Predict Early Recurrence of Hepatocellular Carcinoma After Resection显示文摘Background and Aims:Patients with hepatocellular carci-noma(HCC)surgically resected are at risk of recurrence;however,the risk factors of recurrence remain poorly un-derstood.This study intended to establish a novel machine learning model based on clinical data for predicting early re-currence of HCC after resection.Methods:A total of 220 HCC patients who underwent resection were enrolled.Clas-sification machine learning models were developed to predict HCC recurrence.The standard deviation,recall,and preci-sion of the model were used to assess the model’s accura-cy and identify efficiency of the model.Results:Recurrent HCC developed in 89(40.45%)patients at a median time of 14 months from primary resection.In principal compo-nent analysis,tumor size,tumor grade differentiation,por-tal vein tumor thrombus,alpha-fetoprotein,protein induced by vitamin K absence or antagonist-II(PIVKA-II),aspartate aminotransferase,platelet count,white blood cell count,and HBsAg were positive prognostic factors of HCC recurrence and were included in the preoperative model.After compar-ing different machine learning methods,including logistic re-gression,decision tree,naïve Bayes,deep neural networks,and k-nearest neighbor(K-NN),we choose the K-NN model as the optimal prediction model.The accuracy,recall,preci-sion of the K-NN model were 70.6%,51.9%,70.1%,respec-tively.The standard deviation was 0.020.Conclusions:The K-NN classification algorithm model performed better than the other classification models.Estimation of the recurrence rate of early HCC can help to allocate treatment,eventually achieving safe oncological outcomes. | Chuanli Liu Hongli Yang Yuemin Feng Cuihong Liu Fajuan Rui Yuankui Cao Xinyu Hu Jiawen Xu Junqing Fan Qiang Zhu Jie Li | 2022 | Journal of Clinical and Translational Hepatology2022，10，4： | 0 |
| 5 | Comparison of patients with hepatitis B virus-associated hepatocellular carcinoma:Data from two hospitals from Turkey and China显示文摘Aims:There are many studies on the incidence of hepatitis B virus(HBV)-associated hepatocellular carcinoma(HCC),but very little is known about the HCC features in different populations.The study aimed to compare characteristics in two cohorts of patients with HBV-associated hepatocellular carcinoma from Turkey and China.Methods:Data on patients with HBV-associated HCC diagnosed by imaging or liver biopsy were retrospectively collected from Shandong Provincial Hospital(n=578)and Inonu University Hospital(n=359)between January 2002 and December 2020,and the liver function and HCC characteristics were compared.Continuous variables were compared using Student's t-test or Mann-Whitney U test and categorical variables were compared using the χ^(2) test or Fisher's exact test.Results:The patients in the Turkish cohort had significantly worse Child-Pugh scores(Child-Pugh A:38.3%vs.87.9%;Child-Pugh B:40.3%vs.11.1%;Child-Pugh C:21.4%vs.1.0%;p<0.001)and significantly higher levels of aspartate aminotransferase(66.5[38.0−126.0]vs.36.0[27.0-50.0]IU/L;p<0.001),alanine aminotransferase(47.5[30.0−87.3]vs.33.0[24.0−45.0]IU/L;p<0.001),total bilirubin(20.8[13.7−39.3]vs.17.9[13.8−24.0]mg/dL;p<0.001),and lower albumin levels(32.0[26.0-39.0]vs.40.0[36.1-43.8]g/L;p<0.001)than patients in Chinese cohort.The tumor characteristics showed the Barcelona Clinic Liver Cancer(BCLC)score(BCLC 1:5.1%vs.71.8%;BCLC 2:48.7%vs.24.4%;BCLC 3:24.4%vs.3.8%;BCLC 4:21.8%vs.0;all p<0.001),maximum tumor diameter(5.0[3.0-9.0]vs.3.5[2.5−6.0]cm;p<0.001),alpha-fetoprotein values(27.7 vs.13.2 ng/mL;p<0.001),and percentage of patients with portal vein tumor thrombus(33%vs.6.1%;p<0.001)were all significantly worse in the Turkish cohort compared with Chinese cohort.Conclusions:HBV-associated HCC from the Turkish cohort had worse liver function and more aggressive clinical characteristics than patients from the Chinese cohort. | Brian I.Carr Fajuan Rui Volkan Ince Sezai Yilmaz Xinya Zhao Yuemin Feng Jie Li | 2023 | Portal Hypertension & Cirrhosis2023，2，4： | 0 |
| 6 | Establishment and Evaluation of a Prediction Model of BLR for Severity in Coronavirus Disease 2019显示文摘Background:Coronavirus disease 2019(COVID-19)is an emerging infectious disease and has spread worldwide.Clinical risk factors associated with the severity in COVID-19 patients have not yet been well delineated.The aim of this study was to explore the risk factors related with the progression of severe COVID-19 and establish a prediction model for severity in COVID-19 patients.Methods:We retrospectively recruited patients with confirmed COVID-19 admitted in Enze Hospital,Taizhou Enze Medical Center(Group)and Nanjing Drum Tower Hospital between January 24 and March 12,2020.Take the Taizhou cohort as the training set and the Nanjing cohort as the validation set.Severe case was defined based on the World Health Organization Interim Guidance Report criteria for severe pneumonia.The patients were divided into severe and non-severe groups.Epidemiological,laboratory,clinical,and imaging data were recorded with data collection forms from the electronic medical record.The predictive model of severe COVID-19 was constructed,and the efficacy of the predictive model in predicting the risk of severe COVID-19 was analyzed by the receiver operating characteristic curve(ROC).Results:A total of 402 COVID-19 patients were included in the study,including 98 patients in the training set(Nanjing cohort)and 304 patients in the validation set(Nanjing cohort).There were 54 cases(13.43%)in severe group and 348 cases(86.57%)in nonsevere group.Logistic regression analysis showed that bodymassindex(BMI)and lymphocyte count wereindependent risk factors for severe COVID-19(all P<0.05).Logistic regression equation based on risk factors was established as follows:Logit(BL)=–5.552–5.473L+0.418BMI.The area under the ROC curve(AUC)of the training set and the validation set were 0.928 and 0.848,respectively(allP<0.001).The model was simplified to get a new model(BMI and lymphocyte count ratio,BLR)for predicting severe COVID-19 patients,and the AUC in the training set and validation set were 0.926 and 0.828,respectively(all P<0.001).Conclusions:Higher BMI and lower lymphocyte count are critical factors associated with severity of COVID-19 patients.The simplified BLR model has a good predictive value for the severe COVID-19 patients.Metabolic factors involved in the development of COVID-19 need to be further investigated. | Zebao He Fajuan Rui Hongli Yang Zhengming Ge Rui Huang Lingjun Ying Haihong Zhao Chao Wu Jie Li | 2022 | Infectious Diseases & Immunity2022，2，2： | 0 |
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