Objective To summarize the experience in diagnosis and treatment of popliteal vein entrapment syndrome (PVES). Methods A total of 57 patients with PVES were selected from the Department of Vascular Surgery in Xinxiang Central Hospital from March 2009 to October 2015. Of which 43 patients were severe stenosis of popliteal vein (stenosis degree>90%), and another 14 cases were with stenosis less than 90%. All the patients underwent ascending venography of low limb to confirm the clinical classification after admission. Forty-three cases with severe stenosis of the popliteal vein were treated with releasing popliteal vein of entrapment and stripping varicose veins. The static pressure and dynamic pressure of popliteal vein and foot dorsal vein were measured before and after operation. Another 14 patients were treated with medical circulation driven sock and medical therapy. Results The degrees of popliteal vein stenosis were more than 75% in all patients. The patients were divided into bove-knee stenosis (n=9), knee stenosis (n=18), and below-knee stenosis (n=30) according to the different parts of stenosis. Forty-three patients treated with surgery showed relief of leg swelling and pain, and ulcer healing. And the imaging examination showed that there were no obvious compression and stenosis of popliteal vein, and vascular filling was well. The static pressure and dynamic pressure of the popliteal vein and dorsal vein were lower than those before surgery (P<0.05). The lower limb swelling and pain were relieved, and varicose veins of lower limbs were no longer continued to increase in 14 patients with conservative treatment. Conclusion PVES is easy to be misdiagnosed, which should be paid attention to, and satisfactory clinical results can be achieved by releasing popliteal vein of compression combined with stripping varicose veins in patients with serious symptoms .

OBJECTIVE: To explore the effects of c-Met-siRNA on the proliferation, movement and invasion of laryngeal carcinoma Hep-2 cells in vitro. METHOD: Firstly, the pSilencer 2.0/c-Met-shRNA recombinant plasmid was transfected into laryngeal carcinoma Hep-2 cells with transfecting agent of cationic liposome Lipofectamine 2000. Secondly,the transfection efficacy was tested by RT-PCR and Western-Blot, then the most inhibitive c-Met-siRNA sequence was elected. Cell proliferation, movement and invasion were detected with MTT, cell migration assay and cell invasion assay, respectively. RESULT: After the transfection of pSilencer 2.0/c-Met-shRNA recombinant plasmid into laryngeal carcinoma Hep-2 cells, the expression of mRNA and protein of c-Met decreased significantly in Hep-2 cells, and ability of the proliferation, movement and invasion of laryngeal carcinoma Hep-2 cells were also inhibited. CONCLUSION The results indicated that c-Met-siRNA can down-regulated the expression of c-Met and markedly inhibited laryngeal carcinoma Hep-2 cell proliferation, movement and invasion. It may have the potential as a therapeutic modality to treat human laryngeal carcinoma.
Apoptosis ; genetics ; Carcinoma, Squamous Cell ; genetics ; pathology ; Cell Line, Tumor ; Cell Proliferation ; Humans ; Laryngeal Neoplasms ; genetics ; pathology ; Liposomes ; Proto-Oncogene Proteins c-met ; genetics ; RNA, Messenger ; genetics ; RNA, Small Interfering ; genetics ; Transfection

Objective To construct risk prediction models of death or readmission in patients with acute heart failure(AHF)during the vulnerable phase based on machine learning algorithms and screen the optimal model.Methods A total of 651 AHF patients with admitted to Department of Cardiology of the Second Affiliated Hospital of Army Medical University from October 2019 to July 2021 were included.The clinical data consisting of admission vital signs,comorbidities and laboratory results were collected from electronic medical records.The composite endpoint was defined as all-cause death or readmission for worsening heart failure within 3 months after discharge.The patients were divided into a training set(521 patients)and a test set(130 patients)in a ratio of 8:2 through the simple random sampling.Six machine learning models were developed,including logistic regression(LR),random forest(RF),decision tree(DT),light gradient boosting machine(LGBM),extreme gradient boosting(XGBoost)and neural networks(NN).Receiver operating characteristic(ROC)curve and decision curve analysis(DCA)were used to evaluate the predictive performance and clinical benefit of the models.Shapley additive explanation(SHAP)was used to explain and evaluate the effect of different clinical characteristics on the models.Results A total of 651 AHF patients were included,of whom 203 patients(31.2%)died or were readmitted during the vulnerable phase.ROC curve analysis showed that the AUC values of the LR,RF,DT,LGBM,XGBoost and NN model were 0.707,0.756,0.616,0.677,0.768 and 0.681,respectively.The XGBoost model had the highest AUC value.DCA showed that the XGBoost model exhibited greater clinical net benefit compared with other models,with the best predictive performance.SHAP algorithm analysis showed that the clinical features that had the greatest impact on the output of the model were serum uric acid,D-dimer,mean arterial pressure,B-type natriuretic peptide,left atrial diameter,body mass index,and New York Heart Association(NYHA)classification.Conclusion The XGBoost model has the best predictive performance in predicting the risk of death or readmission of AHF patients during the vulnerable phase.