Objective To investigate the efficiency of chitosan hemostatic dressing combined radial artery hemostasis device in patients after transradial coronary intervention.Method Six hundred consecutive patients after transradial coronary intervention were selected and randomly divided into the experimental group and the control group according to the hospital number,there were 300 cases in each group.Single pneumatic tourniquet was used in the control group,while the chitosan hemostatic dressing combined with pneumatic tourniquet was used in the experimental group.Hemostasis consumption time,incidence of bleeding,ecchymosis and radial arterial occlusion were compared between two groups.Results There was no significant difference in the incidence of skin ecchymosis between two groups,P ＞ 0.05.The hemostasis consumption time and the incidence of bleeding,early radial arterial occlusion and chronic radial arterial occlusion were (127.6± 33.0) min,7.0％(21/300),5.7％(17/300),4.0％(12/300) in the experimental group and (181.6± 32.2) min,20.0％(60/300),11.7％(35/300),9.0％(27/300) in the control group,t or x2 values were 20.258,21.708,6.822,6.170 respectively,and there were significant differences,P＜0.01 or ＜0.05.Conclusion In patients after transradial coronary intervention,the application of chitosan hemostatic dressing combined with pneumatic tourniquet is safer and more effective than using pneumatic tourniquet alone.

OBJECTIVE:To establish a method for content and dissolution determination of berberine hydrochloride in Fengshi qutong capsule. METHODS:HPLC was conducted to determine the content of berberine hydrochloride in the preparation:the column was Waters Xbridge C18 with mobile phase of acetonitrile-0.05 mol/L sodium hydrogen(45:55,V/V)at a flow rate of 0.5 ml/min,de-tection wavelength was 345 nm,column temperature was 30 ℃,and the injection volume was 10 μl. Rotating basket method was used to determine the dissolution of berberine hydrochloride. RESULTS:The linear range of berberine hydrochloride was 1.06-74.2 μg/ml(r=0.9999);RSDs of precision, stability and reproducibility tests were lower than 1%;recovery was 96.8%-100.6%(RSD=1.6%,n=9). The dissolution of samples in 30 min was higher than 80%. CONCLUSIONS:The method is simple and stable with good reproducibility,and can be used for content and dissolution determination of berberine hydrochloride in Fengshi qutong capsule.

Objective To observe the damage degree and expression pattern of Caveolin-3 mRNA by ischemia-reperfusion injury in rabbits of skeletal muscle cell at different phases.Methods In this study,from April 2013 to December 2013,30 lower limbs of 15 Chinese White Rabbits were used and divided into two groups:all the left lower limbs were experimental group,which were made as an experimental model of ischemia-reperfusion injury by occluding left common iliac artery using noninvasive vascular.All the right lower limbs without surgical treatment were the control group.Gastrocnemius samples were obtained at 4h and 8h after reperfusion and handled by HE staining and observed by optical microscopy.By Real-time PCR,Caveolin-3/GAPDH mRNA were detected.Results HE stain showed:in control group,there was no edema,degeneration and inflammatory cell infiltration; in experi-meatal group,muscle cell degeneration had occured at ischemic 5 h.The edema was aggravated,a large number vacuole were formed and inflammatory cell were infiltrated at 4 h reperfusion.Reperfusion injury at 8h significantly reduced compared to 4 h.The Caveolin-3/GAPDH mRNA expression levels by SPSS 19.0 showed:Control group:1.026 ± 0.065,1.004 ±0.037,1.022 ±0.051,experimental group:1.159 ±0.073,1.445 ±0.053,1.208 ±0.058 at ischemic 5 h,4 h and 8 h reperfusion,respectively.On-line analysis of variance cases of ischemic 5 h and 4 h reperfusion and 8 h reperfusion,the experimental group than the control group were increased,with statistical significance (P ＜ 0.05).The experimental group of ischemic 5 h and 8 h reperfusion was no significant difference (P ＞ 0.05).It showed Caveolin-3 mRNA expression levels in ischemia-reperfusion 8 h group returned to normal.There was significant statistical difference between the ischemic 5 h and 4 h reperfusion (P ＜ 0.05).There was significant statistical difference between the 4 h reperfusion and 8 h reperfusion (P ＜ 0.05).Conclusion The expression of Caveolin-3 in experimental group showed a trend of first increased and then decreased.The expression levels of Caveolin-3 mRNA in skeletal muscle cells after ischemia-reperfusion injury is consistent with the development and progression of muscle cell damage.The results indicate that Caveolin-3 may play a control role in the injury and recovery of skeletal muscle cell.

Pulsed electromagnetic field (PEMF), a non-invasive physical treatment modality, is now used clinically to promote bone formation for osteoporosis. The patients after ectomy of ovarian cancer are easily complicated with osteoporosis. However, the safety parameters of PEMF treatment for the osteoporosis patients after resection of ovarian cancer remain unknown. Therefore, this study was designed to examine the effect of different frequency of PEMF on the proliferation, apoptosis and migration of human ovarian cancer cells (SKOV3 cells). Cultured SKOV3 cells were exposed to PEMF stimulation daily with radiation of 8 Hz, 16 Hz, 32 Hz and 64 Hz, respectively. We used sinusoidal waves with strength of 1 mT, twice a day with an interval of 12 hours. An exposure to the waves lasted 30 minutes, for 3 days, with those no PEMF stimulation serving as the control. The proliferation of cells was detected using EdU assay, and the apoptosis of cell was assessed with Annexin V-FITC fluorescence. The migration of cells was measured with the scratch wound assay. The data showed that exposure to PEMF of 1 mT, 8 Hz for 3 days could significantly inhibit the proliferation of SKOV3 cells and induce the apoptosis of the cells. The migrated distance and number were increased by 1 mT, 8 Hz or 32 Hz PEMF stimulation, but decreased by 1 mT, 16 Hz treatment. The results suggested that we should be careful about the safety of PEMF treatment and strictly choose the optical parameters in preventing or treating the osteoporosis of the patients after resection of ovarian cancer.
Apoptosis ; radiation effects ; Cell Line, Tumor ; Cell Movement ; radiation effects ; Cell Proliferation ; radiation effects ; Cystadenocarcinoma, Serous ; pathology ; Electromagnetic Fields ; adverse effects ; Female ; Humans ; Ovarian Neoplasms ; pathology

Ovarian cancer is the fifth leading cause of cancer-related death in women. 75% of ovarian cancer patients were detected at an advanced stage. At present,the disease lacks effective early screening method and the clinical therapy effect is poor,which has become a serious threat to women's health. The use of animal models of ovarian cancer is an important mean to elucidate the pathogenesis of the disease,and to screen effective diagnosis and treatment. The disease models are mainly divided into four types: spontaneous, induced, transplanted and gene intervention type. Mice, rats, hens,Mirotus Fortis and Mongolian gerbil are mainly selected to prepare animal models of ovarian cancer. Based on recent literature reports,we reviewed the preparation method of animal models of ovarian cancer and introduced the evaluation standards and main characteristics of these animal models.

Objective:To construct a prognosis associated micro RNA(miRNA) prediction model based on bioinformatics analysis and evaluate its application value in pancreatic cancer patients.Methods:The retrospective cohort study was conducted. The clinicopathological data of 171 pancreatic cancer patients from the Cancer Genome Atlas (TCGA) (https: //cancergenome.nih.gov/) between establishment of database and September 2017 were collected. There were 93 males and 78 females, aged from 35 to 88 years, with a median age of 65 years. Of the 171 patients, 64 had complete clinicopathological data. Patients were allocated into training dataset consisting of 123 patients and validation dataset consisting of 48 patients using the random sampling method, with a ratio of 7∶3. The training dataset was used to construct a prediction model, and the validation dataset was used to evaluate performance of the prediction model. Nine pairs of miRNA sequencing data (GSE41372) of pancreatic cancer and adjacent tissues were downloaded from Gene Expression Omnibus database. The candidate miRNAs were selected from differentially expressed miRNAs in pancreatic cancer and adjacent tissues for LASSO-COX regression analysis based on the patients of training dataset. A prognosis associated miRNA prediction model was constructed upon survival associated miRNAs which were selected from candidate differentially expressed miRNAs. The performance of prognosis associated miRNA prediction model was validated in training dataset and validation dataset, the accuracy of model was evaluated using the area under curve (AUC) of the receiver operating characteristic curves and the efficiency was evaluated using the consistency index (C-index). Observation indicarors: (1) survival of patients; (2) screening results of differentially expressed miRNAs; (3) construction of prognosis associated miRNA model; (4) validation of prognosis associated miRNA model; (5) comparison of clinicopathological factors in pancreatic cancer patients; (6) analysis of factors for prognosis of pancreatic cancer patients; (7) comparison of prediction performance between prognosis associated miRNA model and the eighth edition TNM staging. Measurement data with normal distribution were represented as Mean± SD, comparison between groups was analyzed by the student- t test, and comparison between multiple groups was analyzed by the AVONA. Measurement data with skewed data were represented as M (range), and comparison between groups was analyzed using the Mann-Whitney U test. Count data were described as absolute numbers or percentages, and comparison between groups was conducted using the chi-square test. Ordinal data were analyzed using the rank sum test. Correlation analysis was conducted based on count data to mine the correlation between prognosis associated miRNA model and clinicopathological factors. COX univariate analysis and multivariate analysis were applied to evaluate correlation with the results described as hazard ratio ( HR) and 95% confidence interval ( CI). HR<1 indicated the factor as a protective factor, HR>1 indicated the factor as a risk factor, and HR equal to 1 indicated no influence on survival. The Kaplan-Meier method was used to draw survival curve and calculate survival rates, and the Log-rank test was used for survival analysis. Results:(1) Survival of patients: 123 patients in the training dataset were followed up for 31-2 141 days, with a median follow-up time of 449 days. The 3- and 5-year survival rates were 16.67% and 8.06%. Forty-eight patients in the validation dataset were followed up for 41-2 182 days, with a median follow-up time of 457 days. The 3- and 5-year survival rates were 15.63% and 9.68%. There was no significant difference in the 3- or 5-year survival rates between the two groups ( χ2=0.017, 0.068, P>0.05). (2) Screening results of differentially expressed miRNAs. Results of bioinformatics analysis showed that 102 candidate differentially expressed miRNAs were selected, of which 63 were up-regulated in tumor tissues while 39 were down-regulated. (3) Construction of prognosis associated miRNA model: of the 102 candidate differentially expressed miRNAs, 5 survival associated miRNAs were selected, including miR-21, miR-125a-5p, miR-744, miR-374b, miR-664. The differential expression patterns of pancreatic cancer to adjacent tissues were up-regulation, up-regulation, down-regulation, up-regulation, and down-regulation, respectively, with the fold change of 4.00, 3.43, 3.85, 2.62, and 2.35. A prognostic expression equation constructed based on 5 survival associated miRNAs = 0.454×miR-21 expression level-0.492×miR-125a-5p expression level-0.49×miR-744 expression level-0.419×miR-374b expression level-0.036×miR-664 expression level. (4) Validation of prognosis associated miRNA model: The C-index of prognosis associated miRNA model was 0.643 and 0.642 for the training dataset and validation dataset, respectively. (5) Comparison of clinicopathological factors in pancreatic cancer patients: results of COX analysis showed that the prognosis associated miRNA model was highly related with pathological T stage and location of pancreatic cancer ( Z=45.481, χ2=10.176, P<0.05). (6) Analysis of factors for prognosis of pancreatic cancer patients: results of univariate analysis showed that pathological N stage, radiotherapy, molecular targeted therapy, score of prognosis associated miRNA model were related factors for prognosis pf pancreatic cancer patients ( HR=2.471, 0.290, 0.172, 2.001, 95% CI: 1.012-6.032, 0.101-0.833, 0.082-0.364, 1.371-2.922, P<0.05). Results of multivariate analysis showed that molecular targeted therapy was an independent protective factor for prognosis of pancreatic cancer patients ( HR=0.261, 95% CI: 0.116-0.588, P<0.05) and score of prognosis associated miRNA model≥1.16 was an independent risk factor for prognosis of pancreatic cancer patients ( HR=1.608, 95% CI: 1.091-2.369, P<0.05). (7) Comparison of prediction performance between prognosis associated miRNA model and the eighth edition TNM staging: in the training dataset, there was a significant difference in the prediction probability for 3- and 5-year survival of pancreatic cancer patients between prognosis associated miRNA model and the eighth edition TNM staging ( Z=-1.671, -1.867, P<0.05). The AUC of the prognosis associated miRNA model and the eight edition TNM staging for 3- and 5-year survival prediction was 0.797, 0.935 and 0.737 , 0.703, with the 95% CI of 0.622-0.972, 0.828-1.042 and 0.571-0.904 , 0.456-0.951. The C-index was 0.643 and 0.534. In the validation dataset, there was a significant difference in the prediction probability for 3- and 5-year survival of pancreatic cancer patients between prognosis associated miRNA model and the eighth edition TNM staging ( Z=-1.729, -1.923, P<0.05). The AUC of the prognosis associated miRNA model and the eight edition TNM staging was 0.750, 0.873 and 0.721 , 0.703, with the 95% CI of 0.553-0.948, 0.720-1.025 and 0.553-0.889, 0.456-0.950, respectively. The C-index was 0.642 and 0.544. Conclusions:A prognosis associated miRNA prediction model can be constructed based on 5 survival associated miRNAs in pancreatic cancer patients, as a complementation to current TNM staging and other clinicopathological parameters, which provides individual and accurate prediction of survival for reference in the clinical treatment.

Objective:To explore the isolation and culture methods of mouse parietal epithelial cells (PECs) of Bowman′s capsule, so as to provide a cell tool for further study.Methods:Mouse renal corpuscles were isolated by cell sieving combined with magnetic separation. After primary culture, identified parietal epithelial cells were induced to differentiate into podocytes. Immunofluorescence staining, real-time quantitative PCR and Western blotting were used to detect specific markers of parietal epithelial cells and podocytes.Results:Primary cultured PECs grew like paving stone and expressed Claudin-1 (PECs specific marker), CD133 (stem cell marker) and CD24 (stem cell marker), without the expression of tubular epithelial cell proteins, mesangial cell and podocyte specific proteins. Cultured to 6 generations in vitro, the PECs still expressed Claudin-1, CD133 and CD24. After incubated with differentiation medium, PECs were able to express podocyte markers WT-1 and Synaptopodin. Conclusion:The renal corpuscles are extracted by cell sieving combined with magnetic separation, and the mouse PECs successfully cultured in vitro can be induced to express podocytes′ markers.

The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis (ARN). The potential application of artificial intelligence (AI) algorithms in these areas of clinical research has not been reported previously. The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs. A total of 149 wide-angle fundus photographs from 40 eyes of 32 ARN patients were collected, and the U-Net method was used to construct the AI algorithm. Thereby, a novel algorithm based on deep machine learning in detection and evaluation of retinal necrosis was constructed for the first time. This algorithm had an area under the receiver operating curve of 0.92, with 86% sensitivity and 88% specificity in the detection of retinal necrosis. For the purpose of retinal necrosis evaluation, necrotic areas calculated by the AI algorithm were significantly positively correlated with viral load in aqueous humor samples (

Objective: Autoregressive integrated moving average (ARIMA) model was used to predict the incidence of tuberculosis in China from 2018 to 2019, providing references for the prevention and control of pulmonary tuberculosis. Methods: The monthly incidence data of tuberculosis in China were collected from January 2005 to December 2017. R 3.4.4 software was used to establish the ARIMA model, based on the monthly incidence data of tuberculosis from January 2005 to June 2017. Both predicted and actual data from July to December 2017 were compared to verify the effectiveness of this model, and the number of tuberculosis cases in 2018-2019 also predicted. Results: From 2005 to 2017, a total of 13 022 675 cases of tuberculosis were reported, the number of pulmonary tuberculosis patients in 2017 was 33.68% lower than that in 2005, and the seasonal character was obvious, with the incidence in winter and spring was higher than that in other seasons. According to the incidence data from 2005 to 2017, we established the model of ARIMA (0,1,2)(0,1,0)₁₂. The relative error between the predicted and actual values of July to December 2017 fitted by the model ranged from 1.67% to 6.80%, and the predicted number of patients in 2018 and 2019 were 789 509 and 760 165 respectively. Conclusion The ARIMA (0, 1, 2)(0, 1, 0)₁₂ model well predicted the incidence of tuberculosis, thus can be used for short-term prediction and dynamic analysis of tuberculosis in China, with good application value.