Objective To investigate the expression of hepatocyte growth factor activator inhibitor- 1 (HAl-1) gene in the tissues of renal cell carcinoma (RCC) and the correlation with tumor angiogenesis.Methods The expression of HAI-1 gene was detected by semi-quantitative RT-PCR in the tissues of 56 cases of RCC,corresponding adjacent non-cancer tissues and 12 nomad renal tissues,the expression of CD34was detected by immunohistochemistry SP method in the above-mentioned organizations,and counted the microvessel density (MVD),analyzed the relationship between the expression of HAI-1 gene in RCC and clinicopathological parameters,MVD.Results The positive expression rate of HAI-1 gene in RCC tissues,the adjacent non-cancer tissues and normal renal tissues were 58.93％(33/56),76.79％(43/56) and 91.67％(11/12),respectively.The expression of HAI-1 gene was a negative correlation to the Fuhrman histological grade,Robson clinical stage (r ＝ -0.617,-0.432,P ＜ 0.01 or ＜ 0.05 ).The positive expression rate in lymph node metastasis was significantly lower than that in without lymph node metastasis [ 35.29％(6/17) vs.69.23％(27/39),P＜ 0.05 ].The MVD in RCC tissues was significantly higher than that in the adjacent noncancer tissues and normal renal tissues(P＜ 0.05).The expression of HAI-I gene in RCC tissues was negative correlated to the MVD (r ＝ -0.516,P ＜ 0.01 ).Conclusions There is a certain relationship between the missing expression of HAI-1 gene in RCC and the development,invasion and metastasis of RCC.The HAI-1 gene deletion can be used as an important molecular marker in the invasion and metastasis of RCC.

OBJECTIVETo develop an HILIC-HPLC method for simultaneous analysis of three main active triterpenoid saponins including dipsacus asponin VI, dipsacus asponin X and dipsacus asponin XII. To evaluate the medical material from various habitats in China, different commercial grades or parts of plant.

METHODThe HPLC was conducted on Venusil HILIC column (4.6 mm x 250 mm, 5 microm) with acetonitrile and water as the mobile phase at 25 degrees C, which was in gradient elution. The flow rate was 1 mL x min(-1); and the detection wavelength was 203 nm.

RESULTThe contents of dipsacus asponin VI, X and XII were 0.77%-14.31%, 0.39%-3.19% and 0.41%-1.49% respectively in different provinces of China, that were related to locations. The contents of saponins from Yunnan and Sichuan provinces were higer than those of Hubei and Guizhou. Thick roots, which were high-class products, contained less saponins than thin ones. In addition, the contents of stem, residual parts of stem and fibrous roots were fewer than main roots.

CONCLUSIONThis method is simple, sensitive and accurate. It could be used to determine the contents of dipsacus asponin VI, X, XII and evaluate the quality of dipsacus asperoides.

China ; Chromatography, High Pressure Liquid ; methods ; Chromatography, Liquid ; methods ; Dipsacaceae ; chemistry ; Drugs, Chinese Herbal ; chemistry ; isolation & purification ; Hydrophobic and Hydrophilic Interactions ; Plant Roots ; chemistry ; Saponins ; chemistry ; isolation & purification

Objective To compare the clinical efficacy and safety of wide-focus high energy lithotriptor with narrow-focus low energy lithotriptor treating renal calculi. Methods A prospective study was conducted to compare both modalities for the management of renal calculi.Stone formers were randomly enrolled into two groups. Group A was managed with wide-focus high energy lithotriptor,while group B was managed with the other. Urine samples were collected to detect neutrophil gelatinase-associated lipocalin(NGAL)and α1-microglobulin(α1-MG)levels before and after SWL. Results 60 were randomized to the group A and 60 to group B. There was no significant difference between two groups in stone free rate and complication rate.For stones more than 10 mm,re-treat rate(11.1%vs 39.1%,P=0.021)was lower and complication rate(25.9%vs 4.3%,P=0.038)was higher in group A. There had a larger increase of NGAL in group A(P < 0.001)after SWL. Conclusion SWL with both lithotriptors are effective and safe for renal stones.Wide-focus high energy lithotripsy was associated with lower re-treat rate and higher complication rate for stones more than 10 mm.NGAL may play a potential role in the evalua-tion of SWL induced early renal injury.

Objective:To explore the effect of repetitive transcranial magnetic stimulation (rTMS) combined with cognitive behavioral therapies (CBT) on the cognitive function and alcohol craving in patients with alcohol dependence.Methods:From March 2019 to September 2021, a total of 150 patients with alcohol dependence were enrolled and randomly divided into rTMS treatment group (rTMS+ sham CBT, n=41), CBT treatment group (CBT+ sham rTMS, n=34), rTMS+ CBT treatment group( n=36) and control group (sham rTMS+ sham CBT, n=39). At baseline (before treatment), 2nd week, 8th week, 12th week and 24th week, alcohol dependence scale (ADS) was used to evaluate the degree of alcohol dependence, the obsessive compulsive drinking scale (OCDS) was used to assess patients' drinking craving, and Montreal cognitive assessment scale (MoCA) was used to assess the overall cognitive level of patients.SPSS 23.0 statistical software was used to compare the differences of ADS, OCDS and MoCA scale scores of the four groups by repeated measure ANOVA and simple effect analysis. Results:(1)The patients in the four groups were evaluated with ADS scale at baseline, 12th week and 24th week respectively.The interaction of group×time( F=1.279, P=0.279) and the main effect of group were not significant ( F=0.882, P=0.454), and the main effect of time was significant ( F=12.925, P<0.001) .Further simple effect analysis showed that the ADS score of rTMS+ CBT group was lower than that of baseline(14.48±5.70, 10.00±6.51) ( P=0.01) at 24th week.(2)Patients in the four groups were assessed with OCDs scale at baseline, 2nd week, 8th week, 12th week and 24th week, and the interaction of group×time was significant ( F=2.015, P=0.042). Further simple effect analysis showed that the OCDs scores of rTMS group and rTMS+ CBT group at each follow-up time node were lower than those at baseline period (all P<0.05). (3)Patients in the four groups were assessed with MoCA scale at baseline, 8th week, 12th week and 24th week, and the interaction of group×time was not significant ( F=1.660, P=0.106), and the main effect of group and the main effect of time were significant ( F=2.964, P=0.038; F=14.239, P<0.001). Further simple effect analysis showed that the score of MoCA scale in CBT group at the 24th week was higher than that at baseline (21.73±5.81, 24.60±3.98)( P=0.029), the score of MoCA scale in rTMS+ CBT group at the 24th week was higher than that at the 8th week (23.50±6.01, 25.95±2.87) ( P=0.006), and the score of MoCA scale in rTMS group at the 12th week was higher than that in control group (22.08±6.64, 26.64±2.46)( P=0.009). Conclusion:rTMS combined with CBT can be effective in improving alcohol craving and cognitive function in patients with alcohol dependence, and has a good long-term effect.

Objective: To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes. Methods: In this retrospective cohort study, we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions. Classification of all-cause, 30-day readmission outcomes were modeled using logistic regression, artificial neural network, and EasyEnsemble. F1 statistic, sensitivity, and positive predictive value were used to evaluate the model performance. Results: We identified 14 most influential data features (4 numeric features and 10 categorical features) and evaluated 3 machine learning models with numerous sampling methods (oversampling, undersampling, and hybrid techniques). The deep learning model offered no improvement over traditional models (logistic regression and EasyEnsemble) for predicting readmission, whereas the other two algorithms led to much smaller differences between the training and testing datasets. Conclusions: Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes. But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.