Objective To observe the effectiveness of alprostedil in treatment of diabetic nephropathy and the mechanism of the renal protection of the drug. Methods One hundred patients with diabetic nephropathy due to diabetes mellitus were randomly divided into 2 groups ,50 in treatment group ,and 50 as controls, alprostadil was used intravenously in the Treatment Group for 12 day while routine management of diabetes such as food intake control,physical exercises and oral drugs or injection of insulin was conducted in both of the 2 groups. Urinary albumin were employed for evaluation as well as the side-effects. Results By the end of the trial, urinary albumin was decreased significantly compared with those before intravenous alprostadil treatment or with those in the control group (P<0.05). Conclusion Urinary albumin was decreased significantly after intravenous alprostadil. Therefore,it is a safe and effective drug in the treatment of diabetic nephropathy.

Objective To observe the changes of behavior,blood cortisol level,glucocorticoid receptors (Grs) and mineralocorticoid receptors (MRs) in hippocampus area after four weeks of unpredictable chronic mild stress,and to investigate the probable role of hypothalamus-pituitary-adrenal (HPA) axis in the pathogenesis of depression in aged people.Methods Aged male Wister rats were randomly assigned to control group and model group.The model group received unpredictable mild stress,including food and water deprivation,restrain,tail clipping,forced swimming,white noise,cage titling and cage rotating for 4 weeks,while the control group was undisturbed unless routine feeding and cage changing.After 4 weeks of procedure,the behavior changes were assessed by sucrose intake test,open-field test and state evaluation,serum cortisol level was measured by chemiluminescent assay,the qualitation and quantitation of GRs and MRs in hippocampus area were evaluated by immunohistochemistry and Western blotting respectively.All data were analyzed by using t-test.Results Body weight,the grooming score,activities in openfield test,food intake and sucrose intake were decreased in model group as compared with control group after 2 weeks of chronic mild stress (all P＜0.01),suggesting the stress induced depressive-like behavior effects on aged rats.Serum cortisol level was elevated in model group as compared with control group after 4 weeks of chronic mild stress (P＜0.01).A decrease of the neurons was found in CA3 of hippocampus,but not in DG area.In CA3 area,GR positive neurons were decreased,but no significant decrease was found in MR positive neurons.Conclusions The chronic mild stress leading to depressive-like behavior effects in aged rats induces overall HPA axis dysfunction,elevation of serum cortisol level,impairment of hippocampus neurons and decrease of GR positive neurons.The HPA axis dysfunction induced by chronic mild stress may play an important role in the pathogenesis of depression.

Objective To analyze biomechanical differences among quantity configurations of four common cannulated screws using three-dimensional finite element analysis.Methods The Dicom CT images of the right proximal femoral neck of a 65-year-old Chinese healthy volunteer were used to establish models of three kinds of different Pauwels types of femoral neck and four kinds of different quantity and configuration models (triangle,inverted triangle,rectangle,diamond) through three-dimensional virtual softwares.Models were imported into the finite element analysis software Ansys.After axial load application,the stress and displacement on the screws and femoral head were compared.The peak stresses on the screws,peak stresses on the femoral head break side and displacement peaks were observed.Results Four kinds of quantity and configuration models (triangle,inverted triangle,rectangle,rhombus) had different peak stress on the screws and femoral head break side and different displacement peak.Among Pauwels type Ⅰ fractures,the peak stresses on the screws were 46.382,32.159,43.985,and 24.342 MPa;the peak stresses on the femoral head break side were 5.840,7.440,3.731,and 6.311 MPa;the displacement peaks were 0.610,0.608,0.598,and 0.595 mm.Among Pauwels type Ⅱ fractures,the peak stresses on the screws were 46.763,39.979,49.619,and 25.692 MPa;the peak stresses on the femoral head break side were 4.971,7.332,3.161,and 5.734 MPa;the displacement peaks were 0.634,0.635,0.622,and 0.631 mm.Among Pauwels type Ⅲ fractures,the peak stresses on the screws were 51.432,39.477,51.515,and 26.949 MPa,and the peak stresses on the femoral head break side were 6.163,10.070,5.257,and 9.552 Mpa;the displacement peaks were 0.662,0.654,0.644,and 0.644 mm.Conclusion The fixation of femoral neck fractures with four screws in a diamond position can guide clinical application,for the procedure has better stress dispersion effects,ensure fracture stablility,provide effective sliding compression and anti-torsion and have biomechanical advantages.

Purpose: Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa. Methods: Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features. Results: Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics. Conclusions This study aimed to improve clinicians’ ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa.