Objective:To analyze the association between osteoporosis and the risk for Alzheimer's disease (AD).Methods:A total of 471 922 study subjects were selected from the UK Biobank database, including 12 818 osteoporosis cases and 459 104 controls. Cox proportional hazard regression model and competing risk model were used to evaluate the association between osteoporosis and AD after adjusting confounding factors. Furthermore, a Mendelian randomization (MR) study was conducted by using the data of two published genome-wide association studies, and 1 050 highly relevant single nucleotide polymorphisms were identified from the bone mineral density data as instrumental variables. The association between bone mineral density and the risk for AD was evaluated by using inverse variance weighted method, MR-Egger regression, and weighted median estimator method. Additionally, sensitivity analyses were performed.Results:After adjusting for confounders, no significant association between osteoporosis and an increased risk for AD was found in the cohort study (Cox proportional hazard regression model analysis: HR=1.10, 95% CI: 0.78-1.56, P=0.588). The MR analysis revealed no association between bone mineral density and the risk for AD (inverse-variance weighted: OR=1.03, 95% CI: 0.98-1.09, P=0.252), and the results remained robust in multiple sensitivity analyses. Conclusion:The study result does not support the association between osteoporosis and risk for AD.

Objective:To analyze the association between osteoporosis and the risk for Alzheimer's disease (AD).Methods:A total of 471 922 study subjects were selected from the UK Biobank database, including 12 818 osteoporosis cases and 459 104 controls. Cox proportional hazard regression model and competing risk model were used to evaluate the association between osteoporosis and AD after adjusting confounding factors. Furthermore, a Mendelian randomization (MR) study was conducted by using the data of two published genome-wide association studies, and 1 050 highly relevant single nucleotide polymorphisms were identified from the bone mineral density data as instrumental variables. The association between bone mineral density and the risk for AD was evaluated by using inverse variance weighted method, MR-Egger regression, and weighted median estimator method. Additionally, sensitivity analyses were performed.Results:After adjusting for confounders, no significant association between osteoporosis and an increased risk for AD was found in the cohort study (Cox proportional hazard regression model analysis: HR=1.10, 95% CI: 0.78-1.56, P=0.588). The MR analysis revealed no association between bone mineral density and the risk for AD (inverse-variance weighted: OR=1.03, 95% CI: 0.98-1.09, P=0.252), and the results remained robust in multiple sensitivity analyses. Conclusion:The study result does not support the association between osteoporosis and risk for AD.

OBJECTIVE: To investigate the effect of environmental estrogen bisphenol A (BPA) exposure on apoptosis of mouse ovarian preantral follicular granulosa cells and ovarian development and explore the underlying mechanism. METHODS: Mouse ovarian preantral follicular granulosa cells were isolated from female ICR mice at postnatal day (PND) 10 and cultured RESULTS: Compared with the control cells group, the isolated cells exposed to a low concentration of BPA (50 μmol/L) showed a significantly lowered apoptosis rate, increased mitochondrial membrane potential, and enhanced cellular proliferation ( CONCLUSIONS BPA can concentration-dependently regulate the function of ovarian preantral follicular granulosa cells in mice and potentially affects both the pregnant mice and the offspring female mice in light of early ovarian development.
Animals ; Apoptosis ; Benzhydryl Compounds ; Female ; Granulosa Cells ; Mice ; Mice, Inbred ICR ; Ovarian Follicle ; Phenols ; Pregnancy

Objective:To explore the feasibility of using faster regional convolutional neural network (Faster R-CNN) to evaluate the status of circumferential resection margin (CRM) of rectal cancer in the magnetic resonance imaging (MRI).Methods:This study was registered in the Chinese Clinical Trial Registry (ChiCTR-1800017410). Case inclusion criteria: (1) the positive area of CRM was located between the plane of the levator ani, anal canal and peritoneal reflection; (2) rectal malignancy was confirmed by electronic colonoscopy and histopathological examination; (3) positive CRM was confirmed by postoperative pathology or preoperative high-resolution MRI. Exclusion criteria: patients after neoadjuvant therapy, recurrent cancer after surgery, poor quality images, giant tumor with extensive necrosis and tissue degeneration, and rectal tissue construction changes in previous pelvic surgery. According to the above criteria, MRI plain scan images of 350 patients with rectal cancer and positive CRM in The Affiliated Hospital of Qingdao University from July 2016 to June 2019 were collected. The patients were classified by gender and tumor position, and randomly assigned to the training group (300 cases) and the validation group (50 cases) at a ratio of 6:1 by computer random number method. The CRM positive region was identified on the T2WI image using the LabelImg software. The identified training group images were used to iteratively train and optimize parameters of the Faster R-CNN model until the network converged to obtain the best deep learning model. The test set data were used to evaluate the recognition performance of the artificial intelligence platform. The selected indicators included accuracy, sensitivity, positive predictive value, receiver operating characteristic (ROC) curves, areas under the ROC curves (AUC), and the time taken to identify a single image.Results:The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CRM status determined by the trained Faster R-CNN artificial intelligence approach were 0.884, 0.857, 0.898, 0.807, and 0.926, respectively; the AUC was 0.934 (95% CI: 91.3% to 95.4%). The Faster R-CNN model's automatic recognition time for a single image was 0.2 s.Conclusion:The artificial intelligence model based on Faster R-CNN for the identification and segmentation of CRM-positive MRI images of rectal cancer is established, which can complete the risk assessment of CRM-positive areas caused by in-situ tumor invasion and has the application value of preliminary screening.

Objective:To explore the feasibility of using faster regional convolutional neural network (Faster R-CNN) to evaluate the status of circumferential resection margin (CRM) of rectal cancer in the magnetic resonance imaging (MRI).Methods:This study was registered in the Chinese Clinical Trial Registry (ChiCTR-1800017410). Case inclusion criteria: (1) the positive area of CRM was located between the plane of the levator ani, anal canal and peritoneal reflection; (2) rectal malignancy was confirmed by electronic colonoscopy and histopathological examination; (3) positive CRM was confirmed by postoperative pathology or preoperative high-resolution MRI. Exclusion criteria: patients after neoadjuvant therapy, recurrent cancer after surgery, poor quality images, giant tumor with extensive necrosis and tissue degeneration, and rectal tissue construction changes in previous pelvic surgery. According to the above criteria, MRI plain scan images of 350 patients with rectal cancer and positive CRM in The Affiliated Hospital of Qingdao University from July 2016 to June 2019 were collected. The patients were classified by gender and tumor position, and randomly assigned to the training group (300 cases) and the validation group (50 cases) at a ratio of 6:1 by computer random number method. The CRM positive region was identified on the T2WI image using the LabelImg software. The identified training group images were used to iteratively train and optimize parameters of the Faster R-CNN model until the network converged to obtain the best deep learning model. The test set data were used to evaluate the recognition performance of the artificial intelligence platform. The selected indicators included accuracy, sensitivity, positive predictive value, receiver operating characteristic (ROC) curves, areas under the ROC curves (AUC), and the time taken to identify a single image.Results:The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CRM status determined by the trained Faster R-CNN artificial intelligence approach were 0.884, 0.857, 0.898, 0.807, and 0.926, respectively; the AUC was 0.934 (95% CI: 91.3% to 95.4%). The Faster R-CNN model's automatic recognition time for a single image was 0.2 s.Conclusion:The artificial intelligence model based on Faster R-CNN for the identification and segmentation of CRM-positive MRI images of rectal cancer is established, which can complete the risk assessment of CRM-positive areas caused by in-situ tumor invasion and has the application value of preliminary screening.

Objective: To explore the clinical effect of percutaneous low frequency electric stimulation on prevention of venous indwelling needle thrombotic blockage in patients with coronary heart disease. Methods: A total of 150 patients admitted to the department of cardiovascular medicine from May 2017 to January 2018 who met the inclusion and exclusion criteria were selected. It was divided into the intervention group and the control group by the random number table method, including 75 cases in the intervention group and 75 cases in the control group. In the control group, the routine nursing method was adopted for the maintenance of intravenous indwelling needle. In the intervention group, on the basis of routine nursing, the percutaneous low frequency electrical stimulation was added. The incidence of thrombosis, occlusion, mean retention time, patient satisfaction and intravenous infusion complications were compared between the two groups. Results: The incidence of thrombosis was 40.0% (30/75) and64.0% (48/75) in the intervention group and the control group, and the blockage rate was 28.0% (21/75) and 52.0% (39/75) in the intervention group and the control group, respectively. There were significant differences between the two groups (χ²=8.654, 9.000, all P< 0.01). The incidence of phlebitis, drug exosmosis and subcutaneous bruising were 13.33% (10/75), 13.33% (10/75) and 18.67% (14/75) in the intervention group, 33.33% (25/75), 40.00% (30/75) and 41.33% (31/75) in the control group, respectively. There were significant differences between the two groups (χ² = 8.385, 13.636, 9.175, all P< 0.01). The indwelling time and patient satisfaction of the intervention group were (2.817±0.221) d and 82.67% (62/75) respectively, while those of the control group were (2.291±0.127) d and 50.67% (38/75), respectively. There were significant differences between the two groups (t=17.926, χ²=17.280, all P<0.01). Conclusions Percutaneous low frequency electrical stimulation has positive significance for improving the formation of thrombus at the point of intravenous indwelling needle and preventing the blockage of catheter caused by thrombosis.

Objective To evaluate the therapeutic effects of home made Nitrol stents in the teatment of malignant or benign tracheobronchial stenosis.Methods Thirteen patients with malignant or benign tracheobronchial stenosis were treated by Nitrol stents. The stenosed sites located in trachea in 5, tracheobronchi in 6, main bronchus in 2. All cases were malignant except one was benign. Results 15 stents were successfully placed the expected position with dyspnea rapidly improved. The average survival time was 11.4 months. Conclusions It is an effective way to place Nitrol stent in treatment of tracheobronchial stenosis under x ray guidance with fibertracheobronchoscopy.