Objective To investigate the diagnosis and treatment strategy of urosepsis caused by ureteral calculi in solitary kidney. Methods The clinical data of patients with urosepsis caused by ureteral calculi in solitary kidney in the Department of Urology of Chengdu 363 Hospital Affiliated to Southwest Medical University from March 2015 to March 2020 were analyzed retrospectively. Results A total of 23 patients were included. One patient received ureteroscopic holmium laser lithotripsy, after which urosepsis and renal function deteriorated, then got better after anti-infection and hemodialysis treatment in intensive care unit; 17 patients received implantation of ureteral stent by cystoscopy, and 5 patients received percutaneous nephrostomy by ultrasound guiding, the 22 patients received ureteroscopic lithotripsy or flexible ureteroscopic lithotripsy electively. One patients had subcapsular renal hematoma postoperatively and worse renal insufficiency, the rest 22 patients had improved renal function. All patients were cured clinically. Conclusions For solitary kidney patients who have urosepsis caused by ureteral calculi, emergency treatment is necessary. The relief of urinary obstruction must be based on effective anti-infection. Choosing cystoscopic ureteral stent implantation or percutaneous nephrostomy depends on patients’ individualization. Ureterscopic lithotripsy simultaneously is not recommended. Ureteral intubation before cystoscopic ureteral stent implantation is important, which can increase the success rate of ureteral stent implantation.
目的 研究CD14过表达对胃癌细胞核转录因子-κB（NF-κB）活性以及人β防御素-2（hBD-2）表达的影响，探讨CD14在胃癌发生发展中的作用。 方法 体外培养CD14稳定转染的胃癌SGC-7901细胞系及空质粒转染的对照细胞，CD14蛋白受体胞壁酰二肽刺激细胞，凝胶迁移实验检测NF-κB的活性，蛋白质印迹法检测NF-κB蛋白的表达，同时分别用逆转录-聚合酶链反应以及蛋白质印迹法检测hBD-2 mRNA及蛋白的表达。 结果 与对照组相比，CD14过表达的细胞中NF-κB的活性明显增强，蛋白表达量也大幅度增加，同时hBD-2的mRNA及蛋白的表达都有所提高。 结论 胃癌细胞中CD14在介导NF-κB的激活以及hBD-2的表达中发挥重要作用。
Objective To develop a Matlab toolbox to improve the efficiency of musculoskeletal kinematics analysis while ensuring the consistency of musculoskeletal kinematics analysis process and results. Methods Adopted the design concept of “Batch processing tedious operation”, based on the Matlab connection OpenSim interface function ensures the consistency of musculoskeletal kinematics analysis process and results, the functional programming was applied to package the five steps for scale, inverse kinematics analysis, residual reduction algorithm, static optimization analysis, and joint reaction analysis of musculoskeletal kinematics analysis as functional functions, and command programming was applied to analyze musculoskeletal movements in large numbers of patients. A toolbox called LLMKA (Lower Limbs Musculoskeletal Kinematics Analysis) was developed. Taking 120 patients with medial knee osteoarthritis as the research object, a clinical researcher was selected using the LLMKA toolbox and OpenSim to test whether the analysis process and results were consistent between the two methods. The researcher used the LLMKA toolbox again to conduct musculoskeletal kinematics analysis in 120 patients to verify whether the use of this toolbox could improve the efficiency of musculoskeletal kinematics analysis compared with using OpenSim. Results Using the LLMKA toolbox could analyze musculoskeletal kinematics analysis in a large number of patients, and the analysis process and results were consistent with the use of OpenSim. Compared to using OpenSim, musculoskeletal kinematics analysis was completed in 120 patients using the LLMKA toolbox with only 2 operations were needed to enter the patient body mass data, operating steps decreased by 99.19%, total analysis time by 66.84%, and manual participation time by 99.72%, just need 0.079 1 hour (4 minutes and 45 seconds). Conclusion The LLMKA toolbox can complete a large number of musculoskeletal kinematics analysis in patients with one click in a way that is consistent in process and results with using OpenSim, reducing the total time of musculoskeletal kinematics analysis, and liberating clinical researchers from cumbersome steps, making more energy into the clinical significance of musculoskeletal kinematics analysis results.
Objective To develop an automatic diagnostic tool based on deep learning for lumbar spine stability and validate diagnostic accuracy. Methods Preoperative lumbar hyper-flexion and hyper-extension X-ray films were collected from 153 patients with lumbar disease. The following 5 key points were marked by 3 orthopedic surgeons: L4 posteroinferior, anterior inferior angles as well as L5 posterosuperior, anterior superior, and posterior inferior angles. The labeling results of each surgeon were preserved independently, and a total of three sets of labeling results were obtained. A total of 306 lumbar X-ray films were randomly divided into training (n=156), validation (n=50), and test (n=100) sets in a ratio of 3∶1∶2. A new neural network architecture, Swin-PGNet was proposed, which was trained using annotated radiograph images to automatically locate the lumbar vertebral key points and calculate L4, 5 intervertebral Cobb angle and L4 lumbar sliding distance through the predicted key points. The mean error and intra-class correlation coefficient (ICC) were used as an evaluation index, to compare the differences between surgeons’ annotations and Swin-PGNet on the three tasks (key point positioning, Cobb angle measurement, and lumbar sliding distance measurement). Meanwhile, the change of Cobb angle more than 11° was taken as the criterion of lumbar instability, and the lumbar sliding distance more than 3 mm was taken as the criterion of lumbar spondylolisthesis. The accuracy of surgeon annotation and Swin-PGNet in judging lumbar instability was compared. Results ① Key point: The mean error of key point location by Swin-PGNet was (1.407±0.939) mm, and by different surgeons was (3.034±2.612) mm. ② Cobb angle: The mean error of Swin-PGNet was (2.062±1.352)° and the mean error of surgeons was (3.580±2.338)°. There was no significant difference between Swin-PGNet and surgeons (P>0.05), but there was a significant difference between different surgeons (P<0.05). ③ Lumbar sliding distance: The mean error of Swin-PGNet was (1.656±0.878) mm and the mean error of surgeons was (1.884±1.612) mm. There was no significant difference between Swin-PGNet and surgeons and between different surgeons (P>0.05). The accuracy of lumbar instability diagnosed by surgeons and Swin-PGNet was 75.3% and 84.0%, respectively. The accuracy of lumbar spondylolisthesis diagnosed by surgeons and Swin-PGNet was 70.7% and 71.3%, respectively. There was no significant difference between Swin-PGNet and surgeons, as well as between different surgeons (P>0.05). ④ Consistency of lumbar stability diagnosis: The ICC of Cobb angle among different surgeons was 0.913 [95%CI (0.898, 0.934)] (P<0.05), and the ICC of lumbar sliding distance was 0.741 [95%CI (0.729, 0.796)] (P<0.05). The result showed that the annotating of the three surgeons were consistent. The ICC of Cobb angle between Swin-PGNet and surgeons was 0.922 [95%CI (0.891, 0.938)] (P<0.05), and the ICC of lumbar sliding distance was 0.748 [95%CI(0.726, 0.783)] (P<0.05). The result showed that the annotating of Swin-PGNet were consistent with those of surgeons. ConclusionThe automatic diagnostic tool for lumbar instability constructed based on deep learning can realize the automatic identification of lumbar instability and spondylolisthesis accurately and conveniently, which can effectively assist clinical diagnosis.
ObjectiveTo investigate the mechanism of Semaphorin 3A (Sema3A) in fracture healing after nerve injury by observing the expression of Sema3A in the tibia fracture healing after traumatic brain injury (TBI). MethodsA total of 192 Wistar female rats, 8-10 weeks old and weighing 220-250 g, were randomly divided into tibia fracture group (group A, n=48), TBI group (group B, n=48), TBI with tibia fracture group (group C, n=48), and control group (group D, n=48). The tibia fracture model was established at the right side of group A; TBI model was made in group B by the improved Feeney method; the TBI and tibia fracture model was made in group C; no treatment was given in group D. The tissue samples were respectively collected at 3, 5, 7, 14, 21, and 28 days after operation; HE staining, immunohistochemistry staining, and Western blot method were used for the location and quantitative detection of Sema3A in callus tissue. ResultsHE staining showed that no obvious changes were observed at each time point in groups B and D. At 3 and 5 days, there was no obvious callus growth at fracture site with inflammatory cells and fibrous tissue filling in groups A and C. At 7 and 14 days, fibrous tissue grew from periosteum to fracture site in groups A and C; the proliferation of chondrocytes in exterior periosteum gradually formed osteoid callus at fracture site in groups A and C. The chondrocyte had bigger size, looser arrangement, and more osteoid in group C than group A. Group B had disorder periosteum, slight subperiosteal bone hyperplasia, and no obvious change of bone trabecula in group B when compared with group D. At 21 and 28 days, cartilage callus was gradually replaced by new bone trabecula in groups A and C. Group C had loose arrange, disorder structure, and low density of bone trabecula, big callus area and few chondrocyte and osteoid when compared with group A; group B was similar to Group D. Immunohistochemistry staining showed that Sema3A expression in chondrocytes in group C was higher than that in group A, particularly at 7, 14, and 21 day. Sema3A was significantly higher in osteoblasts of new bone trabecula in group A than group C, especially at 14 and 21 days (P<0.05). Western blot results showed that the Sema3A had the same expression trend during fracture healing in groups A and C. However, the expression of Sema3A protein was significantly higher in group C than group A (P<0.05) and in group B than group D (P<0.05) at 7, 14, 21, and 28 days. ConclusionAbnormal expression of Sema3A may play a role in fracture healing after nerve injury by promoting the chondrocytes proliferation and reducing the distribution of sensory nerve fibers and osteoblast differentiation.