A simple assay for detection of phospholipase C (PLC) activity was developed based on a fluorescence liposome probe using the Liss Rhod PE-loaded phospholipid liposomes.The liposome probe was prepared by the coassembly of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and fluorescent lipid (Liss Rhod PE).The probe showed very low background fluorescence due to fluorescence self-quenching effect of Liss Rhod PE.As the PLC enzyme selectively digested lipid, the Rhod fluorescence was recovered from its quenched state, leading to the sensitive detection of PLC.This assay provided a limit of detection (at a signal-to-noise ratio of 3) of 2 U/L for PLC.In the presence of PLC inhibitor, the fluorescent response of the sensor for PLC decreased, indicating that the assay could also be used for screening PLC inhibitors.

Objective To evaluate the accuracy of three-dimensional(3D)reconstruction of the gallbladder volume based on computed tomography(CT)images and study the biomechanical changes in gallbladder motility to explore the relationship between gallbladder dynamics and gallstone formation.Methods A method for calculating gallbladder volume based on CT 3D reconstruction of The gallbladder model was proposed and compared with the ellipsoid method.A finite element model of the gallbladder was constructed for fluid dynamics analysis to simulate changes in gallbladder motor function under different angles of convergence between the cystic and common bile ducts and in the presence of gallstones.Results The mean errors of the specific gallbladder model volume and ellipsoid volume of the 50 patients were 7.26％and 25.35％,respectively.During the refilling period,the maximum pressure,deformation,and flow velocity of the pear-shaped gallbladder were significantly higher than those of the gourd-shaped gallbladder.The angle between the gallbladder and common bile duct had little effect on the bile flow pattern,and the maximum bile flow rate was reached at an angle of 120°.The bile flow velocity of the gallbladder with calculus was lower than that of the gallbladder without calculus,and there was a vortex near the calculus.Conclusions Calculating gallbladder volume based on CT 3D reconstruction is more accurate than the ellipsoid method.Compared with a pear-shaped gallbladder,a gourd-shaped gallbladder has lower gallbladder wall contraction,bile flow rate,and poor motor function.The bile flow rate in the gallbladder is slow,which is more likely to lead to the enlargement of gallstones or the formation of new gallstones.

Objective To evaluate the risk factors and sonographic findings of pregnancies complicated by placenta increta or placenta percreta. Methods Totally, 2219 cases were retrospectively analyzed from 20 tertiary hospitals in China from January 2011 to December 2015. The data were collected based on the original case records. All cases were divided into two groups, the placenta increta (PI) group (79.1％, 1755/2219) and the placenta percreta (PP) group (20.9％, 464/2219), according to the degree of placental implantation. The risk factors and sonographic findings of placenta increta or percreta were analyzed by uni-factor and logistic regression statistic methods. Results The risk factors associated with the degree of placental implantation were age, gravida, previous abortion or miscarriage, previous cesarean sections, and placenta previa (all P<0.05), especially, previous cesarean sections (χ2=157.961) and placenta previa (χ2=91.759). Sonographic findings could be used to predict the degree of placental invasion especially the boundaries between placenta and uterine serosa, the boundary between placenta and myometrium, the disruption of the placental-uterine wall interface and loss of the normal retroplacental hypoechoic zone(all P<0.01). Conclusions Previous cesarean sections and placenta previa are the main independent risk factors associated with the degree of placenta implantation. Ultrasound could be used to make a prenatal suggestive diagnosis of placenta accreta spectrum disorders.

Objective:To design a novel electromagnetic ejection device for endoscopic suturing to achieve continuous deployment of suture nails.Methods:An electromagnetic ejection device and its accompanying suture nail structure were designed and a prototype was fabricated based on electromagnetic ejection principles. A finite element model of the electromagnetic ejection device was constructed to study the effects of armature-coil center distance and different driving voltages on suture nail ejection speed. An experimental platform for testing electromagnetic ejection velocity was constructed, and a high-speed camera was used to detect the ejection velocity. A platform for the suture embedding experiment was built to measure the effects of different voltages on the inserting speed of suture into the gastric wall tissue. A platform for a suture extraction force experiment was built to evaluate the extraction force of sutures embedded in tissues under different driving voltages.Results:A suture nail structure and electromagnetic ejection device were designed, and a prototype was fabricated. The ejection velocity increased and then decreased with the increase of the armature-coil center distance, and the maximum ejection velocity was 15.81 m/s at the center distance of 18 mm. At this distance, the voltage was linearly related to the ejection velocity, and the experimental values of the staple basically coincided with the simulated values. When the driving voltage was in the range of 150 to 180 V, the suture nails could successfully insert in the tissues, and the 180 V voltage group had a greater insertion depth. The extraction force of the suture nails at 120, 150, 180, and 210 V voltages were (0.49 ± 0.19), (1.14 ± 0.19), (1.23 ± 0.15), and (1.85 ± 0.31) N, respectively.Conclusions:A novel electromagnetic ejection device for endoscopic suturing is proposed that is capable of continuous firing of suture nails. This device provides a new long-distance driving method for intelligent, minimally invasive surgical instruments.

Objective:To establish and validate a gallstones classification method based on deep learning.Methods:A total of 618 gallstones samples were collected from East Hospital Affiliated to Tongji University, and 1 023 high-definition cross-sectional gallstones profile images were captured to construct a cross-sectional gallstones profile image dataset. Based on the traditional eight-category gallstones classification method, a lightweight network model, MobileNet V3, was trained using deep learning and transfer learning methods. The classification performance of MobileNet was evaluated using a confusion matrix with metrics such as accuracy rate, precision rate, F1 score, and recall rate. The MobileNet V3 was improved and further validated using accuracy and loss values.Results:The accuracy rate (94.17%), precision rate (94.03%), F1 score (92.96%) and recall rate (92.99%) of the improved MobileNet V3 model were better than other networks. The improved MobileNet V3 model achieved the highest accuracy rate (94.17%) in gallstones profile classification and was validated by the test set. The confusion matrix showed a weighted average of accuracy rate (92.0%), precision rate (92.6%), and F1 score (92.2%) for each category of gallstones.Conclusions:Based on deep learning, a high-accuracy gallstones classification method is proposed, which provides a new idea for the intelligent identification of gallstones.