The goal of this paper is to solve the problems of large volume, slow dynamic response and poor intelligent controllability of traditional gait rehabilitation training equipment by using the characteristic that the shear yield strength of magnetorheological fluid changes with the applied magnetic field strength. Based on the extended Bingham model, the main structural parameters of the magnetorheological fluid damper and its output force were simulated and optimized by using scientific computing software, and the three-dimensional modeling of the damper was carried out after the size was determined. On this basis and according to the design and use requirements of the damper, the finite element analysis software was used for force analysis, strength check and topology optimization of the main force components. Finally, a micro magnetorheological fluid damper suitable for wearable rehabilitation training system was designed, which has reference value for the design of lightweight, portable and intelligent rehabilitation training equipment.
Transcranial electrical stimulation (TES) is a non-invasive neuromodulation technique with great potential. Electrode optimization methods based on simulation models of individual TES field could provide personalized stimulation parameters according to individual variations in head tissue structure, significantly enhancing the stimulation accuracy of TES. However, the existing electrode optimization methods suffer from prolonged computation times (typically exceeding 1 d) and limitations such as disregarding the restricted number of output channels from the stimulator, further impeding their clinical applicability. Hence, this paper proposes an efficient and practical electrode optimization method. The proposed method simultaneously optimizes both the intensity and focality of TES within the target brain area while constraining the number of electrodes used, and it achieves faster computational speed. Compared to commonly used electrode optimization methods, the proposed method significantly reduces computation time by 85.9% while maintaining optimization effectiveness. Moreover, our method considered the number of available channels for the stimulator to distribute the current across multiple electrodes, further improving the tolerability of TES. The electrode optimization method proposed in this paper has the characteristics of high efficiency and easy operation, potentially providing valuable supporting data and references for the implementation of individualized TES.
In this paper, a method for dose calculation with pencil beam kernels constructed by point kernel superposition was proposed to accelerate the dose calculation during intensity optimization iteration. With this method, the direct aperture optimization method can be integrated in the planning system based on point kernel convolution/superposition model. The dose calculation time was also reduced during the iteration. From the result of the phantom and clinical patient data test, it was concluded that this method could be used for the intensity optimization of iteration dose calculation as the satisfied precision due to the optimization result coherence obtained. By implementing the method in the planning system product based on point kernel convolution/superposition model, a lot of additional research and development works for the pencil beam dose calculation model as well as the product maintenance cost can be avoided.
In order to realize brain-computer interface (BCI), optimal features of single trail motor imagery electroencephalogram (EEG) were extracted and classified. Mu rhythm of EEG was obtained by preprocessing, and the features were optimized by spatial filtering, which are estimated from a set of data by method of common spatial pattern. Classification decision can be made by Fisher criterion, and classification performance can be evaluated by cross validation and receiver operating characteristic (ROC) curve. Optimal feature dimension determination projected by spatial filter was discussed deeply in cross-validation way. The experimental results show that the high discriminate accuracy can be guaranteed, meanwhile the program running speed is improved. Motor imagery intention classification based on optimized EEG feature provides difference of states and simplifies the recognition processing, which offers a new method for the research of intention recognition.
Due to individual differences of the depth of anaesthesia (DOA) controlled objects, the drawbacks of monitoring index, the traditional PID controller of anesthesia depth could not meet the demands of nonlinear control. However, the adjustments of the rules of DOA fuzzy control often rely on personal experience and, therefore, it could not achieve the satisfactory control effects. The present research established a fuzzy closed-loop control system which takes the cerebral state index (CSI) value as a feedback controlled variable, and it also adopts the particle swarm optimization (PSO) to optimize the fuzzy control rule and membership functions between the change of CSI and propofol infusion rate. The system sets the CSI targets at 40 and 30 through the system simulation, and it also adds some Gaussian noise to imitate clinical disturbance. Experimental results indicated that this system could reach the set CSI point accurately, rapidly and stably, with no obvious perturbation in the presence of noise. The fuzzy controller based on CSI which has been optimized by PSO has better stability and robustness in the DOA closed loop control system.
ObjectiveTo identify a more popularized preparation protocol of leukocytes-rich platelet-rich plasma (L-PRP) for higher tolerance rate.MethodsThe peripheral blood samples of 76 volunteers (45.0 mL/case) were mixed with 5 mL sodium citrate injection for blood transfusion, and L-PRP was prepared by twice centrifugations. All blood samples were divided into three groups according to the parameters of twice centrifugation: experimental group A (12 cases, 400×g, 10 minutes for the first time and 1 100×g, 10 minutes for the second time), experimental group B (27 cases, 800×g, 10 minutes for the first time and 1 100×g, 10 minutes for the second time), and control group (37 cases, 1 360×g, 10 minutes for the first time and 1 360×g, 10 minutes for the second time). The platelet recovery rate and platelet and leukocyte enrichment coefficient of L-PRP in each group were calculated and compared.ResultsAfter removal of abnormal blood samples (platelet recovery rate was more than 100% or white thrombus), the remaining 55 cases were included in the statistical analysis, including 10 cases in experimental group A, 21 cases in experimental group B, and 24 cases in control group. The platelet enrichment coefficient and platelet recovery rate of experimental group B were significantly higher than those of experimental group A and control group (P<0.05); there was no significant difference between experimental group A and control group (P>0.05). There was no significant difference in leukocyte enrichment coefficient between experimental groups A, B, and control group (P>0.05).ConclusionThe preparation quality of PRP is affected by various factors, including centrifugal force, centrifugal time, temperature, and operation process, etc. Twice centrifugation (800×g, 10 minutes for the first time and 1 100×g, 10 minutes for the second time) is an ideal and feasible centrifugation scheme, which can obtain satisfactory platelet recovery rate and enrichment coefficient with thicker buffy coat, which can reduce the fine operation requirements for operators, improve the fault tolerance rate and generalization.
To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features (P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.
ObjectiveTo investigate the effect of pulmonary ultrasound on pulmonary complications in ultra-fast-track anesthesia for congenital heart disease surgery.MethodsIn 2019, 60 patients with congenital heart diseases underwent ultra-fast-track anesthesia in Shenzhen Children's Hospital, including 34 males and 26 females with the age ranging from 1 month to 6 years. They were randomly divided into a normal group (group N, n=30) and a lung ultrasound optimization group (group L, n=30). Both groups were used the same anesthesia method and anesthetic compatibility. The group N was anesthetized by ultra-fast-track, the tracheal tube was removed after operation and then the patients were sent to the cardiac intensive care unit (CCU). After operation in the group L, according to the contrast of pre- and post-operational lung ultrasonic examination results, for the patients with fusion of B line, atelectasis and pulmonary bronchus inflating sign which caused the increase of lung ultrasound score (LUS), targeted optimization treatment was performed, including sputum suction in the tracheal tube, bronchoscopy alveolar lavage, manual lung inflation suction, ultrasound-guided lung recruitment and other optimization treatments, and then the patients were extubated after lung ultrasound assessment and sent to CCU. The occurrence of pulmonary complications, LUS, oxygenation index (OI), extubation time, etc were compared between the two groups.ResultsCompared with the induction of anesthesia and 1 hour after extubation of the two groups, the incidence of pulmonary complications in the group L (18 patients, 60.0%) was lower than that in the group N (26 patients, 86.7%, χ2= 4.17, P=0.040) and the rate of patients with LUS score reduction was higher in the group L (15 patients, 50.0%) than that in the group N (7 patients, 23.3%, χ2=4.59, P=0.032). The correlation analysis between the LUS and OI value of all patients at each time point showed a good negative correlation (P<0.05). Extubation time in the group L was longer than that in the group N (18.70±5.42 min vs. 13.47±4.73 min, P=0.001).ConclusionUltra-fast-track anesthesia for congenital heart disease can be optimized by pulmonary ultrasound examination before extubation, which can significantly reduce postoperative pulmonary complications, improve postoperative lung imaging performance, and help patients recover after surgery, and has clinical application value.
In order to calibrate the hand-eye transformation of the surgical robot and laser range finder (LRF), a calibration algorithm based on a planar template was designed. A mathematical model of the planar template had been given and the approach to address the equations had been derived. Aiming at the problems of the measurement error in a practical system, we proposed a new algorithm for selecting coplanar data. This algorithm can effectively eliminate considerable measurement error data to improve the calibration accuracy. Furthermore, three orthogonal planes were used to improve the calibration accuracy, in which a nonlinear optimization for hand-eye calibration was used. With the purpose of verifying the calibration precision, we used the LRF to measure some fixed points in different directions and a cuboid’s surfaces. Experimental results indicated that the precision of a single planar template method was (1.37±0.24) mm, and that of the three orthogonal planes method was (0.37±0.05) mm. Moreover, the mean FRE of three-dimensional (3D) points was 0.24 mm and mean TRE was 0.26 mm. The maximum angle measurement error was 0.4 degree. Experimental results show that the method presented in this paper is effective with high accuracy and can meet the requirements of surgical robot precise location.
High quality clinical trial depends on the preliminary research design and optimizing, the quality control in the medium term, the source data verification and statistics in later stages. Steering committee (SC) can meet above requirements. According to the characteristics of the research project, we invited international experts whose professional background are obstetrics and gynecology, statistics and methodology to set up SC. SC will hold regular conference, the content of the conference included project progress and quality control, research assistant training and assessment, conducting knowledge lectures and so on. The establishment of SC ensured the protocol maneuverability, answered PCOS related problems from different professional perspectives, and solved the problems such as how to improve the scientific research output. At the same time, it provides a platform of scientific research practice and self-improvement. It has profound influence on standardizing the management of the clinical trial, strengthening the consciousness of team work, promoting multi-disciplinary team cooperation, expanding scientific research thinking and cultivating clinical research talent.