Thoracic trauma has the characteristics of complexity, specificity, urgency and severity. Therefore, the treatment is particularly important. Thoracic Traumatology Group, Trauma Medicine Branch of Zhejiang Medical Association organized the writing of the thoracic trauma and further optimization consensus of Zhejiang thoracic surgery industry Treatment and diagnosis of rib and sternum trauma: A consensus statement by Zhejiang Association for Thoracic Surgery (version 2021), compiled the popular science book Emergency Treatment and Risk Avoidance Strategy of Thoracic Trauma and Illustration of Real Scene Treatment of Trauma, actively prepared to build the trauma database of Zhejiang Province, and participated in the construction of trauma group in the Yangtze River Delta. Although Zhejiang Province has carried out many related works in the diagnosis and treatment of chest trauma, it is still inconsistent with the development requirements of the times. Standardization of chest trauma treatment, popularization of relevant knowledge, management of trauma big data, grass-roots radiation promotion tour and further optimization of industry consensus are the requirements and objectives of this era.
摘要:目的:优化药品单剂量调剂,加强信息化管理,优化操作流程。 方法:采用东华软件:住院药房管理系统(DTCISIP)和住院药品调剂系统(DTCISID) 实施。结果:东华软件成功实现了我院4300病床的药品单剂量调剂及各部门管理联网,优化了操作系统及流程,且系统运行稳定。结论:东华软件进行药品单剂量调剂,加强了药品的出入管理,优化了药品单剂量调剂的操作流程。Abstract: Objective: To improve united dose dispension, enhance the utilization of information technology in management of united dose dispension and optimize clinical human resource. Methods: DONG HUA software, which included DTCISIP system(system for management of medicine for inpatients) and DTCISID system(system for dispension of medicine for in-patients), was used to carry out united dose dispension. Results: United dose dispension of 4300 beds were easy to achieve by using DONG HUA software. The system worked smoothly and received lots of praise. Conclusion: The management of medicine is enhanced and clinical human resource is optimized by using DONG HUA software to carry out united dose dispension
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.
Sudden cardiac arrest is one of the critical clinical syndromes in emergency situations. A cardiopulmonary resuscitation (CPR) is a necessary curing means for those patients with sudden cardiac arrest. In order to simulate effectively the hemodynamic effects of human under AEI-CPR, which is active compression-decompression CPR coupled with enhanced external counter-pulsation and inspiratory impedance threshold valve, and research physiological parameters of each part of lower limbs in more detail, a CPR simulation model established by Babbs was refined. The part of lower limbs was divided into iliac, thigh and calf, which had 15 physiological parameters. Then, these 15 physiological parameters based on genetic algorithm were optimized, and ideal simulation results were obtained finally.
Antimicrobial peptides (AMPs) are a class of peptides widely existing in nature with broad-spectrum antimicrobial activity. It is considered as a new alternative to traditional antibiotics because of its unique mechanism of antimicrobial activity. The development and application of natural AMPs are limited due to their drawbacks such as low antimicrobial activity and unstable metabolism. Therefore, the design and optimization of derived peptides based on natural antimicrobial peptides have become recent research hotspots. In this paper, we focus on ribosomal AMPs and summarize the design and optimization strategies of some related derived peptides, which include reasonable primary structure modification, cyclization strategy and computer-aided strategy. We expect to provide ideas for the design and optimization of antimicrobial peptides and the development of anti-infective drugs through analysis and summary in this paper.
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.
Objective To analyze the risk factors of type 2 diabetes mellitus and establish BP neural network model for screening of type 2 diabetes mellitus based on particle swarm optimization (PSO) algorithm. Methods Inpatients with type 2 diabetes mellitus in the Department of Endocrinology of the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between July 2021 and August 2022 were selected as the case group and healthy people in the Health Management Center of the Affiliated Hospital of Guangdong Medical University as the control group. Basic information and physical and laboratory examination indicators were collected for comparative analysis. PSO-BP neural network model, BP neural network model and logistic regression models were established using MATLAB R2021b software and the optimal screening model of type 2 diabetes mellitus was selected. Based on the optimal model, the mean impact value algorithm was used to screen the risk factors of type 2 diabetes mellitus. Results A total of 1 053 patients were included in the case group and 914 healthy peoples in the control group. Except for type of salt, family history of comorbidities, body mass index, total cholesterol, low density lipoprotein cholesterol and staple food intake (P>0.05), the other indexes showed significant differences between the two groups. The performance of the PSO-BP neural network model outperformed the BP neural network model and the logistic regression model. Based on PSO-BP neural network model, the mean impact value algorithm showed that the risk factors for type 2 diabetes mellitus were fasting blood glucose , heart rate, age , waist-arm ratio and marital status , and the protective factors for type 2 diabetes mellitus were high density lipoprotein cholestero, vegetable intake, residence, education level, fruit intake and meat intake. Conclusions There are many influencing factors of type 2 diabetes mellitus. Focus should be placed on high-risk groups and regular disease screening should be carried out to reduce the risk of type 2 diabetes. The screening model of PSO-BP neural network performs the best, and it can be extended to the early screening and diagnosis of other diseases in the future.
Aiming at the disadvantages of traditional direct aperture optimization (DAO) method, such as slow convergence rate, prone to stagnation and weak global searching ability, a gradient-based direct aperture optimization (GDAO) is proposed. In this work, two different optimization methods are used to optimize the shapes and the weights of the apertures. Firstly, in order to improve the validity of the aperture shapes optimization of each search, the traditional simulated annealing (SA) algorithm is improved, the gradient is introduced to the algorithm. The shapes of the apertures are optimized by the gradient based SA method. At the same time, the constraints between the leaves of multileaf collimator (MLC) have been fully considered, the optimized aperture shapes are meeting the requirements of clinical radiation therapy. After that, the weights of the apertures are optimized by the limited-memory BFGS for bound-constrained (L-BFGS-B) algorithm, which is simple in calculation, fast in convergence rate, and suitable for solving large scale constrained optimization. Compared with the traditional SA algorithm, the time cost of this program decreased by 15.90%; the minimum dose for the planning target volume was improved by 0.29%, the highest dose for the planning target volume was reduced by 0.45%; the highest dose for the bladder and rectum, which are the organs at risk, decreased by 0.25% and 0.09%, respectively. The results of experiment show that the new algorithm can produce highly efficient treatment planning a short time and can be used in clinical practice.
Stress distribution of denture is an important criterion to evaluate the reasonableness of technological parameters, and the bite force derived from the antagonist is the critical load condition for the calculation of stress distribution. In order to improve the accuracy of stress distribution as much as possible, all-ceramic crown of the mandibular first molar with centric occlusion was taken as the research object, and a bite force loading method reflecting the actual occlusal situation was adopted. Firstly, raster scanning and three dimensional reconstruction of the occlusal surface of molars in the standard dental model were carried out. Meanwhile, the surface modeling of the bonding surface was carried out according to the preparation process. Secondly, the parametric occlusal analysis program was developed with the help of OFA function library, and the genetic algorithm was used to optimize the mandibular centric position. Finally, both the optimized case of the mesh model based on the results of occlusal optimization and the referenced case according to the cusp-fossa contact characteristics were designed. The stress distribution was analyzed and compared by using Abaqus software. The results showed that the genetic algorithm was suitable for solving the occlusal optimization problem. Compared with the reference case, the optimized case had smaller maximum stress and more uniform stress distribution characteristics. The proposed method further improves the stress accuracy of the prosthesis in the finite element model. Also, it provides a new idea for stress analysis of other joints in human body.
It is the main method for amplifying the specific gene to use the nucleic acid amplification system to accomplish polymerase chain reaction (PCR). The temperature retard between heat source and sample exists in the heating and cooling progresses of most nucleic acid amplification system. The retard would result in the problem that the sample would take a long time to reach the set temperature and the problem would reduce the speed of integrate reaction. Non-specific products would be created in the process of amplification when the sample cannot reach the set temperature within a certainly time and the amplified efficiency would be reduced. A miniaturization nucleic acid amplification system heated by air was designed in this study according to the principle of air-heated nucleic acid amplification system and the characteristics of the PCR instrument Smart-cycler. The heat transfer process was analyzed and the heat transfer time was calculated. The actual temperature was measured in real time, and the temperature curves were fitted. The heating time was chosen by analysis results and data fitting and the air temperature was changed, while the sample temperature was recorded. The retard between sample and air was optimized by choosing the best curve of sample temperature. The temperature retard between sample and air was reduced sharply and the required time of integrate progress is shortened to 50%. We confirmed from the amplification experiment of Listeria monocytogenes that the improved system could complete 3 cycles within 4 minutes, and the amplification effect was good. The amplification speed and effect could be improved effectively by optimizing the delay between sample and air.