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.
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.
Day surgery is flourishing in public hospitals in China with the advantages of strong planning, short stay and high efficiency. Under the background of “diversification of surgeons, diversification of disease structure and precision of scheduling needs”, higher requirements are put forward for refined scheduling strategies of day surgery. The research of scientific and precise surgical scheduling strategy is of great significance to realize efficient coordination and optimal allocation of day surgical resources. This article reviews the necessity of precise scheduling of day surgery, the current situation of scheduling of day surgery, the key dimensions affecting the scheduling of day surgery, and the evaluation system for precise scheduling of day surgery.
To obtain recombinant human β2-microglobulin (rhβ2M) with properties of good solubility and high purity from E.coli, prokaryotic expression conditions were optimized and protein purification was performed in this study. After testing the effect of different IPTG concentrations, temperatures and induction times on the production of rhβ2M, the optimum expression conditions were determined, i.e. joining IPTG to final concentration being 0.8 mmol/L and inducing time 6 h and at temperature of 25℃. Under the optimum induction conditions, the ratio of soluble rhβ2M to soluble bacterial protein was 63.7%. After purified by Ni Sepharose 6 Fast Flow, the purity of rhβ2M achieved a greater value of 95%. Western blot analysis revealed that rhβ2M possessed the antigen property that specifically interacted with anti-β2M antibody.
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.
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.
Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method. It outperformed existing methods, providing a superior performance and introducing a novel perspective for decoding EEG signals, thereby contributing to the field of emotion research from EEG signal.
Medication adherence will directly affect the validity of primary endpoint indicator. This article discussed how to improve the medication adherence of clomiphene citrate based on PCOSact. We found that 20 (3+15+2) cases were "protocol violation" and there were cases in which researchers made mistakes while distributing medicine and guiding patients how to take medicine. Focusing on these problems we sumed up experience and emphasized the importance of medication compliance through the following aspects:(1) Improvement of insite supervision and remote monitoring; (2) Standardization training for research assistants; (3) Health education for subjects.
摘要:目的:優化藥品單劑量調劑,加強信息化管理,優化操作流程。 方法:采用東華軟件:住院藥房管理系統(DTCISIP)和住院藥品調劑系統(DTCISID) 實施。結果:東華軟件成功實現了我院4300病床的藥品單劑量調劑及各部門管理聯網,優化了操作系統及流程,且系統運行穩定。結論:東華軟件進行藥品單劑量調劑,加強了藥品的出入管理,優化了藥品單劑量調劑的操作流程。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
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.
