Recent studies showed that certain drugs can change regulatory reaction parameters in gene regulatory networks (GRNs) and therefore restore pathological cells to a normal state. A state control framework for regulating biological networks has been built based on attractors and bifurcation theory to analyze this phenomenon. However, the control signal is self-developed in this framework, of which the parameter perturbation method can only calculate the state transition time of cells with single control variable. Therefore, an optimal control method based on the dynamic optimization algorithms is proposed for complex biological networks modeled by nonlinear ordinary differential equations (ODEs). In this approach, dynamic optimization problems are constructed based on basic characteristics of the biological networks. Furthermore, using an example of a simple low-dimensional three-node GRN and a complex high-dimensional cancer GRN, MATLAB is utilized to calculate optimal control strategies with either single or multiple control variables. This method aims to achieve accurate and rapid state regulation for biological networks, which can provide a reference for experimental researches and medical treatment.
Objective To explore the application effect of process optimization in perioperative venous access management. Methods A total of 205 general surgery patients in the Operating Room of Cheng Du Shang Jin Nan Fu Hospital, West China Hospital of Sichuan University from April to May 2018 were selected as the control group, and 205 general surgery patients from June to August 2018 were selected as the observation group. The traditional management process was used in the control group, and the process optimization management was performed in the observation group. The establishment of venous access and related complications between the two groups of patients, as well as the satisfaction of patients and staff before and after the process optimization were compared. Results There was no significant difference in gender, age, education level, operation type, anesthesia method, operation duration, or intraoperative intravenous infusion channels between the two groups of patients (P>0.05). There was no statistically significant difference in gender, age, educational background, job title, job nature, or working years of the staff participating in the satisfaction survey before and after the process optimization (P>0.05). The rate of repetitive venous puncture (15.61% vs. 58.05%) and the idelness ratio of the intraoperative indwelling needle approach (10.73% vs. 52.20%) in the observation group were lower than those of the control group, and the differences were statistically significant (P<0.05). There was no statistically significant difference in the incidence of tube blockage, detubation, or phlebitis/exudation between the two groups (P>0.05). After process optimization, patient satisfaction (22.91±3.43 vs. 17.44±4.90) and staff satisfaction (28.17±2.56 vs. 20.65±3.71) were higher than before optimization, and the differences were statistically significant (P<0.05). Conclusions The process optimization of venous access management for perioperative patients can effectively reduce the rate of venous repeated venipuncture and the idelness ratio of the intraoperative indwelling needle approach, reduce invasive operations on patients, reduce the ineffective work of nurses, avoid the waste of medical resources such as manpower and materials, and improve the satisfaction of patients and staff. It is worthy of promotion and application.
The stiffness of an ideal fracture internal fixation implant should have a time-varying performance, so that the fracture can generate reasonable mechanical stimulation at different healing stages, and biodegradable materials meet this performance. A topology optimization design method for composite structures of fracture internal fixation implants with time-varying stiffness is proposed, considering the time-dependent degradation process of materials. Using relative density and degradation residual rate to describe the distribution and degradation state of two materials with different degradation rates and elastic modulus, a coupled mathematical model of degradation simulation mechanical analysis was established. Biomaterial composite structures were designed based on variable density method to exhibit time-varying stiffness characteristics. Taking the bone plate used for the treatment of tibial fractures as an example, a composite structure bone plate with time-varying stiffness characteristics was designed using the proposed method. The optimization results showed that material 1 with high stiffness formed a columnar support structure, while material 2 with low stiffness was distributed at the degradation boundary and inside. Using a bone remodeling simulation model, the optimized bone plates were evaluated. After 11 months of remodeling, the average elastic modulus of callus using degradable time-varying stiffness plates, titanium alloy plates, and stainless steel plates were 8 634 MPa, 8 521 MPa, and 8 412 MPa, respectively, indicating that the use of degradable time-varying stiffness plates would result in better remodeling effects on the callus.
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.
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.
Consultation is an important form for the diagnosis and treatment of severe diseases, and consultation management is an important content of medical management work, which directly affects the medical quality and treatment efficiency of the hospital. With the help of information network platform, our hospital has realized electronic consultation system online through scientific development, training enhancement, and safeguard mechanism improvement. The system can optimize consultation work process effectively, improve the consultation work, save manpower cost and help construction of hospital informatization.
In the present study, packaging system composed of pAAV-CMV-GFP, pAAV-RC and pHelper were transfected into human embryonic kidney 293 cells (HEK293 cells) mediated by polyethyleneimine (PEI) to explore an optimal transfection condition. Different total plasmid DNA dosages (1, 2, 3, 4, 5, 6μg) and different PEI/Plasmid ratios (1:1, 3:1, 5:1, 7:1) were tested with detection of green fluorescence protein (GFP) with ImagePro Plus6.0 Software. Then transfection efficiency of the optimized transfection system was further observed for different time periods(12, 24, 36, 48, 60, 72 h). The results showed that total plasmid dosage of 4μg/well with PEI/plasmid ratio of 3:1~5:1 was an efficient transfection condition. Transfection efficiency-time curve was an S-shaped curve. Transfection efficiency reached a plateau at 60 h after transfection. The optimized conditions for PEI-mediated transfection at the optimal time result in enhanced transfection efficiency of triple plasmid into HEK293 cells.
Existing near-infrared non-invasive blood glucose detection modelings mostly detect multi-spectral signals with different wavelength, which is not conducive to the popularization of non-invasive glucose meter at home and does not consider the physiological glucose dynamics of individuals. In order to solve these problems, this study presented a non-invasive blood glucose detection model combining particle swarm optimization (PSO) and artificial neural network (ANN) by using the 1 550 nm near-infrared absorbance as the independent variable and the concentration of blood glucose as the dependent variable, named as PSO-2ANN. The PSO-2ANN model was based on two sub-modules of neural networks with certain structures and arguments, and was built up after optimizing the weight coefficients of the two networks by particle swarm optimization. The results of 10 volunteers were predicted by PSO-2ANN. It was indicated that the relative error of 9 volunteers was less than 20%; 98.28% of the predictions of blood glucose by PSO-2ANN were distributed in the regions A and B of Clarke error grid, which confirmed that PSO-2ANN could offer higher prediction accuracy and better robustness by comparison with ANN. Additionally, even the physiological glucose dynamics of individuals may be different due to the influence of environment, temper, mental state and so on, PSO-2ANN can correct this difference only by adjusting one argument. The PSO-2ANN model provided us a new prospect to overcome individual differences in blood glucose prediction.
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.
Objective To explore the experience and needs of orthopedic inpatients for pre-hospital examinations led by nurses, provide a reference for optimizing the pre-hospital examination procedures and improve the pre-hospital examination experience of patient. Methods Using the method of phenomenology, semi-structured in-depth interviews were conducted on 35 patients who attended the Department of Orthopedics of the Second Affiliated Hospital of Army Medical University from July to August 2018 and had undergone pre-hospital examinations. Colaizzi’s seven-step method was used to encode, analyze, organize, summarize, and refine topics. Results Patients’ experience and needs for pre-hospital examinations led by nurses could be divided into three major sections: attitudes and emotions, individualized pre-rehabilitation needs and pre-hospital examination feelings. Attitudes and emotions included high treatment expectations, feelings of loss, and some patients’ understanding of pre-hospital examinations. Individualized pre-rehabilitation needs included pre-rehabilitation needs with cardiopulmonary diseases, pre-rehabilitation needs with sleep dysfunction, nutritional conditioning needs, and medication safety needs. Patients’ feelings during pre-hospital examinations mainly included complicated procedures and staff attitudes that need to be improved. Conclusion Some links in the pre-hospital inspection process urgently need to be optimized. In pre-hospital examinations, it is necessary to focus on patient expectation management and predictive communication, improve multidisciplinary cooperation, formulate personalized pre-rehabilitation plans, optimize examination procedures, strengthen humanistic care, and improve patient experience.