Currently, about one-third of patients with anti-epilepsy drug or resective surgery continue to have sezure, the mechanism remin unknown. Up to date, the main target for presurgical evaluation is to determene the EZ and SOZ. Since the early nineties of the last century network theory was introduct into neurology, provide new insights into understanding the onset, propagation and termination. Focal seizure can impact the function of whole brain, but the abnormal pattern is differet to generalized seizure. Brain network is a conception of mathematics. According to the epilepsy, network node and hub are related to the treatment. Graphy theory and connectivity are main algorithms. Understanding the mechanism of epilepsy deeply, since study the theory of epilepsy network, can improve the planning of surgery, resection epileptogenesis zone, seizure onset zone and abnormal node of hub simultaneously, increase the effect of resectiv surgery and predict the surgery outcome. Eventually, develop new drugs for correct the abnormal network and increase the effect. Nowadays, there are many algorithms for the brain network. Cooperative study by the clinicans and biophysicists instituted standard and extensively applied algorithms is the precondition of widely used clinically.
The innovative behavior of clinical nurses is of great significance for the professional development of nurses and the improvement of nursing service quality. This research topic has received continuous attention from domestic and foreign scholars. There is still significant room for improvement in the level of innovative behavior among clinical nurses in China. Constructing effective interventions to enhance innovative behavior among clinical nurses in China is an urgent requirement to promote the development of nursing informatization and nursing quality. This article reviews the intervention forms, theoretical support, effectiveness, and limitations of innovative behaviors among clinical nurses both domestically and internationally. It proposes prospects for future intervention plans, aiming to provide ideas and references for nursing managers to develop tailored, scientific, and effective intervention strategies.
With the range of application of computational biology and systems biology gradually expanding, the complexity of the bioprocess models is also increased. To address this difficult problem, it is required to introduce positive alternative analysis method to cope with it. Taking the dynamic model of the epidemic control process as research object, we established an evaluation model in our laboratory. Firstly, the model was solved with nonlinear programming method. The results were shown to be good. Based on biochemical systems theory, the ODE dynamic model was transformed into S-system. The eigen values of the model showed that the system was stable and contained oscillation phenomenon. Next the sensitivities of rate constant and logarithmic gains of the three key parameters were analyzed, as well as the robust of the system. The result indicated that the biochemical systems theory could be applied in different fields more widely.
With the change of COVID-19, the prevention and control of COVID-19 infection epidemic entered a new stage in December 2022. How to quickly complete the emergency treatment of a large number of patients in a short period of time, and ensure that patients in emergency department can get rapid and effective medical treatment has always been an urgent problem that emergency department need to solve. The Department of Emergency Medicine of West China Hospital of Sichuan University has adopted patient-oriented management measures based on the core idea of the new public management theory, and has achieved remarkable results. Therefore, this article summarizes the workflow and nursing management strategies of the emergency department rescue area of West China Hospital of Sichuan University in dealing with the batch treatment of COVID-19 infected patients, including optimizing and correcting the environment layout of the ward, implementing the “secondary triage” mode in the rescue area, adding an inter-hospital referral platform for critical patients with COVID-19 emergency, building a conventional COVID-19 reserve material repository in the emergency department, setting up a field office for multi-department joint emergency admission service, optimizing emergency transport services for patients with COVID-19, scientific scheduling and reasonable human resource management, and providing humanistic care for employees, in order to provide reference for the management practice of the emergency department.
Objective To explore the influencing factors of the usage behavior of the scientific research management system and provide references for hospitals in constructing scientific research management systems. Methods Data were collected through questionnaires in April 2024. Based on the unified theory of acceptance and use of technology (UTAUT), the information system success model, and the self-efficacy theory, a research model on the influencing factors of the usage behavior of the scientific research management system among medical staff was constructed from the dual perspectives of users and information systems. The structural equation model was utilized to explore the influencing factors of the usage behavior of the scientific research management system. Results A total of 527 questionnaires were collected. Among them, there were 157 males and 370 females. The overall Cronbach α coefficient of the questionnaire was 0.916, and the KMO value was 0.896. For Bartlett’s test of sphericity (P<0.001). The composite reliability of each latent variable was greater than 0.7, and the average variance extracted was greater than 0.5. Therefore, this questionnaire had good reliability and validity. The research showed that information quality, performance expectancy, effort expectancy, and system quality all had significant positive impacts on the usage intention of users of the scientific research management system (P<0.05). Meanwhile, facilitating conditions and usage intention both had significant positive impacts on the usage behavior of users (P<0.05). Conclusions The construction of the scientific research management system should be guided by management needs, comprehensively sort out the general scientific research work needs of medical staff. Through the apply information-based means, various forms of training, and strengthening policy guidance, the aim is to improve the intelligence level of system operations, enhance the convenience of user self-service, and promote the effective construction of the ecosystem of the scientific research management system.
Objective To construct an intervention program for postoperative fear of falling in elderly patients with hip fracture based on cognitive behavioral theory. Methods Based on cognitive behavioral theory and literature review, an initial draft of intervention plan for postoperative fear of falling in elderly patients with hip fracture was constructed. From January to March 2025, after two rounds of expert consultations and revisions, the final plan was formed. Results A total of 16 experts across the country were invited to participate in two rounds of Delphi expert consultations, covering areas such as orthopedic clinical nursing, orthopedic clinical medicine, nursing education, nursing management, rehabilitation therapy, and psychological therapy. The active participation rates for the two rounds of consultations were 94.12% and 100.00%, respectively. The expert authority coefficients were 0.860 and 0.907, respectively, and the Kendall harmony coefficients were 0.369 and 0.524, respectively. Ultimately, a program composed of 5 primary indicators (fall fear management team, fall fear management goals, fall fear assessment, fall fear intervention measures, and post-intervention effect evaluation), 17 secondary indicators, and 31 tertiary indicators was constructed. Conclusion The intervention program for postoperative fear of falling in elderly patients with hip fracture based on cognitive behavior theory constructed in this study is scientific and operable, which can provide reference and guidance for clinical nursing staff.
Traditional sample entropy fails to quantify inherent long-range dependencies among real data. Multiscale sample entropy (MSE) can detect intrinsic correlations in data, but it is usually used in univariate data. To generalize this method for multichannel data, we introduced multivariate multiscale entropy into multiscale signals as a reflection of the nonlinear dynamic correlation. But traditional multivariate multiscale entropy has a large quantity of computation and costs a large period of time and space for more channel system, so that it can not reflect the correlation between variables timely and accurately. In this paper, therefore, an improved multivariate multiscale entropy embeds on all variables at the same time, instead of embedding on a single variable as in the traditional methods, to solve the memory overflow while the number of channels rise, and it is more suitable for the actual multivariate signal analysis. The method was tested in simulation data and Bonn epilepsy dataset. The simulation results showed that the proposed method had a good performance to distinguish correlation data. Bonn epilepsy dataset experiment also showed that the method had a better classification accuracy among the five data set, especially with an accuracy of 100% for data collection of Z and S.
In the context of actively coping with aging, China has introduced a series of health care integration policies. Using the advocacy coalition framework theory, this paper aims to analyze the process of health care integration policy changes in China from three dimensions: policy beliefs, external events and policy learning. The policy subsystem of health care integration in China includes two coalitions: top-down cascade promotion and bottom-up absorption and radiation. External events and policy learning triggered policy change, where policy learning included endogenous learning within the coalition and exogenous learning between the coalitions. A policy impasse occurs when the two advocacy coalitions are at odds, and policy brokers and professional forums can get rid of the policy impasse. In the process of policy change in China’s health care integration, the two major advocacy coalitions have reached a certain consensus. It is recommended to alleviate the problems in the integration of health care by strengthening the external factors in the change of health care policy, enhancing the policy learning in the change of health care policy, and making full use of the information resources in the change of health care policy, so as to promote the high-quality development of the integration of health care.
Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the “evolutional” and “structural” properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.
Mild cognitive impairment (MCI) is a clinical transition state between age-related cognitive decline and dementia. Researchers can use neuroimaging and neurophysiological techniques to obtain structural and functional information about the human brain. Using this information researchers can construct the brain network based on complex network theory. The literature on graph theory shows that the large-scale brain network of MCI patient exhibits small-world property, which ranges intermediately between Alzheimer's disease and that in the normal control group. But brain connectivity of MCI patients presents topologically structural disorder. The disorder is significantly correlated to the cognitive functions. This article reviews the recent findings on brain connectivity of MCI patients from the perspective of multimodal data. Specifically, the article focuses on the graph theory evidences of the whole brain structural and functional and the joint covariance network disorders. At last, the article shows the limitations and future research directions in this field.