ObjectiveTo investigate the effect of positive family behavior support on emotional and behavioral problems in preschool children with epilepsy. Methods A total of 80 preschool epileptic children and their parents who were admitted to the Department of Neurology of our hospital from October 2022 to February 2023 were selected as the research objects, and were divided into experimental group and control group with 40 cases each by random number table method. The control group received neurology routine nursing, and the experimental group received positive family behavior support intervention based on the control group. The scores of family intimacy and adaptability scale, strengths and difficulties questionnaire, medication compliance and quality of life of epilepsy children were compared before and after intervention between the two groups. ResultsAfter intervention, the scores of strength and difficulty questionnaire in experimental group were lower than those in control group (P<0.05), and the scores of family intimacy and adaptability scale, quality of life and medication compliance in experimental group were higher than those in control group (all P<0.05). ConclusionThe application of positive family behavior support program can reduce the occurrence of emotional behavior problems, improve family closeness and adaptability, improve medication compliance, and improve the quality of life of preschool children with epilepsy.
The paper discusses the scientificity, evidence-based research, clinical practice and related problems of traditional Chinese medicine (TCM) from three aspects: theory, demonstration and application, and attempts to clarify ambiguities and misconceptions, further correctly evaluate the historical status, important value and realistic significance of TCM, strengthen our national and cultural confidence, reinforce our theoretical and practical confidence, and strongly refute the derogation and stigmatization towards TCM theory and practice by very few people, in order to provide suggestions for the progress and development of TCM.
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.
ObjectiveThis study aimed to revise the perioperative recovery scale for integrative medicine (PRSIM) based on item response theory (IRT). MethodsUnder the guidance of IRT, a total of 349 patient data collected during the development of the original version of PRSIM at Guangdong Provincial Hospital of Chinese Medicine were used. Principal component analysis was performed using SPSS 18.0 software to test the unidimensionality. The R language was utilized for parameter estimation, including discrimination coefficient, difficulty parameters and information content, as well as drawing item characteristic curves to assess item quality and estimate item functioning differences. A comprehensive screening process was carried out by combining expert consultations, patient evaluations, and discussions within a core group. ResultsThe degree of discrimination of all items ranged from ?0.535 to 2.195. The difficulty coefficient ranged from ?10.343 to 5.461, and the average information content of all items ranged from 0.043 to 1.075. Based on the criteria for parameter selection, nine items were retained. The results of expert consultations indicated the removal of 5 items and the modification of 7 items. After discussion within the core group, a final decision was made to remove 5 items. ConclusionBased on a synthesis of IRT and expert consultation feedback, and following discussions within the core group, a revised version comprising 15 items is retained and modified from the original 20 items.
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.
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.
Objective To introduce a new theory of shoulder stability mechanism, rebalancing theory, and clinical application of this new theory for the shoulder instability and dysfunction of motion. Methods Through extensive review of the literature related to shoulder instability and dysfunction of the motion in recent years, combined with our clinical practice experience, the internal relation between passive stability mechanism and dynamic stability mechanism were summarized. Results Rebalancing theory of shoulder stability mechanism is addressed, namely, when the shoulder stability mechanism is destructive, the stability of the shoulder can be restored by the rebalance between dynamic stability mechanism and passive stability mechanism. When dynamic stability is out of balance, dynamic stability can be restored by rebalancing the different parts of dynamic stability mechanism or to strengthen the passive stability mechanism. When passive stability mechanism is out of balance, passive stability can be restored by rebalancing the soft tissue and bone of the shoulder. ConclusionRebalancing theory of shoulder stability mechanism could make a understanding the occurrence, development, and prognosis of shoulder instability and dysfunction from a comprehensive and dynamic view and guide the treatment effectively.
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.
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 investigate the network reorganization and dynamic brain activity in visuospatial neglect (VSN) patients using resting-state electroencephalography (rEEG), and to develop classification models to facilitate its identification. Methods In this retrospective study, stroke patients admitted to the Department of Rehabilitation, Xuanwu Hospital, Capital Medical University between August 2022 and December 2024 were included and divided into VSN (n=22) and non-VSN (n=21) groups based on paper-and-pencil assessments. A healthy control group (n=20) was also recruited. Microstate segmentation and graph-theoretical analysis were applied to rEEG data to extract microstate parameters and topological network features. Four machine learning models (logistic regression, na?ve Bayes, k-nearest neighbors, and decision tree) were built for classification. Results Compared with the non-VSN group, the VSN group showed significantly increased mean duration and time coverage in microstate C, and significantly decreased coverage and occurrence in microstate D. Graph-theoretical analysis revealed higher average clustering coefficients in the VSN group. Degree centrality in the frontal-central regions (C1, CZ) was significantly lower, while that in the parietal-occipital regions (P5, P3, PO7, PO5) was significantly higher than in the non-VSN group. Among the classification models, logistic regression and na?ve Bayes models performed best, with the mean duration of microstate C contributing most to classification performance. Conclusions Patients with VSN exhibit distinct alterations in electroencephalography microstate dynamics and functional network topology. Microstate parameters play a crucial role in distinguishing VSN from non-VSN stroke cases, and combining these features with machine learning offers a promising approach for early identification and personalized intervention of VSN.