Epilepsy is a neurological disease with disordered brain network connectivity. It is important to analyze the brain network mechanism of epileptic seizure from the perspective of directed functional connectivity. In this paper, causal brain networks were constructed for different sub-bands of epileptic electroencephalogram (EEG) signals in interictal, preictal and ictal phases by directional transfer function method, and the information transmission pathway and dynamic change process of brain network under different conditions were analyzed. Finally, the dynamic changes of characteristic attributes of brain networks with different rhythms were analyzed. The results show that the topology of brain network changes from stochastic network to rule network during the three stage and the node connections of the whole brain network show a trend of gradual decline. The number of pathway connections between internal nodes of frontal, temporal and occipital regions increase. There are a lot of hub nodes with information outflow in the lesion region. The global efficiency in ictal stage of α, β and γ waves are significantly higher than in the interictal and the preictal stage. The clustering coefficients in preictal stage are higher than in the ictal stage and the clustering coefficients in ictal stage are higher than in the interictal stage. The clustering coefficients of frontal, temporal and parietal lobes are significantly increased. The results of this study indicate that the topological structure and characteristic properties of epileptic causal brain network can reflect the dynamic process of epileptic seizures. In the future, this study has important research value in the localization of epileptic focus and prediction of epileptic seizure.
Objective To analyze the hot spot and future application trend of artificial intelligence technology in the field of intensive care medicine. Methods The CNKI, WanFang Data, VIP and Web of Science core collection databases were electronically searched to collect the related literature about the application of artificial intelligence in the field of critical medicine from January 1, 2013 to December 31, 2022. Bibliometrics was used to visually analyze the author, country, research institution, co-cited literature and key words. Results A total of 986 Chinese articles and 4 016 English articles were included. The number of articles published had increased year by year in the past decade, and the top three countries in English literature were China, the United States and Germany. The predictive model and machine learning were the most frequent key words in Chinese and English literature, respectively. Predicting disease progression, mortality and prognosis were the research focus of artificial intelligence in the field of critical medicine. ConclusionThe application of artificial intelligence in the field of critical medicine is on the rise, and the research hotspots are mainly related to monitoring, predicting disease progression, mortality, disease prognosis and the classification of disease phenotypes or subtypes.
When a clustered coronavirus disease 2019 epidemic occurs, how to prevent and control hospital infection is a challenge faced by each medical institution. Under the normalization situation, building an effective prevention and control system is the premise and foundation for medical institutions to effectively prevent and control infection when dealing with clustered epidemics. According to the principles of control theory, medical institutions should quickly switch to an emergency state, and effectively deal with the external and internal infection risks brought by clustered epidemics by strengthening source control measures, engineering control measures, management control measures and personal protection measures. This article summarizes the experience of handling clustered outbreaks in medical institutions in the prevention and control of coronavirus disease 2019, and aims to provide a reference for medical institutions to take effective prevention and control measures when dealing with clustered outbreaks.
ObjectiveTo systematically review the efficacy of exercise therapy for patients with chronic low back pain (CLBP) by network meta-analysis (NMA).MethodsThe PubMed, EBSCO, EMbase, The Cochrane Library, Web of Science, CNKI, WanFang Data, VIP and CBM databases were electronically searched to collect randomized controlled trials (RCT) on exercise for patients with CLBP from inception to May, 2020. Two reviewers independently screened literature, extracted data and assessed risk of bias of included studies. Then, NMA was performed by Stata 15.1 software.ResultsA total of 79 RCTs involving 5 782 CLBP patients were included. The effect of exercise therapy on pain in patients with CLBP were in the following rankings: yoga (SMD=−1.25, 95%CI −1.87 to −0.64, P<0.000 1), health Qigong/Taichi (SMD=−1.12, 95%CI −1.87 to −0.64, P=0.002), sling exercise (SMD=−1.07, 95%CI −1.64 to −0.50, P<0.000 1), Mackenzie therapy (SMD=−1.05, 95%CI −1.68 to −0.42, P=0.001), pilates (SMD=−0.96, 95%CI −1.74 to −1.78, P=0.016), multimodal training (SMD=−0.80, 95%CI −1.33 to −0.27, P=0.003) and stabilisation/motor control (SMD=−0.62, 95%CI −1.03 to −0.21, P=0.003). The effect of exercise therapy on function in patients with CLBP were in the following rankings: Mackenzie therapy (SMD=−0.62, 95%CI −1.03 to −0.21, P=0.003), and yoga (SMD=−0.88, 95%CI −1.51 to −0.25, P=0.007). Clusterank results showed that Mackenzie therapy, yoga, pilates, sling exercise and multimodal training were similar in improving pain and physical function in patients with CLBP.ConclusionsThe current study shows that yoga, Mackenzie therapy, pilates, sling exercise and multimodal training constitute the optimal group for improving CLBP symptoms. Health Qigong/Taichi is second only to yoga in improving pain in patients with CLBP, which has great promotional value.
ObjectiveWhen using multi-center data to construct clinical prediction models, the independence assumption of data will be violated, and there is an obvious clustering effect among research objects. In order to fully consider the clustering effect, this study intends to compare the model performance of the random intercept logistic regression model (RI) and the fixed effects model (FEM) considering the clustering effect with the standard logistic regression model (SLR) and the random forest algorithm (RF) without considering the clustering effect under different scenarios. MethodsIn the process of forecasting model establishment, the prediction performance of different models at the center level was simulated when there were different degrees of clustering effects, including the difference of discrimination and calibration in different scenarios, and the change trend of this difference at different event rates was compared. ResultsAt the center level, different models, except RF, showed little difference in the discrimination of different scenarios under the clustering effect, and the mean of their C-index changed very little. When using multi-center highly clustered data for forecasting, the marginal forecasts (M.RI, SLR and RF) had calibrated intercepts slightly less than 0 compared with the conditional forecasts, which overestimated the average probability of prediction. RF performed well in intercept calibration under the condition of multi-center and large samples, which also reflected the advantage of machine learning algorithm for processing large sample data. When there were few multiple patients in the center, the FEM made conditional predictions, the calibrated intercept was greater than 0, and the predicted mean probability was underestimated. In addition, when the multi-center large sample data were used to develop the prediction model, the slopes of the three conditional forecasts (FEM, A.RI, C.RI) were well calibrated, while the calibrated slopes of the marginal forecasts (M.RI and SLR) were greater than 1, which led to the problem of underfitting, and the underfitting problem became more prominent with the increase in the central aggregation effect. In particular, when there were few centers and few patients, overfitting of the data could mask the difference in calibration performance between marginal and conditional forecasts. Finally, the lower the event rate the central clustering effect at the central level had a more pronounced impact on the forecasting performance of the different models. ConclusionThe highly clustered multi-center data are used to construct the model and apply it to the prediction in a specific environment. RI and FEM can be selected for conditional prediction when the number of centers is small or the difference between centers is large due to different incidence rates. When the number of hearts is large and the sample size is large, RI can be selected for conditional prediction or RF for edge prediction.
Due to the high dimensionality and complexity of the data, the analysis of spatial transcriptome data has been a challenging problem. Meanwhile, cluster analysis is the core issue of the analysis of spatial transcriptome data. In this article, a deep learning approach is proposed based on graph attention networks for clustering analysis of spatial transcriptome data. Our method first enhances the spatial transcriptome data, then uses graph attention networks to extract features from nodes, and finally uses the Leiden algorithm for clustering analysis. Compared with the traditional non-spatial and spatial clustering methods, our method has better performance in data analysis through the clustering evaluation index. The experimental results show that the proposed method can effectively cluster spatial transcriptome data and identify different spatial domains, which provides a new tool for studying spatial transcriptome data.
Objective To learn the distribution pattern and worldwide research tendency of vitrectomy literatures. Methods Articles were searched from American Institute of Scientific Information (ISI) online database of web of science (WOS) database as a data source, to analyze the age distribution, national and regional, funding agency and citation of the vitrectomy literatures included during the year of 1971 -2011. The analysis software BibExcel and SPSS 19.0 were used to cluster highfrequency of them. Results Totally 8540 literatures were included, the numbers of them were gradually increased since 1971, significantly after 1991. The literatures were mainly in English, the literatures of our country capacity ranked 6th; funded institutions in all article, the National Natural Science Foundation of China ranked No. 5. Citation gradually increased since 1991, increased significantly after 2004. There were 50 highfrequency subjects, and hot topics were clustered into 6 categories which including vitrectomy for diseases of macula lutea, new techniques and complication of vitrectomy, medical treatment and surgical therapy of diabetic retinopathy, cataract, vitrectomy for endophthalmitis caused by intraocular injection and eye injury. Conclusions There is a growing trend on the research of vitrectomy. The hot topics include vitrectomy for diseases of macula lutea, new techniques and complication of vitrectomy. It may provide references for the scholars in scientific research and clinical studies.
ObjectiveTo explore the preventive role of maintaining constant pressure of the endotracheal catheter cuff on ventilator-associated pneumonia (VAP). MethodsFrom January to December 2015, 96 patients of type Ⅱ respiratory failure were selected as the trial group who underwent intubation and mechanical ventilation more than 48 hours in the Intensive Care Unit (ICU). We used pressure gauges to measure the endotracheal catheter cuff pressure regularly and maintained a constant pressure in addition to the application of artificial airway cluster management. We recorded the initial pressure value which was estimated by pinching with finger and set initial pressure to 30 cm H2O (1 cm H2O=0.098 kPa). We measured endotracheal catheter cuff pressure and recorded it during different intervals. We reviewed 88 patients with the same disease as the control group who only accepted artificial airway cluster management between January and December 2014. Mechanical ventilation time, VAP occurrence time, ICU admission time, the incidence of VAP were recorded and analyzed for both the two groups of patients. ResultsIn the trial group, the initial pressure of endotracheal catheter cuff which was estimated by pinching with finger showed that only 11.46% of pressure was between 25 and 30 cm H2O and 82.29% of the pressure was higher than 30 cm H2O. We collected endotracheal catheter cuff pressure values during different interval time by using pressure gauges to maintain a constant management. The ratio at the pressure between 25 and 30 cm H2O was respectively 41.32%, 43.75%, 64.20%, 76.54%, 91.13%, and 91.85%. ICU admission time, mechanical ventilation time in patients of the trial group decreased more, compared with the control group, and the differences were statistically significant (t=4.171, P<0.001; t=4.061, P<0.001). The VAP occurrence time in patients of the trial group was later than the control group (t=2.247, P<0.001). ConclusionThe endotracheal catheter cuff pressure estimated by pinching with finger has errors. We recommend using pressure gauges to detect pressure every four hours, which utilizes minimal time to maintain effective pressure. The method of artificial airway of cluster management combined with the pattern of maintaining constant endotracheal catheter cuff pressure can shorten ICU admission time, mechanical ventilation time and delay the occurrence of VAP.
ObjectiveTo learn the distribution pattern and characteristics of autoimmune pancreatitis research literature, and its worldwide research trend. MethodsPublished data between September 22, 2004 and September 21, 2014 were searched by using the keyword autoimmune pancreatitis in the database of Pubmed. Publication year, journals, authors and research topics were bibliometrically analyzed. The analysis software Bibliographic Item Co-occurrence Matrix Builder was used for cluster analysis on high-frequency keywords. ResultsA total of 1 518 articles on autoimmune pancreatitis were acquired. The amount of published literature rose rapidly in the past 10 years, reaching its peak in the year of 2012. Most of the articles were published by several leading authors in the leading journals. There were 26 keywords with a frequency of more than 30 times, and 4 categories were classified through cluster analysis of these keywords. They were pathology and immunology, imaging, diagnosis and treatment. ConclusionsAttention on autoimmune pancreatitis has been increasing in the recent 10 years. Japanese researchers have been taking the lead. Current research focus is the diagnosis of autoimmune pancreatitis.
Objective To investigate the phenotyping of COPD by cluster analysis and evaluate the value of this method.Methods 168 COPD patients were enrolled from Beijing Tongren Hospital. Demographic and clinical data, such as, sex, age, body mass index ( BMI) , smoking index, course of disease,exacerbation rate, and comorbidities were collected. Pulmonary function test, emphysema scoring by HRCT,dyspnea by MMRC score, COPD assessment test ( CAT) score, six-minute walk test were performed for each patient during the stable stage. Cluster analysis was conducted using SPSS 13. 0. Results According to the GOLD criteria,5, 75, 75, and 13 patients were classified into GOLD stage 1, 2, 3, and 4, respectively. There was no difference among different stages in sex distribution, BMI, smoking index, hypertension, and cerebral infarction incidence( P gt; 0. 05) , but the differences in age, disease course, dyspnea score, six-minute walk distance, BODE score, CAT score, coronary heart disease, exacerbation rate, and HRCT emphysema visual score were significant( P lt;0. 05) . By cluster analysis,168 patients were finally classified into three groups:younger/mild, older/ severe, and older/moderate. The patients with the same GOLD stage appeared indifferent clusters and the patients belonging to different GOLD stages could be in the same cluster. There were significant differences among three groups in age, BMI, exacerbation rate, dyspnea score, CAT score, and comorbidities. The result showed that HRCT emphysema visual score was also an important index todifferentiate clusters, suggesting that emphysema was an important phenotype of COPD. Conclusions Cluster analysis can classify homogeneous subjects into the same cluster, and heterogeneous subjects into different clusters. The results suggest that COPD phenotyping by cluster analysis is clinically useful and significant.