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.
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.
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.
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.
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.
Cluster randomized trial (CRT) is one of the most common design for complex intervention. This paper mainly introduced:the definition of CRT, two designs of CRT including the completely randomization and the restricted randomization (such as stratified randomization and matching randomization), and the statistical analysis methods (such as the general statistical analysis and mixed effect model/multi-level model). This paper also introduced how to estimate the sample size of a CRT, how to report a CRT, and how to apply it into a clinical or community study.
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.
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 observe the effects of cluster therapy combined with anisodamine, dexamethasone and ambroxol on arterial blood gas, inflammatory cytokines and pulmonary pathological changes by making an early (<48 h) primary blast lung injury model in rats. MethodsEighty Wistar rats were randomly divided into six groups, ie. a control group (n=5), an injury group (n=15), an ambroxol treatment group (n=15), a dexamethasone treatment group, a scopolamine treatment group (n=15), a combination of ambroxol, dexamethasone and anisodamine group (n=15). The treatment groups were injected intraperitoneally with ambroxol 46.7 mg/kg (three times a day) or (and) dexamethasone at 5 mg·kg–1·d–1 or (and) anisodamine at a dose of 3.33 mg/kg (three times a day). The rats in the injury group were injected intraperitoneally with an equal volume of normal saline. Respiratory rate and weight change were observed before and after injury. Five rats were sacrificed at 6 hours, 24 hours and 48 hours after injury in each experimental group. Arterial blood gas analysis, Yelverton pathological score, lung tissue wet/dry weight ratio, serum tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) were measured. The lung histopathology was observed. ResultsAfter lung blast injury, the rats in the injury group showed progressive respiratory acidosis, and hypoxemia increased with the increase of IL-6 and TNF-α in a time-dependent manner. The PaO2 decreased in the groups with ambroxol, dexamethasone and anisodamine alone or in combination with anisodamine, and the contents of serum IL-6 and TNF-α decreased. Pathological edema and inflammatory infiltration of lung tissue were alleviated significantly. ConclusionsAfter treatment with dexamethasone, anisodamine and ambroxol after lung blast injury, blood gas analysis is improved, inflammatory factor level is decreased and lung injury is alleviated, indicating that the three drugs can treat lung detonation injury in rats. The cluster therapy is superior to the single drug therapy.
ObjectiveTo research on the influence of cluster management on the nutritional intervention for nasopharynx cancer patients undergoing intensity modulated radiation therapy (IMRT), in order to discuss effective and feasible nutrition management method. MethodEighty-three nasopharynx cancer patients undergoing IMRT between June 2013 and December 2014 were selected as the study subjects. They were divided into two groups randomly. Regular health education and nutritional guidance were carried out for the 41 patients in the control group, while nutritional risk screening (NRS)-2002 nutrition screening, nutrition assessment and nutritional intervention were carried out for the 42 patients in the intervention group. Nutrition risk, nutritional status and side-reaction were recorded and evaluated for both groups of patients. ResultsAfter treatment, NRS-2002 score of the intervention group was lower than the control group (P<0.05). Body weight, constitutional index, skinfold thickness of triceps brachii muscle, mid-arm circumference and mid-arm muscle circumference of the intervention group were better than the control group (P<0.05). Total serum protein, serum albumin, serum transferrin were better and the rate of levelⅢ-Ⅳ radiation-induced oral mucositis was lower in the intervention group than that in the control group (P<0.05). ConclusionsThe application of cluster management model in nutritional intervention is a way to promote patients' rehabilitation, which can effectively improve the whole body situation of nasopharynx cancer patients, and reduce malnutrition rate and side-reaction.