Objective? To explore the research hotspots and development frontier of nursing information in China by utilizing the visual data analysis software to explore the evolution of nursing information nearly 10 years and summarize the future trend as well as challenges so as to provide a scientific and objective reference for carrying out further research. Methods? We implemented subject search in China National Knowledge Infrastructure (CNKI), and analyzed the literatures on nursing information published in 2007 by using CiteSpaceV software, and drew a knowledge graph to comprehensively analyze and visually show the overall research overview, research hotspots and research trend. Results? A total of 1 828 literatures were searched in CNKI and 1 488 effective literatures were included after screening. The literatures were imported into CiteSpaceV after transforming format. And then, we carried out the cluster analysis for high-frequency keywords to analyze the central idea of hotspot keywords. Results showed the research hotspots nearly 10 years included four aspects involving information technology integrating clinical nursing practice, design and optimization of nursing information management system, exploration of nursing information education system models, early investigation on nursing formation accomplishment and information ability and exploration of training model. The structure of burst term distribution revealed that researches on nursing information deepened and refined to data mining, emphasizing patients reported outcome, paying attention to continuous nursing which patients engaged and experienced as well as research trend at the present stage. Conclusions? Research hotspots on nursing information has been formed in China. However, the relevance between cooperation and researches is still deficient in the field of nursing formation in China. In the future, these researches hotspots should be dug continually according to policies, technologies, education as well as scientific researches and researches should be carried out in specific projects.

Objective:To construct and validate a predictive model for the risk of excessive blood loss in pa-tients with ruptured tubal pregnancy,and to provide a basis and tool for the assessment of changes in the condi-tion of patients with ruptured tubal pregnancy.Methods:Clinical data of inpatients with ruptured tubal pregnancy from January 2014 to July 2021 were retrospectively analyzed,who underwent surgical treatment in the Depart-ment of Gynecology,Dongguan Maternal and Child Health Hospital.The pelvic blood volume was categorized into excessive blood loss and non-excessive blood loss groups based on whether the amount of pelvic blood was found to be≥750 ml intraoperatively.Factors influencing the occurrence of excessive blood loss were screened and modeled by univariate analysis,Lasso regression,and multi-factor Logistic stepwise regression.The area un-der the subject working characteristic curve(AUC)was used to evaluate the discrimination of the predictive mod-el,the model's consistency was evaluated by calibration curve and goodness-of-fit test,and the clinical utility of the model was evaluated and validated by the decision analysis curve.Finally,column line plots were drawn.Results:①A total of 386 patients with ruptured tubal pregnancy were included,of whom 124(32.12%)had blood loss≥750 ml.②The optimal predictors for predicting concomitant blood loss in patients with ruptured tubal preg-nancy were screened,including:days of abdominal pain,dizziness,pallor,fatigue,the maximum diameter of para-metrial mass,human chorionic gonadotropin(β-hCG),and hemoglobin(Hb)and the model and the column line graphswere constructed accordingly.③The prediction model AUC was 0.827(95%CI 0.781-0.873);the cut-off value was 0.391,at which point the specificity and sensitivity were 68.55%and 84.35%,respectively,and the AUC validated within the model by resampling was 0.804.Clinical decision curves showed that the threshold probability intervals for the maximum net benefit values ranged from 8.5%-97%,respectively.Conclusions:The constructed prediction model was validated to suggest good discriminatory efficacy and degree of consistency.As a tool,it has clinical application value in predicting the risk of hemorrhage in patients with ruptured tubal pregnan-cy.It can help to determine the occurrence of adverse events such as hemorrhagic shock at an early stage and improve the success rate of rescue treatment.

OBJECTIVES: Firefighters are prone to suffer from psychological trauma and post-traumatic stress disorder (PTSD) in the workplace, and have a poor prognosis after PTSD. Reliable models for predicting PTSD allow for effective identification and intervention for patients with early PTSD. By collecting the psychological traits, psychological states and work situations of firefighters, this study aims to develop a machine learning algorithm with the aim of effectively and accurately identifying the onset of PTSD in firefighters, as well as detecting some important predictors of PTSD onset. METHODS: This study conducted a cross-sectional survey through convenient sampling of firefighters from 20 fire brigades in Changsha, which were evenly distributed across 6 districts and Changsha County, with a total of 628 firefighters. We used the synthetic minority oversampling technique (SMOTE) to process data sets and used grid search to finish the parameter tuning. The predictive capability of several commonly used machine learning models was compared by 5-fold cross-validation and using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, precision, recall, and F1 score. RESULTS: The random forest model achieved good performance in predicting PTSD with an average AUC score at 0.790. The mean accuracy of the model was 90.1%, with an F1 score of 0.945. The three most important predictors were perseverance, forced thinking, and reflective deep thinking, with weights of 0.165, 0.158, and 0.152, respectively. The next most important predictors were employment time, psychological power, and optimism. CONCLUSIONS PTSD onset prediction model for Changsha firefighters constructed by random forest has strong predictive ability, and both psychological characteristics and work situation can be used as predictors of PTSD onset risk for firefighters. In the next step of the study, validation using other large datasets is needed to ensure that the predictive models can be used in clinical setting.
Humans ; Stress Disorders, Post-Traumatic/diagnosis* ; Firefighters/psychology* ; Cross-Sectional Studies ; Algorithms ; Machine Learning

Objective To observe the effect of electroacupuncture at Baihui (DU20) and Shenting (DU24) on brain functional activity and working memory of rats with vascular cognitive impairment (VCI). Methods Eighteen Sprague-Dawley rats were included, in which twelve rats were ligated bilateral common carotid arteries and six rats were not ligated (sham group). The modeled rats were randomly divided into model group (n = 6) and electroacupuncture group (n = 6). The electroacupuncture group received electroacupuncture at Baihui and Shenting for four weeks. They were assessed with Y maze and Morris water maze before and after intervention, and scaned with resting-state functional magnetic resonance imaging after intervention to calculate regional homogeneity (ReHo). Results Compared with the sham group, alternation rate of Y maze decreased (P < 0.001), and escape latency of Morris water maze increased (P < 0.05) in the model group and the electroacupuncture group before intervention. Compared with the model group, alternation rate of Y maze increased (P < 0.05), and escape latency of Morris water maze decreased (P < 0.05) after intervention in the electroacupuncture group. Compared with the sham group, ReHo of bilateral hippocampus, olfactory cortex, sensory cortex and auditory cortex, and left striatum decreased in the model group; compared with the model group, ReHo of bilateral prefrontal lobe, hippocampus and olfactory cortex, and left amygdala increased in the electroacupuncture group. Conclusion Electroacupuncture at Baihui and Shenting can improve the memory function of VCI rats, which may be related to the functional activities of prefrontal lobes, hippocampus and amygdala.