ObjectiveThis paper aims to observe the syndrome improvement and negative antigen conversion rate of Qingxuan Daozhi formula in the treatment of influenza in children （heat accumulation in the lung and stomach syndrome）. MethodsThrough a multi-center randomized controlled methodology design，confirmed influenza cases were collected from October 2022 to April 2023 in the pediatrics department of eight hospitals，such as Dongfang Hospital of Beijing University of Chinese Medicine. A total of 180 children with influenza and heat accumulation in the lung and stomach syndrome conforming to the standard were recruited through the clinic. The sick children meeting the inclusion criteria were randomly divided into groups by a block-randomized method. The children in the experimental group were treated with Qingxuan Daozhi formula for five days，and those in the control group were treated with Oseltamivir Phosphate Granules for five days. The primary efficacy indicator was the negative conversion rate of influenza antigen detection. Secondary efficacy indicators were the Canadian acute respiratory illness and flu scale （CARIFS） and the incidence of complications，severe cases， and critical cases. Follow-up observation was conducted on the day of enrollment，48 hours after medication，72 hours after medication， and （6+1） d after medication. ResultsOne hundred and eighty participants were randomly assigned to the experimental group （90 cases） or the control group （90 cases）. All participants were followed up during the study. Comparison of influenza antigen detection results in the primary efficacy indicators showed that the average time of negative influenza antigen conversion in the experimental group was （5.29±1.25） d，and that in the control group was （5.40±1.68） d，without a statistically significant difference. After five days of intervention，52 cases in the experimental group and 51 cases in the control group converted to negative，without a statistically significant difference. CARIFS score results in the secondary efficacy indicators showed that during 72 hours after intervention，there were statistically significant differences between the experimental group and the control group in three dimensions， including headache，muscle soreness， and the need for extra care （P<0.05）. On the （6+1） days after the intervention，the differences in both the experimental group and the control group were statistically significant in 10 dimensions， including sore throat，bad sleep，uncomfortable feeling，poor spirit and fatigue，crying more than usual，the need for extra care，symptom，function，influence on parents，and total score （P<0.05）. The comparison results within the group in the dimensional scores of symptom， function， and influence on parents，as well as the CARIFS total score showed that with the delay of follow-up time，scores of both groups decreased significantly，with a statistically significant difference （P<0.01）. Inter-group comparison results showed that the mean score of the experimental group was higher than that of the control group at the time of enrollment. With the progress of intervention，the score of the experimental group was significantly decreased compared with that of the control group. At the end of follow-up，the mean score of the experimental group was lower than that of the control group，with no statistically significant difference. In terms of the incidence of complications，severe cases， and critical cases， there were no complications，severe cases， and critical cases in the two groups，without a statistically significant difference. ConclusionThe symptom improvement effect and negative antigen conversion rate of Qingxuan Daozhi formula in the treatment of influenza in children （heat accumulation in the lung and stomach syndrome） are not inferior to Oseltamivir Phosphate granules， and children's acceptance is better. It can be more widely used in clinical treatment of influenza in children （heat accumulation in the lung and stomach syndrome）.

ObjectiveTo systematically compare the effects of diverse electromagnetic stimulation protocols on hand function and activities of daily living (ADL) in stroke patients using network meta-analysis (NMA), and screen the optimal stimulation strategy for post-stroke hand dysfunction rehabilitation. MethodsNMA was performed in accordance with the PRISMA-NMA statement. Databases of CNKI, Wanfang data, VIP, CBM, PubMed, Web of Science and Embase were searched from database inception to April, 2026 for eligible randomized controlled trials (RCTs) comparing different electromagnetic stimulation protocols for post-stroke hand impairment. The Cochrane Risk of Bias tool and PEDro scale were used to assess methodological quality of included studies, while the GRADE system was adopted to grade evidence quality of primary outcomes of Fugl-Meyer Assessment-Upper Extremities (FMA-UE) and modified Barthel Index (MBI). After literature screening, data extraction and bias evaluation, RevMan 5.4 and Stata 17.0 were used for meta-analysis and NMA. ResultsA total of 22 RCT involving 17 electromagnetic stimulation protocols were finally included. The PEDro scores ranged from six to ten. The top five protocols for improving FMA-UE score were repetitive peripheral magnetic stimulation (rPMS) + repetitive transcranial magnetic stimulation (rTMS), intermittent theta-burst stimulation (iTBS) + rPMS, iTBS + transcranial direct current stimulation (tDCS), transcutaneous electrical acupoint stimulation (TEAS) and contralaterally controlled functional electrical stimulation (CCFES). tDCS was the most effective on MBI, followed by TEAS and iTBS+rPMS. Evidence quality for FMA-UE and MBI was moderate. Funnel plots suggested low risk of publication bias, and consistency tests verified good coherence between direct and indirect comparisons in network models. ConclusionElectromagnetic stimulation significantly facilitates upper limb and hand functional recovery, and improves ADL in stroke patients. The combined central-peripheral magnetic stimulation protocol may be the most effective on motor rehabilitation, whereas tDCS on ADL.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

ObjectiveTo construct a risk prediction model for carotid artery plaques in high-risk populations of stroke based on five machine learning methods. MethodsThe clinical information of the high-risk population of stroke was collected. Factor analysis and statistical analysis of syndrome elements were conducted on their traditional Chinese medicine （TCM） symptoms and tongue and pulse manifestations. On the basis of the results of factor analysis and variable screening， five machine learning methods-classification and regression tree （CART） decision tree， support vector machine （SVM）， back propagation（BP） neural network， logistic regression， and random forest-were used to construct the risk prediction model for carotid artery plaques. ResultsThe most common TCM syndrome elements in the high-risk population of stroke was Qi deficiency. The scores of Qi deficiency， Yin deficiency， and Yang deficiency in the population with carotid artery plaques were higher than those without carotid artery plaques （P<0.05）. The CART decision tree， SVM， logistic regression， BP neural network， and random forest models showed the areas under the receiver operating characteristic （ROC） curves of 0.71， 0.75， 0.76， 0.76， and 0.75， the accuracy rates of 68.94%， 69.27%， 69.44%， 70.10%， and 69.60%， the precision rates of 68.56%， 68.53%， 68.75%， 69.74%， and 69.42%， the recall rates of 68.91%， 67.93%， 67.92%， 68.10%， and 67.31%， and the F₁ values of 0.69， 0.68， 0.68， 0.68， and 0.68， respectively. ConclusionAmong the high-risk population of stroke， the most frequently distributed TCM syndrome element is Qi deficiency， with the rest mainly being fire heat， Yin deficiency， Yang deficiency， phlegm dampness， blood stasis， and Qi stagnation. Deficiency syndrome may be a major factor leading to carotid artery plaques in the high-risk population of stroke. The BP neural network model demonstrates better performance in predicting the risk of carotid artery plaques in the high-risk population of stroke. People with carotid artery plaques are more likely to present with symptoms such as a heavy head， dizziness， headache， and thready pulse. The primary community benefits more widely when the BP neural network model is adopted to predict the risk of carotid artery plaques in the high-risk population of stroke over 40 years old.

Objective To analyze the serological survey results of Changsha City in 2016 and 2024, and to investigate the short-term impact of adjusting the immunization program with two doses of mumps containing vaccine on the mumps antibody levels of healthy individuals in Changsha City. Methods Using the relevant data and samples from the mumps serological surveys conducted in 2016 and 2024, serum mumps IgG antibodies were quantitatively detected by enzyme-linked immunosorbent assay. The results from these two surveys were analyzed and compared using SPSS 25.0 software. Results The survey included 1 512 participants in 2016 and 1 398 in 2024. In 2024, both the seropositivity rate and IgG antibody levels against mumps in the healthy population were significantly higher than those in 2016 (all P<0.01). Specifically, the seropositivity rates and IgG antibody levels in the age groups of 8–17 months, 18 months–2 years, 3–4 years, and 5–9 years were significantly higher in 2024 compared to 2016 (all P < 0.001). However, among the participants in 2024, the 15–19 years age group showed relatively low seropositivity rate and IgG antibody levels. Conclusion Following the adjustment of the two-dose MuCV immunization strategy, the mumps IgG antibody levels among children aged 8 months to 9 years in Changsha have increased. It is necessary to continue implementing the routine two-dose MuCV vaccination strategy and to explore the necessity of strengthening and supplementing immunization for key groups, including the high-risk 5–9-year-old population and the immunologically vulnerable 15–19-year-old population.

Objective To systematically evaluate the risk prediction models for anastomotic leakage (AL) in patients with esophageal cancer after surgery. Methods A computer-based search of PubMed, EMbase, Web of Science, Cochrane Library, Chinese Medical Journal Full-text Database, VIP, Wanfang, SinoMed and CNKI was conducted to collect studies on postoperative AL risk prediction model for esophageal cancer from their inception to October 1st, 2023. PROBAST tool was employed to evaluate the bias risk and applicability of the model, and Stata 15 software was utilized for meta-analysis. Results A total of 19 literatures were included covering 25 AL risk prediction models and 7373 patients. The area under the receiver operating characteristic curve (AUC) was 0.670-0.960. Among them, 23 prediction models had a good prediction performance (AUC>0.7); 13 models were tested for calibration of the model; 1 model was externally validated, and 10 models were internally validated. Meta-analysis showed that hypoproteinemia (OR=9.362), postoperative pulmonary complications (OR=7.427), poor incision healing (OR=5.330), anastomosis type (OR=2.965), preoperative history of thoracoabdominal surgery (OR=3.181), preoperative diabetes mellitus (OR=2.445), preoperative cardiovascular disease (OR=3.260), preoperative neoadjuvant therapy (OR=2.977), preoperative respiratory disease (OR=4.744), surgery method (OR=4.312), American Society of Anesthesiologists score (OR=2.424) were predictors for AL after esophageal cancer surgery. Conclusion At present, the prediction model of AL risk in patients with esophageal cancer after surgery is in the development stage, and the overall research quality needs to be improved.

Objective:To explore the effectiveness of interprofessional cooperative simulation in teaching emergency care for shock patients among intensive care unit (ICU) undergraduate nursing students.Methods:An interprofessional cooperative simulation-based teaching faculty team was established for ICU undergraduate nursing students, and a shock case library was developed. Using convenience sampling, 32 ICU undergraduate nursing students in 2022 were selected as the control group and received conventional simulation-based teaching, with students rotating through roles as nurses, standardized patients, doctors, and family members. In the experimental group, 34 ICU undergraduate nursing students in 2023 and 24 ICU clinical medicine interns were recruited to act as doctors for interprofessional cooperative simulation-based teaching. Each group was divided into subgroups, with each subgroup consisting of 4-5 nursing students. One group completed simulation-based training per month for a total of 8 sessions, with each session lasting 3 hours. The teaching adopted the on-site "tidal ward" in situ simulation, and the scenarios included patient history collection and health assessment, shock emergency care, nursing evaluation, and health education. The differences between the two groups of nursing students were compared in terms of ICU exit theoretical assessment score, objective structured clinical examination skill assessment score, and satisfaction with simulation-based teaching. SPSS 22.0 was used for independent samples t test and Mann-Whitney U test. Results:The experimental group achieved significantly higher scores in theoretical assessment (84.65±8.06), total score of satisfaction with simulation-based teaching (101.00±5.13), and clinical learning and multiprofessional team dimensions (47.32±3.35) compared to the control group ( P<0.001). The experimental group achieved higher scores in objective structured clinical examination skill assessment (81.40±7.22), guiding feedback and reflection (37.50±3.04), and judgmental thinking and clinical reasoning (16.00±2.03) compared to the control group, though the differences were not significant ( P=0.977, 0.668, and 0.636). Conclusions:Interprofessional cooperative simulation enhances the shock patient emergency care abilities and satisfaction with simulation-based teaching for undergraduate nursing students.

The clinical diagnosis and treatment of urinary tract infection has long faced the challenges of insufficient standardization of diagnosis and treatment pathways,irrational use of antimicrobial drugs and high recurrence rate.How to optimize the hierarchical diagnosis and treatment pathway of urinary tract infection,standardize the use of antimicrobial drugs,and reduce the recurrence rate have always been the focus of clinical attention.There is significant heterogeneity in the existing diagnostic criteria for urinary tract infection,which seriously affects the comparability and evidence integration of clinical and research studies.In order to solve the above problems,a consensus on global multidisciplinary diagnostic criteria for urinary tract infection has been formed by international multidisciplinary experts after three rounds of Delphi method.Breaking through the traditional classification framework,the consensus innovatively established a four-dimensional quantitative scoring system including local symptoms and signs,systemic inflammatory response,quantitative analysis of pyuria and urine culture results,and established a hierarchical standard for stepwise urinary tract diagnosis according to the scoring threshold.Based on the key citations related to the consensus,this paper interprets in detail the basis for the selection of core indicators and the establishment of thresholds for the diagnosis of urinary tract infection in the consensus,and focuses on the key issues and implementation paths of the consensus in localization practice.This consensus provides a unified standard for standardizing the clinical diagnosis and treatment of urinary tract infection,improving the homogeneity of clinical research through standardized diagnostic processes,and promoting the standardization of UTI drug research and development and the rational use of antibiotics and precision.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To analyze application effect of trend analysis for safe risk in management and control for infection of medical devices,so as to optimize the management path for medical devices.Methods:Focusing on the main factors affecting the safety risks of medical devices,the least squares method of linear regression was applied for trend analysis to optimize the device management path and strengthen management.A total of 70 clinically used medical devices in the First Affiliated Hospital of Xi'an Jiaotong University from January to December 2024 were selected.These devices were managed using two models:the conventional management model and the trend analysis management model,with 35 devices under each model.The infection risk rate of medical equipment and the equipment management quality score were compared between the two management models.Additionally,160 patients managed under the two models(80 patients per model)were included to compare the patient infection rate.Results:In the 450 diagnostic and treated records of sampling inspection and consultation for medical devices from treatment equipment,diagnosis equipment,auxiliary imaging equipment and surgical equipment,the rate of infectious risk of using management mode with trend analysis were respectively 1.78%(8/450),2.22%(10/450),2.22%(10/450)and 2.44%(11/450),all of which were significantly lower than these of using conventional management mode,and the differences were significant(x2=9.904,8.902,10.465,10.770,P＜0.05).The scores of cleaning quality,the quality of disinfection and sterilization,the quality of distribution,and the retrieving quality of the management mode with trend analysis for medical devices were significantly higher than those of the conventional management mode,and the differences were statistically significant(t=15.889,13.172,15.872,17.399,P＜0.05).The infection rate of patients in the trend analysis management mode was 6.25%(5/80),which was significantly lower than that in the conventional management mode[22.50(18/80)],and the difference was statistically significant(x2=11.006,P＜0.05).Conclusion:The trend analysis for safe risk can analyze the risk factors in use and operation for medical devices from multiple perspectives,and mining main factors and conduct intervention for the devices,and reduce the incidence of safe risks of medical devices and the patients'infection rate,and improve the quality of clinical services.