Objective To explore the effects of empathy technology on emotion regulation, sleep improvement and quality of life improvement in patients with first benign paroxysmal positional vertigo (BPPV). Methods A total of 100 patients with the first BPPV in The Second Affiliated Hospital of Naval Medical University from December 2023 to November 2024 were selected, and were divided into control group and observation group by random number table method. The patients in both groups received routine reduction treatment and outpatient follow-up every 3 months after discharge, with a total of 4 follow-up visits. The patients in control group received routine health and psychological education; on this basis, the patients in observation group received empathy technique intervention before, during, after reposition and during follow-up. The emotion, sleep and quality of life scales were evaluated before intervention and at the 4th outpatient follow-up. The recurrence rates of BPPV within 1 year after discharge were compared between the two groups. Results One year after discharge, self-rating anxiety scale (SAS), self-rating depression scale (SDS), symptom checklist 90 (SCL-90) and Pittsburgh sleep quality index (PSQI) scores in the observation group were lower than those in the control group, and multi-dimensional quality of life scores in the observation group was higher than that in the control group. These differences were statistically significant (P＜0.05). The recurrence rate of BPPV in the observation group was lower than that in the control group 1 year after discharge (P＜0.05). Conclusions Empathy technology intervention can further improve emotion, sleep quality, and quality of life, but reduce disease recurrence rate in first BPPV patients receiving routine treatment and health education, so it can be widely used in clinical practice.

BackgroundPerceived discrimination has been identified as a main risk factor for depression in maintenance hemodialysis patients. Chronic Illness Rejection and Discrimination Scale （CIRDS） is a measure for assessing perceived discrimination in individuals with chronic disease. However， the Chinese version of CIRDS for maintenance hemodialysis patients has not yet been established. ObjectiveTo translate CIRDS into Chinese version and evaluate its reliability and validity in maintenance hemodialysis patients， so as to provide an effective tool for assessing the perceived discrimination among maintenance hemodialysis patients. MethodsThe Brislin's model for translation， back-translation， cross-cultural adaptation and pre-experimentation was utilized to develop a Chinese version of CIRDS. A coherent of 250 maintenance hemodialysis patients attending Taihe Hospital Affiliated to Hubei Medical College， from July to October 2023 were selected as the research subjects. The formal scale was refined by employing item analysis， exploratory factor analysis and confirmatory factor analysis. The validity of the scale was evaluated using content validity and construct validity. The reliability of the scale was evaluated using Cronbach's α coefficient， test-retest reliability and split-half reliability. ResultsThe Chinese version of CIRDS consisted of 11 items， including 2 factors （perceived discrimination and perceived rejection）. The scale-level content validity index （S-CVI） value was 0.898 and the item-level content validity index （I-CVI） values ranged from 0.875 to 1.000. Two common factors were extracted by exploratory factor analysis and explained 65.41% of the total variance. Confirmatory factor analysis also indicated that the model provided a good fit for the data. The Cronbach's α coefficient of the scale was 0.910， with Cronbach's α coefficients of 0.835 and 0.912 for the perceived discrimination and perceived rejection， respectively. The split-half reliability of the scale was 0.803， and the test-retest reliability was 0.920. ConclusionThe Chinese version of CIRDS has excellent reliability and validity， which can be used to evaluate the perceived discrimination in maintenance hemodialysis patients.

[Objective] To evaluate the participation rate of voluntary blood donation among college students in China by meta-analysis. [Methods] CNKI, Wan Fang Data, VIP, Pub Med, Web of science and Embase databases were searched to collect cross-sectional studies on the participation rate of voluntary blood donation among college students from the establishment of the database to August 10, 2024. Two researchers independently screened the literature, extracted the data and assessed the risk of bias of the included studies, and then used Stata16.1 software for meta-analysis. [Results] Finally, 36 articles were included, with a total of 37 348 research subjects and 11 541 college students participating in voluntary blood donation. The meta-analysis results showed that the participation rate of college students in voluntary blood donation in China was 34.0% [95% CI (31.0,37.0)]. The sub group analysis results showed that the participation rate of college students in voluntary blood donation in different regions was 36.1% [95% CI (24.1, 48.1)] in the eastern region, 30.2% [95% CI (26.8, 33.6)] in the central region, and 35.1% [95% CI (31.0, 39.3)] in the western region, with the eastern region higher than the central and western regions (P<0.001); The participation rate of college students in voluntary blood donation during different research periods was 32.0% before 2020 [95% CI (31.4, 32.6)] and 27.1% after 2020 [95% CI (26.3, 27.9)], with before 2020 higher than after 2020 (P<0.001); The participation rate of voluntary blood donation among college students of different genders is 36.8% for males [95% CI (32.8, 40.9)] and 28.5% for females [95% CI (24.8, 32.2)], with males higher than females (P<0.001); The participation rate of college students in voluntary blood donation among different academic backgrounds was 26.8% for associate degree students [95% CI (23.1, 30.5)], 26.4% for undergraduate students and above [95% CI (22.9, 29.8)], with no statistically significant difference (P>0.05); The participation rate of college students in voluntary blood donation among different majors is 46.4% [95% CI (34.4, 58.4)] for medical majors and 29.1% [95% CI (22.1, 36.0)] for non-medical majors, with medical majors higher than non-medical majors (P<0.001); The participation rate of college students in voluntary blood donation among different grades is 27.7% [95% CI (24.3, 31.2)] for second grade and below, 33.7% [95% CI (26.4, 40.9)] for third grade and above, with the latter higher than the former (P<0.001); The participation rate of college students in voluntary blood donation among different household registrations is 24.7% in urban areas [95% CI (21.5, 27.8)] and 26.8% in rural areas [95% CI (22.1, 31.4)], with no statistically significant difference (P>0.05); The participation rate of college students in voluntary blood donation among different family attitudes was 43.3% in support [95% CI (18.5, 68.2)] and 37.8% in non support [95% CI (26.6, 48.9)], with no statistical difference (P>0.05); The participation rate of college students in voluntary blood donation was 35.7% [95% CI (27.8, 43.5)] among those who were aware of the blood donation policies, and 24.7% [95% CI (13.7, 35.7)] among those who were not aware, with the former higher than the latter (P<0.001); The participation rate of voluntary blood donation among college students was 47.8% [95% CI (34.5, 61.0)] among those who were aware of blood donation knowledge and 38.0% [95% CI (22.1, 53.9) among those who were not aware, with the former higher than the latter (P<0.001). [Conclusion] There is still room for improvement in the rate of voluntary blood donation among college students, and the government should plan the overall situation of blood collection, and cooperate with colleges and universities to play the main role of donation publicity, and correctly identify potential donors, so as to improve the participation rate of voluntary blood donation among college students and promote the development of voluntary blood donation.

ObjectiveTo investigate the current status of emergency management for severe mental disorders in Shanghai, and to provide countermeasures and suggestions for the establishment of a sound emergency management system for severe mental disorders and the enhancement of emergency management capability. MethodsA questionnaire survey and qualitative interviews were used to conduct an investigation into the emergency management in 17 district-level mental illness prevention and control institutions in Shanghai, which includes the basic situation of emergency management for severe mental disorders, the construction of emergency response teams and personnel, emergency preparedness drills and training, emergency management plans and rules and regulations, and problems encountered in emergency management. ResultsIn terms of emergency management mechanism and basic situation, resources such as personnel allocation, security funds and green channel were well equipped in each district-level mental illness prevention and control institution in Shanghai. However, the equipment of some hardware facilities was still insufficient to some extent. Therefore, further improvement on the emergency management mechanism for severe mental disorders was needed. With regard to the construction of emergency team and personnel allocation, the majority were those aged between 35‒<45 years old, with a bachelor’s degree, and more than 10 years of working experience. For example, 90.27% staff in district-level mental illness prevention and control institution had a bachelor’s degree or above, which was higher than that among the staff in community-level (73.60%); staff majored in clinical medicine in district-level institution accounted for the proportion at 52.71%, higher than that among the staff in community-level (28.86%); 57.24% staff in district-level institution had an intermediate professional title, higher than that among the staff in community-level (42.28%); and 69.90% staff in district-level institution had more than 10 years of working experience, higher than that among the staff in community-level (43.62%). In the aspect of emergency drills and training, all district-level mental illness prevention and control institutions in Shanghai had a high demand for emergency training, and the weak aspects mainly focused on lack of emergency service protocols, skills of addressing technical challenges, and construction of effectiveness evaluation system. Moreover, the teaching methods were primarily centered on case analysis, simulation drills, interactive discussions, and so forth. Concerning emergency management plans and rules and regulations, all districts in Shanghai had relatively established well-developed systems for emergency response plans, emergency response leadership groups, and emergency response operational task forces for severe mental disorders. About half of the institutions had established other rules and regulations related to emergency management of severe mental disorders in addition to emergency plans. ConclusionShanghai has initially established an emergency management system for severe mental disorders, but it is still fragile in specialized training for emergency management of severe mental disorders, construction of emergency management mechanisms, and the building-up of grassroots emergency teams. Further priorities should include strengthening emergency management training, enhancing the construction of emergency management personnel teams, and gradually establishing a more comprehensive and integrated emergency management mechanism for severe mental disorders.

Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

Objectives:To develop a machine learning prediction model for poor outcome of acute minor ischemic stroke (AMIS) at 90 days after onset and to explain the importance of various risk factors.Methods:Patients with AMIS admitted to the Second People's Hospital of Hefei from June 2022 to December 2023 were included retrospectively. AMIS was defined as the National Institutes of Health Stroke Scale (NIHSS) score ≤5 at admission. According to the modified Rankin Scale score at 90 days after onset, the patients were divided into a good outcome group (<2) and a poor outcome group (≥2). Recursive feature elimination (RFE) method was used to screen characteristic variables of poor outcome. Based on logistic regression (LR), supported vector machine (SVM), and extreme Gradient Boosting (XGBoost) machine learning algorithms, prediction models for poor outcome of AMIS were developed, and the predictive performance of the models was compared by the area under the curve (AUC) of receiver operating characteristic (ROC) curve and the calibration curve. Shapley Additive exPlanations (SHAP) algorithm was used to explain the role of characteristic variables in the optimal prediction model. Results:A total of 225 patients with AMIS were included, of which 152 (67.56%) had good outcome and 73 (32.44%) had poor outcome. Multivariate analysis showed that baseline NIHSS score, baseline systolic blood pressure, hypertension, diabetes, low-density lipoprotein cholesterol, homocysteine, body mass index, D-dimer, and age were the characteristic variables associated with poor outcome in patients with AMIS. The ROC curve analysis shows that the LR model had the best predictive performance (AUC=0.888, 95% confidence interval [ CI] 0.807-0.970), the next was the XGBoost model (AUC=0.888, 95% CI 0.796-0.980), while the SVM model had the lowest performance (AUC=0.849, 95% CI 0.754-0.944). The calibration curve showed that the LR model performed the best in terms of calibration accuracy. SHAP showed that baseline systolic blood pressure, baseline NIHSS score, diabetes, hypertension and body mass index were the top five risk factors for poor outcome of patients with AMIS. Conclusions:The LR algorithm has stable and superior performance in predicting poor outcome of patients with AMIS. Baseline systolic blood pressure, baseline NIHSS score, diabetes, hypertension and body mass index are the important risk factors for poor outcome of patients with AMIS.

Objective To explore the clinical efficacy of antibiotic bone cement covered reconstruction steel plate in the treatment of infected anterior pelvic ring fracture.Methods From January 2017 to March 2022,11 patients with infected ante-rior pelvic ring fracture were treated with antibiotic bone cement covered reconstruction steel plate including 7 males and 4 fe-males and the age ranged from 27 to 49 years old.The pelvic fractures were classified according to the Tile typology:4 cases of Cl type,4 cases of C2 type,and 3 cases of C3 type.Among them,2 cases of infected anterior ring were infected after internal fixation of anterior ring,and 9 patients were infected with infected anterior ring due to incomplete early debridement,which was classified as infected according to the injury severity score(ISS)for 24 to 38 scores.The anterior ring was internally fixed by extended debridement,irrigation,and antibiotic bone cement covered reconstruction plate,and the posterior ring fractures were all closed reduction and internally fixed with sacroiliac screws.Results All 11 cases obtained follow-up from 13 to 20 months.A-mong them,2 patients had recurrence of postoperative infection,1 case was re-dissected and replaced with antibiotic bone cement-coated internal fixation,and 1 case had a milder infection without accumulation of the medullary cavity,and the infection was controlled by retaining the plate and replacing the antibiotic bone cement only after dissecting.Two cases developed incisional oozing,which healed after removal of the internal fixation three months postoperatively.All patients did not show pelvic fracture redisplacement or reinfection during the follow-up period.All 11 cases eventually healed bony.At the final follow-up,according to the Matta score,the fracture reduction was excellent in 6 cases,good in 4,and possible in 1.According to the Majeed functional score,it was excellent in 6,good in 3,and possible in 2.Conclusion Antibiotic bone cement covered reconstruction plate is ef-fective in the treatment of infected anterior pelvic ring fracture,with high intraoperative safety and low recurrence rate of infec-tion,which is conducive to the early postoperative rehabilitation and significantly shortens the course of the disease.

Objective:This study aimed to provide basic data for the prevention, diagnosis and treatment of pediatric viral myocarditis (VMC) in China through analyzing the epidemic characteristics and disease burden of pediatric inpatients with VMC from 2016 to 2021.Methods:We performed a descriptive statistical analysis to the age, genders, seasons, regions and hospitalization cost and days of pediatric VMC inpatients and the death. All of the information was obtained from 27 Children′s hospitals or Maternal and Child Health hospitals of 23 provinces of China from 2016 to 2021.Results:A total of 7 647 599 cases including 1 646 VMC inpatients were admitted into our study. The annual numbers of hospitalizations were 173, 227, 313, 301, 295 and 337, with the hospitalized constituent ratios being 14.9/100 000, 17.9/100 000, 23.0/100 000, 20.5/100 000, 26.5/100 000 and 26.4/100 000 from 2016 to 2021. In recent 6 years, the proportion of VMC hospitalizations had increased yearly ( P<0.001), and had associated with the onset age ( P<0.001). Aged 12-≤18 years owned the highest hospitalized constituent ratio. The Northeast of China owned the largest number of VMC inpatients, and the East second to it. Among the 1 646 VMC children, there were 68 deaths, with the hospitalized case fatality rate of 4.13%. There were no significant differences between genders, age, seasons, years and fatality rate of VMC inpatients. For the diseases burden, the median of hospitalization days of all VMC inpatients was 10 days (IQR 6, 21), and the median of hospitalization cost was 1 1 842.3 RMB (IQR 6 969.22, 19 714.78). The median of hospitalization days of deceased VMC children was only 1 day (IQR 1, 3), the median cost could be 8 874.03 RMB (IQR 5 277.94, 5 6 151.59). Conclusions:In this study, we found that proportion of hospitalization of VMC children increased year by year, adolescence might be a risk factor of VMC. The fatality of VMC inpatients could be up to 4.13%, and the death led to a huge economic burden of society, family and individuals.

Objective:To investigate the etiological distribution of severe community-acquired pneumonia (CAP) in children in Hebei Province.Methods:The nasopharyngeal swab samples and clinical data from 314 children with severe pneumonia were retrospectively analyzed between January 2014 and January 2016.Results:Among the 314 children, 298 (94.94%) showed positive result for pathogens in their nasopharyngeal swab samples, with 246 cases (78.34%) of multiple pathogens, predominantly mixed viruses and bacteria (206 cases, 65.61%). A total of 848 strains of pathogens were detected, including 483 strains (56.96%) of viruses, predominantly respiratory syncytial virus with 97 strains of subtype A and 86 strains of B. Bacteria and atypical pathogens ( Mycoplasma pneumoniae) had 365 strains (43.04%), mainly Streptococcus pneumoniae (220 strains) and Haemophilus influenzae (119 strains). The detection rate of pathogens was higher in children under one year of age ( χ2=21.389, P<0.001). There were no significant differences in different seasons, but the detection rates of respiratory syncytial virus A, respiratory syncytial virus B, and Streptococcus pneumoniae were higher in autumn, winter, and spring, respectively, with statistically significant differences ( χ2=22.205, P<0.001; χ2=37.874, P<0.001; χ2=11.380, P=0.009). Conclusions:Nasopharyngeal swab sample testing in children with severe pneumonia typically shows a coexistence of viral and bacterial pathogens, with detection rates varying among different age groups and seasons. Nasopharyngeal swab sample testing for pathogens provides valuable references for the identification of clinical pathogens.

Objective To observe the value of residual neural network(ResNet)-101-feature pyramid network(FPN)model based on CT for differentiating benign and malignant lung nodules.Methods Totally 2 040 lung nodules in 2 000 patients were retrospectively enrolled,including 1 150 benign and 890 malignant nodules.The nodules were divided into training set(n=1 632)and test set(n=408)at the ratio of 8∶2,the former including 881 benign and 751 malignant ones,while the latter including 269 benign and 139 malignant ones,respectively.Taken ResNet-101 as the backbone network,combined with FPN,a classification model was established based on chest CT,and the efficiency of this model alone and combined with evaluation of physicians for differentiating benign and malignant lung nodules were evaluated.Results Among 269 benign lung nodules in test set,ResNet-101-FPN model alone correctly diagnosed 214 nodules(214/269,79.55%),while combined with evaluation of physicians correctly diagnosed 230 ones(230/269,85.50%).For 139 malignant nodules in test set,ResNet-101-FPN model alone correctly diagnosed 124 nodules(124/139,89.21%),while combined with evaluation of physicians correctly diagnosed 131 ones(131/139,94.24%).The sensitivity,accuracy and precision of ResNet-101-FPN model combined with evaluation of physicians for distinguishing benign and malignant lung nodules were all higher,while the specificity of the combination was lower than those of ResNet-101-FPN model alone,but the differences were not significant(all P＞0.05).Conclusion ResNet-101-FPN model could be used to distinguish benign and malignant lung nodules based on CT.Combining with evaluation of physicians could improve diagnostic efficiency of this model.