Objective To analyze the deployment of policy instruments and the distribution of stakeholder engagement in provincial policies on nursing assistant training and management.Methods The relevant policy texts on nursing assistant training and management were systematically searched and collected from the official websites of provincial governments and their direct departments,CNKI,and the PKU Law Database.A two-dimensional framework of policy instruments-stakeholder was constructed,and the content analysis was used to classify,encode and quantify policy clauses.Results The study encompassed 20 provincial-level nursing assistant training and management policies,yielding a total of 359 codes.Within the policy instruments dimension,environmental,supply,and demand instruments constituted 66.30％,28.97％,and 4.74％,respectively.In terms of stakeholders,the management-side accounted for 56.55％,providers and trainers for 18.11％each,partners for 5.01％,and demand-side for 2.23％.Both management-side and trainers engaged with 3 policy instruments,providers with 2,and partners and demand-side with one each.Conclusion In the provincial nursing assistant training and management policies,there are differences in the deployment of policy instruments,and the distribution of stakeholders is uneven.Managers should pay attention to publicity guidance and platform construction,improve incentive mechanisms and training programs,and innovate cooperation models with stakeholders,as well as strengthen communication and exchange.

Objective:This study aimed to construct a prediction model for diagnosis of proliferative lupus nephritis based on a machine learning algorithm. Additionally, a user-friendly platform was developed to propose a non-invasive method to assist the pathologic classification of lupus nephritis.Methods:A retrospective analysis was conducted on clinical and pathological data of lupus nephritis patients confirmed by renal biopsy at Zhengzhou University People′s Hospital from January 2017 to August 2023. The study population was randomly divided into training and testing sets in a 7∶3 ratio. Utilizing six machine learning algorithms, classification models were developed. The predictive performance of each model was assessed using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The optimal model, once identified, was deployed as a web-based calculator for convenient model application. SPSS 25.0 and R 4.2.2 were used to analyze the data.Results:The study included a total of 212 patients, with 138 cases with proliferative lupus nephritis and 74 cases with non-proliferative lupus nephritis. The AUC values for the six models, namely logistic regression, decision tree, random forest, support vector machine, extreme gradient boosting, and light gradient boosting machine, were 0.79, 0.62, 0.79, 0.88, 0.81, and 0.77, respectively; the accuracy rates were 82.54%, 65.08%, 74.60%, 85.71%, 69.84%, 71.43%, respectively. Among them, the support vector machine model demonstrated the optimal performance. This model had deployed as a web-based calculator. Based on feature importance scores, the top 10 influencing factors were identified, including anti URNP antibody, immunoglobulin G, serum globulin, estimated glomerular filtration rate, anti Smith antibody, BMI index, anti dsDNA antibody, uric acid, anti-Rib.p antibody, and gender.Conclusion:A prediction model based on machine learning algorithms was successfully established, and a web calculator was developed to offer a simple and non-invasive method for diagnosing proliferative lupus nephritis. This can assist clinicians in evaluating the risk-benefit ratio of kidney biopsy in patients with lupus nephritis.

Objective To analyze the deployment of policy instruments and the distribution of stakeholder engagement in provincial policies on nursing assistant training and management.Methods The relevant policy texts on nursing assistant training and management were systematically searched and collected from the official websites of provincial governments and their direct departments,CNKI,and the PKU Law Database.A two-dimensional framework of policy instruments-stakeholder was constructed,and the content analysis was used to classify,encode and quantify policy clauses.Results The study encompassed 20 provincial-level nursing assistant training and management policies,yielding a total of 359 codes.Within the policy instruments dimension,environmental,supply,and demand instruments constituted 66.30％,28.97％,and 4.74％,respectively.In terms of stakeholders,the management-side accounted for 56.55％,providers and trainers for 18.11％each,partners for 5.01％,and demand-side for 2.23％.Both management-side and trainers engaged with 3 policy instruments,providers with 2,and partners and demand-side with one each.Conclusion In the provincial nursing assistant training and management policies,there are differences in the deployment of policy instruments,and the distribution of stakeholders is uneven.Managers should pay attention to publicity guidance and platform construction,improve incentive mechanisms and training programs,and innovate cooperation models with stakeholders,as well as strengthen communication and exchange.

Objective:This study aimed to construct a prediction model for diagnosis of proliferative lupus nephritis based on a machine learning algorithm. Additionally, a user-friendly platform was developed to propose a non-invasive method to assist the pathologic classification of lupus nephritis.Methods:A retrospective analysis was conducted on clinical and pathological data of lupus nephritis patients confirmed by renal biopsy at Zhengzhou University People′s Hospital from January 2017 to August 2023. The study population was randomly divided into training and testing sets in a 7∶3 ratio. Utilizing six machine learning algorithms, classification models were developed. The predictive performance of each model was assessed using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The optimal model, once identified, was deployed as a web-based calculator for convenient model application. SPSS 25.0 and R 4.2.2 were used to analyze the data.Results:The study included a total of 212 patients, with 138 cases with proliferative lupus nephritis and 74 cases with non-proliferative lupus nephritis. The AUC values for the six models, namely logistic regression, decision tree, random forest, support vector machine, extreme gradient boosting, and light gradient boosting machine, were 0.79, 0.62, 0.79, 0.88, 0.81, and 0.77, respectively; the accuracy rates were 82.54%, 65.08%, 74.60%, 85.71%, 69.84%, 71.43%, respectively. Among them, the support vector machine model demonstrated the optimal performance. This model had deployed as a web-based calculator. Based on feature importance scores, the top 10 influencing factors were identified, including anti URNP antibody, immunoglobulin G, serum globulin, estimated glomerular filtration rate, anti Smith antibody, BMI index, anti dsDNA antibody, uric acid, anti-Rib.p antibody, and gender.Conclusion:A prediction model based on machine learning algorithms was successfully established, and a web calculator was developed to offer a simple and non-invasive method for diagnosing proliferative lupus nephritis. This can assist clinicians in evaluating the risk-benefit ratio of kidney biopsy in patients with lupus nephritis.

Objective:To explore the effect of communication competence and psychological resilience on job burnout among Operating Room nurses.Methods:From March to June 2023, randomized clustering sampling was used to select 138 registered Operating Room nurses from four ClassⅢ Grade A hospitals in Taizhou for investigation. The survey was conducted using the general information questionnaire, Operating Room Nurses' Job Stressor Scale, Chinese version of the Connor-Davidson Resilience Scale, Nurses' Clinic Communication Competence Scale, and Maslach Burnout Inventory-General Survey. Hierarchical linear regression analysis was used to explore the effects of communication competence and psychological resilience on job burnout among Operating Room nurses.Results:A total of 138 questionnaires were sent out, and 133 valid questionnaires were collected, with a valid response rate of 96.38% (133/138). Among 133 Operating Room nurses, the job burnout score was (56.35±9.28), and the communication competence, psychological resilience, and work stress scale scores were (196.71±18.92), (78.09±18.31), and (96.37±22.47), respectively. Pearson correlation showed that job burnout among Operating Room nurses was negatively correlated with psychological resilience ( r=-0.475, P<0.01) and communication competence ( r=-0.241, P<0.01), and positively correlated with work stress ( r=0.360, P<0.01). Hierarchical linear regression analysis showed that, after controlling for other variables, psychological resilience and communication competence were the influencing factors of job burnout among Operating Room nurses ( P<0.01), which could explain 17.70% of the variation. Conclusions:The level of job burnout among Operating Room nurses is relatively high, and psychological resilience and communication competence are independent influencing factors. Managers can provide psychological counseling and support services for Operating Room nurses, offer communication competence training programs, and prevent and reduce job burnout among Operating Room nurses.

Objective To investigate the ability of combined baseline serum markers, i.e., HBV DNA, HBV RNA, HBsAg, and HBcrAg, to predict HBeAg seroconversion in patients with HBeAg-positive chronic hepatitis B (CHB) treated by nucleos(t)ide analogues. Methods A retrospective analysis was performed for 83 HBeAg-positive patients selected as subjects from the prospective CHB follow-up cohort established by Difficult & Complicated Liver Diseases and Artificial Liver Center, Beijing YouAn Hospital, Capital Medical University, from June 2007 to July 2008, and the baseline serum levels of HBV DNA, HBV RNA, HBsAg, and HBcrAg were analyzed. The t -test or the Mann-Whitney U test was used for comparison of continuous data between two groups, and the chi-square test was used for comparison of categorical data between two groups. The Spearman method was used for correlation analysis. A Cox regression model was established to calculate HBeAg seroconversion prediction score, and the time-dependent receiver operating characteristic curve was used to evaluate the ability of combined markers in predicting HBeAg seroconversion. The Kaplan-Meier method was used to calculate cumulative seroconversion rate in each group, and the Log-rank test was used for comparison between groups. Results For the 83 HBeAg-positive patients, the median follow-up time was 108 months, and 44.58%(37/83) of these patients achieved HBeAg seroconversion. Compared with the non-seroconversion group, the HBeAg seroconversion group had significantly lower baseline serum levels of HBV DNA [6.23(1.99-9.28) log 10 IU/mL vs 7.69(2.05-8.96) log 10 IU/mL, Z =-2.345, P =0.019] and HBV RNA [4.81(1.40-7.53) log 10 copies/mL vs 6.22(2.00-8.49) log 10 copies/mL, Z =-1.702, P =0.010], and there were no significant differences in the levels of HBsAg and HBcrAg between the two groups ( P > 0.05). The Cox regression equation constructed based on the above serum markers showed a median score of 0.95(range 0.37-3.45) for predicting HBeAg seroconversion. In the total population, the combined score was negatively correlated with HBsAg, HBV DNA, HBV RNA, and HBcrAg ( r =-0.697, -0.787, -0.990, and -0.819, all P < 0.001). Based on the median prediction score, the patients were divided into high HBeAg seroconversion group and low HBeAg seroconversion group; as for the prediction of HBeAg seroconversion rate at 36, 60, and 84 months, the high HBeAg seroconversion group had a seroconversion rate of 43.90%, 51.20%, and 63.10%, respectively, while the low HBeAg seroconversion group had a seroconversion rate of 9.60%, 17.00%, and 19.8%, respectively, and there was a significant difference between the two groups ( χ 2 =11.6, P < 0.001). Conclusion The combined prediction score based on baseline serum HBV markers can predict HBeAg seroconversion in CHB patients treated by nucleos(t)ide analogues.

Objective:To evaluate the diagnostic performance and clinical significance of SLE-DAS in the disease activity of SLE patients in China.Methods:The clinical data of 134 patients with SLE were collected. The disease activity was evaluated by SLE-DAS, SLEDAI-2000, BILAG-2004 and PGA scoring tools. Pearson test and Spearman test were used to analyze the correlation. The receiver operating characteristic curve (ROC curve) was used to evaluate SLE-DAS, and Kappa consistency test was adapted to assess the consistency of the two scoring methods.Results:One hundred and thirty-four patients with SLE, including 7 males and 127 females, aged 13-77 years, with an average of (35±13) years were included. Among them, renal involvement was 38.1%, skin mucosal involvement was 11.2%, musculoskeletal involvement was 8.2%, blood system involvement was 13.4%, heart and lung involvement was 2.2%, neuropsychiatric involvement was 1.5%, and multisystem involvement was 3.0%. SLE-DAS was positively correlated with CRP, ESR, anti-dsDNA antibody, urinary protein (24 h) level, SLEDAI-2000, BILAG-2004 and PGA ( r=0.25, 0.34, 0.47, 0.77, 0.93, 0.94, 0.95, P<0.01); SLE-DAS was negatively correlated with PLT, Hb, C3 and C4 ( r=-0.29, -0.43, -0.41, -0.32, P<0.01). When SLEDAI-2000>5 was used as a cut point for analyzing SLE-DAS, the results showed that the area under the curve (AUC) 95% CI of SLE-DAS was 0.961 (0.927,0.995), the Yoden index was 0.845. When the cut-off value was set up to 4.65( P<0.001), the sensitivity was 98.11%, the specificity was 86.42%, and the accuracy was 91.04%. Kappa consistency test showed that kappa value was 0.819( P<0.001). Conclusions:SLE-DAS can be used to evaluate the disease activity of SLE patients and can be used as the evidence to guide treatment plan in clinical practice.

Objective:To evaluate the correlation between anti-C1q antibody and disease activity and cellular immune function in patients with systemic lupus erythematosus (SLE).Methods:The clinical data and test indexes of 134 patients with SLE and 90 healthy people who were admitted to Henan Provincial People′s Hospital from June 2017 to February 2018 were collected. The level of anti-C1q antibody was measured by enzyme-linked immunosorbent assay (ELISA), and lymphocyte subsets were measured by flow cytometry. According to the score of Systemic Lupus Erythematosus Disease Activity Index (SLEDAI)-2K, SLE patients were divided into active and inactive groups, and SLE patients were divided into LN group and non-LN group according to the presence or absence of kidney involvement. The levels of anti-C1q antibodies and lymphocyte subsets were compared among the three groups, and correlations between anti-C1q antibodies and disease activity and lymphocytes were analyzed. The predictive value of anti-C1q antibodies and anti double stranded DNA (dsDNA) antibodies for SLE disease activity was evaluated.Results:The anti-C1q antibody level, percentage of T cells and Ts cells in SLE group were higher than those in control group, while the percentage of Th cells, percentage of NK cells, T cell count, Th cell count, B cell count and NK cell count in SLE group were lower than those in control group (all P<0.05); The anti-C1q antibody level in the active group was higher than that in the inactive group, and the counts of T cells, Ts cells, Th cells, B cells and NK cells were lower than those in the inactive group (all P<0.05); The anti-C1q antibody level in LN group was higher than that in non-LN group, and the T cell count, Ts cell count, Th cell count, B cell count, NK cell count were lower than that in non-LN group, with statistically significant difference (all P<0.05). Correlation analysis showed that age, hemoglobin (HB), C3, C4, T cell count, Th cell count, B cell count and NK cell count were negatively correlated with anti-C1q antibody, while SLEDAI-2K, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and anti-dsDNA antibody were positively correlated with anti-C1q antibody (all P<0.05). Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of anti-C1q antibody alone in predicting SLE disease activity was 0.702, with a sensitivity of 0.547 and a specificity of 0.827. The combination of anti-C1q and anti ds-DNA antibodies resulted in an AUC of 0.761, a sensitivity of 0.756, and a specificity of 0.691. The combined detection value of the two antibodies predicting SLE disease activity was better than the single detection. Conclusions:Anti-C1q antibody is closely related to disease activity and cellular immune dysfunction, and has certain predictive value in SLE disease activity.

Objective:To observe the clinical efficacy of angiotensin-receptor neprilysin inhibitors (ARNI) in the treatment of maintenance hemodialysis (MHD) with heart failure.Methods:The clinical data of heart failure patients who accepted MHD in Central China Fuwai Hospital were retrospectively collected. All patients accepted regular treatments of heart failure, and then the treatment group was treated with ARNI, while the control group was treated with valsartan. The treatment course was 6 months. The cardiac parameters: left ventricular ejection fraction (LVEF), left ventricular end-diastolic dimension (LVEDD), left ventricular end-systolic dimension (LVESD), pulmonary artery pressure, right ventricular end-diastolic dimension (RVED), right atrial end-diastolic dimension (RAED), N-terminal pro-B-type natriuretic peptide (NT-pro BNP), and serum potassium were collected and compared between the two groups. Multivariate ordered logistic regression analysis was adopted to analyze the influencing factors of treatment effect.Results:A total of 60 MHD patients with heart failure were enrolled with age of (53.92±11.88) years old, 37 males (61.7%), dialysis age of (27.83±12.92) months, and blood pressure of (154.22±15.27) mmHg/(85.43±12.31) mmHg. (1) There was no significant difference of the clinical data and cardiac parameters between the treatment group ( n=30) and the control group ( n=30) before treatment (all P>0.05); (2) After treatment of 6 months, the total effective rate [28/30(93.3%)] in the treatment group was significantly higher than that in the control group [20/30(66.7%)] and the rehospitalization rate [2/30(6.7%)] in the treatment group was significantly lower than that in the control group [10/30(33.3%)] (both P<0.05); (3) After treatment of 6 months, LVEF, LVEDD, LVESD, pulmonary artery pressure, RVED, RAED, NT-pro BNP, and blood pressure were all improved significantly compared with the baseline in both groups (all P<0.05) and there was no significant difference of serum potassium and body weight before and after treatment in the two groups (all P>0.05); (4) After treatment of 6 months, LVEF in the treatment group was higher than that in the control group and LVEDD, LVESD, pulmonary artery pressure, NT-pro BNP, and blood pressure in the treatment group were lower than those in the control group (all P<0.05). There was no significant difference of RVED, RAED, serum potassium and body weight between the two groups after treatment (all P>0.05); (5)The difference values before and after treatment of LVEF, LVEDD, LVESD, NT-pro BNP, body weight, systolic blood pressure, and diastolic blood pressure were different between the two groups (all P<0.05); (6)Therapy method ( β=-1.863, 95% CI -2.948-0.777, P=0.001) and residual urine ( β=-1.686, 95% CI -3.079- -0.293, P=0.018) were independent influencing factors of treatment effect (the treatment effect of ARNI was better than that of valsartan; the treatment effect of patients with normal urine volume was better than that of patients with oliguria and anuria). Conclusions:ARNI can effectively improve cardiac function in MHD patients with heart failure, inhibit ventricular remodeling, and improve disease prognosis.

Objective To investigate the consistency between Shengxiang (S) and Xinbo (X) real-time PCR methods in the quantification of HBV RNA. Methods In the prospective follow-up cohort of 108 chronic hepatitis B (CHB) patients established from July 2007 to August 2008, 20 patients with HBeAg seroconversion were selected, and 20 patients without seroconversion were selected by propensity score matching at a ratio of 1∶ 1. The two quantification methods from S and X companies were used, and a retrospective analysis was performed for HBV RNA in serum samples at baseline and weeks 12, 24, and 48. The paired t -test was used for comparison of normally distributed continuous data between groups, and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups; the chi-square test was used for comparison of categorical data. The Pearson correlation coefficient, intraclass correlation coefficient (ICC), and the Bland-Altman method were used to evaluate the consistency of the two quantification methods. Results A total of 132 serum samples were tested by S reagent, and 154 were tested by X reagent; the detection rate of HBV RNA was 100% by both reagents. A total of 131 serum samples were tested by both reagents, with 34 samples at baseline and 29, 35, and 33 samples, respectively, at weeks 12, 24, and 48 of follow-up; at these four time points, the HBV RNA quantification data detected by X reagent were significantly higher than those detected by S reagent (5.75±1.64/5.43±1.73/5.13±1.54/4.76±1.55 log 10 copies/mL vs 4.80±1.48/4.52±1.53/4.10±1.50/3.92± 1.43 log 10 copies/mL, t =8.348, t =5.341, Z =-5.086, Z =-4.762, all P < 0.001). The correlation analysis of the two methods showed a Pearson correlation coefficient of 0.915 (95% confidence interval [ CI ]: 0.836-0.957) and an ICC of 0.771(95% CI : -0.021 to 0.931) at baseline, a Pearson correlation coefficient of 0.849(95% CI : 0.701-0.927) and an ICC of 0.733(95% CI : 0.138-0.902) at week 12, a Pearson correlation coefficient of 0.951(95% CI : 0.905-0.975) and an ICC of 0.776(95% CI : -0.058 to 0.942) at week 24, and a Pearson correlation coefficient of 0.933(95% CI : 0.867-0.967) and an ICC of 0.804(95% CI : -0.014 to 0.944) at week 48 (all P < 0.05). The Bland-Altman analysis showed that the difference of 96.18%(126/131) samples tested by the two methods was within the mean difference±1.96 standard deviation. Conclusion HBV RNA quantification by X reagent is higher than that by S reagent, while the two real-time PCR quantification methods show a good consistency in CHB patients with HBeAg seroconversion and those without seroconversion.