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: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.

Diabetic nephropathy(DN)is one of the most important complications of diabetes.Its pathogenesis is com-plex and has not been fully elucidated.Epithelial-mesenchymal transition(EMT)plays an important role in the development of DN.Relevant data show that glycogen synthesis kinase-3β(GSK-3β)participates in the process of EMT through multiple sig-naling pathways and affects the occurrence and progression of DN.This article reviews the research progress of GSK-3β in-volved in EMT in DN.

Objective:To investigate the predictive value of serum uric acid/albumin ratio (sUAR) for acute kidney injury (AKI) after cardiac valve surgery.Methods:The clinical data of adult patients undergoing cardiac valve surgery under cardiopulmonary bypass from January 2021 to December 2021 from the Heart Center of Henan Provincial People's Hospital were collected retrospectively, and the sUAR was calculated. All patients were divided into AKI group and non-AKI group according to whether AKI occurred within 7 days after cardiac valve surgery, and the differences of clinical data between the two groups were compared. Multivariate logistic regression model was used to analyze the independent correlation factors of AKI after cardiac valve surgery. The receiver operating characteristic (ROC) curve was used to evaluate the performance of relevant indicators.Results:A total of 422 patients were enrolled, including 194 females (46.0%), 141 hypertension patients (33.4%) and 172 atrial fibrillation patients (40.8%). They were 57 (50, 65) years old. Their sUAR was 8.13 (6.57, 9.54) μmol/g, and hemoglobin was 135 (125, 145) g/L. There were 142 cases in AKI group and 280 cases in non-AKI group, and the incidence of AKI after cardiac valve surgery was 33.6%. Age, atrial fibrillation rate, baseline serum creatinine, N terminal pro B type natriuretic peptide, serum urea,serum uric acid, blood glucose and sUAR were higher in the AKI group than those in the non-AKI group (all P<0.05), and estimated glomerular filtration rate, lymphocyte count,hemoglobin and serum albumin were lower in the AKI group than those in the non-AKI group (all P<0.05). The median cardiopulmonary bypass time of patients in the AKI group was slightly longer than that in the non-AKI group, but the difference was not statistically significant [159 (125, 192) min vs. 151 (122, 193) min, Z=-0.797, P=0.426], and there were no statistically significant differences in other indicators between the two groups. The results of multivariate logistic regression analysis showed that sUAR ( OR=1.467, 95% CI 1.308-1.645, P<0.001), age ( OR=1.045, 95% CI 1.020-1.072, P<0.001), atrial fibrillation ( OR=2.520, 95% CI 1.580-4.020, P<0.001), hemoglobin ( OR=0.984, 95% CI 0.971-0.997, P=0.015) were the independent correlation factors. ROC curve analysis showed that the area under the curve ( AUC) of sUAR predicting AKI after cardiac valve surgery was 0.710 (95% CI 0.659-0.760, P<0.001) with a sensitivity of 85.2% and specificity of 45.0% for the sUAR cut-off point of 7.28 μmol/g. The AUC for the diagnosis of AKI after cardiac valve surgery was 0.780 (95% CI 0.734-0.825, P<0.001) with a sensitivity of 72.5% and specificity of 71.8% for the combination of sUAR with age, hemoglobin and atrial fibrillation. Conclusions:For patients undergoing cardiac valve surgery under cardiopulmonary bypass, preoperative high sUAR is an independent risk factor for postoperative AKI, and sUAR has a certain predictive value for postoperative AKI.

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 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 explore the risk factors of poor renal prognosis in patients with coronary artery bypass surgery (CABG)-associated acute kidney injury (AKI), and establish a preliminary clinical risk prediction model for chronic kidney disease (CKD) progression in CABG-associated AKI patients, and evaluate its predictive efficacy.Methods:It was a retrospective, observational cohort study. The study subjects were patients who underwent CABG at Central China Fuwai Hospital from January 1, 2018 to December 31, 2020, with a baseline estimated glomerular filtration rate (eGFR)>60 ml·min -1·(1.73 m 2) -1 and postoperative complication of AKI. The patients were followed up for 90 days after discharge from hospital. The endpoint event was defined as progression to CKD after 90 days of the occurrence of CABG-associated AKI. The patients were divided into CKD group and non-CKD group based on whether they experienced endpoint events. The baseline clinical data were compared between the two groups. The logistic regression model was used to analyze the risk factors of endpoint event. The receiver-operating characteristic curve was drawn to evaluate the performance of the clinical risk prediction model for predicting poor renal prognosis in CABG-associated AKI patients. Results:A total of 124 CABG-associated AKI patients were enrolled in the study, including 95 males and 29 females, aged (62.57±9.61) years old. Thirty-eight patients (30.8%) progressed to new-onset CKD 90 days after CABG-associated AKI. Compared with non-CKD group, CKD group had lower preoperative hemoglobin ( t=2.778, P=0.006) and baseline eGFR ( t=3.603, P<0.001), higher proportion of women ( χ2=10.714, P=0.001), and higher preoperative blood NT-proBNP ( Z=-2.150, P=0.030) and discharged serum creatinine ( Z=-5.290, P<0.001). The multivariate logistic regression analysis results revealed that female ( OR=5.179, 95% CI 1.535-17.477, P=0.008), high preoperative blood NT-proBNP ( OR=1.049, 95% CI 1.004-1.095, P=0.032), low baseline eGFR ( OR=0.928, 95% CI 0.889-0.968, P=0.001), and high serum creatinine at discharge ( OR=1.019, 95% CI 1.009-1.029, P<0.001) were independent influencing factors of CABG-associated AKI to CKD. The clinical risk prediction model including female, preoperative blood NT-proBNP, preoperative baseline eGFR, and serum creatinine at discharge produced a moderate performance for predicting CABG-associated AKI to CKD ( AUC=0.872, 95% CI 0.806-0.939, P<0.001). Conclusion:Patients with CABG-associated AKI are at high risks for new-onset CKD. Female, preoperative high NT-proBNP, preoperative low baseline eGFR and high serum creatinine at discharge can help identify patients with a high risk of CABG-associated AKI to CKD.

Humans ; Vaccines, Inactivated ; COVID-19/prevention & control* ; Vaccination ; Viral Vaccines ; Immunity ; Antibodies, Viral

Background::Transplant renal artery stenosis (TRAS) is a vascular complication after kidney transplantation associated with poor outcomes. This study aimed to analyze the efficacy and safety of low-dose aspirin for preventing TRAS.Methods::After kidney transplantation, patients were enrolled from January 2018 to December 2020 in Henan Provincial People’s Hospital. A total of 351 enrolled recipients were randomized to an aspirin group with low-dose intake of aspirin in addition to standard treatment ( n = 178), or a control group with only standard treatment ( n = 173). The patients was initially diagnosed as TRAS (id-TRAS) by Doppler ultrasound, and confirmed cases were diagnosed by DSA (c-TRAS). Results::In the aspirin and control groups, 15.7% (28/178) and 22.0% (38/173) of the recipients developed id-TRAS, respectively, with no statistical difference. However, for c-TRAS, the difference of incidence and cumulative incidence was statistically significant. The incidence of c-TRAS was lower in the aspirin group compared with the control group (2.8% [5/178] vs. 11.6% [20/173], P = 0.001). Kaplan–Meier estimates and Cox regression model identified the cumulative incidence and hazard ratio (HR) of TRAS over time in two groups, showing that recipients treated with aspirin had a significantly lower risk of c-TRAS than those who were not treated (log-rank P = 0.001, HR = 0.23, 95% confidence interval [CI]: 0.09–0.62). The levels of platelet aggregation rate ( P < 0.001), cholesterol ( P = 0.028), and low-density lipoprotein cholesterol ( P = 0.003) in the aspirin group were decreased compared with the control group in the third-month post-transplantation. For the incidence of adverse events, there was no statistical difference. Conclusion::Clinical application of low-dose aspirin after renal transplant could prevent the development of TRAS with no significant increase in adverse effects.Trial Registration::Clinicaltrials.gov, NCT04260828.

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.