Objective:To investigate the potential utility of the lymphocyte to monocyte ratio (LMR) and its modified index, albumin-LMR product, as predictive biomarkers for disease activity and presence of lupus nephritis in systemic lupus erythematosus (SLE).Methods:A total of 264 patients with newly diagnosed SLE who were treated at Henan Provincial People′s Hospital between December 2016 and September 2022 were included in this study. Their clinical data were subsequently collected for analysis. Patients were classified into non-active disease group (SLEDAI<5, n=55) and active disease group(SLEDAI≥5, n=209) based on the SLE SLEDAI. The Mann-Whitney U test was employed to compare the differences in clinical parameter levels, LMR and albumin-LMR product between the two groups. Additionally, the active disease group was further stratified into mild(SLEDAI 5~9, n=86), moderate(SLEDAI 10~14, n=96) and severe (SLEDAI≥15, n=27) subgroups to assess differences in LMR and albumin-LMR product. Further more patients were stratified into two groups based on renal involvement: those without lupus nephritis(non-LN) and those with lupus nephritis (LN). For non-parametric comparisons, the Mann-Whitney U test was used for intergroup comparisons between two groups, while the Kruskal-Wallis H test was applied for comparisons among three groups. If the Kruskal-Wallis H test revealed statistically significant differences ( P<0.05), pairwise comparisons were performed using the Mann-Whitney U test, and the significance level was adjusted using the Bonferroni method to account for multiple testing.Correlations between LMR, albumin-LMR product, and disease activity indicators were analyzed using Spearman′s correlation. The diagnostic value of LMR and albumin-LMR product for SLE activity was evaluated using the receiver operating characteristic (ROC) curves. Results:Both LMR and albumin-LMR product were significantly lower in active disease group compared to the non-active group {LMR:3.56 (2.15, 5.00) vs. 5.68 (3.89, 7.00); albumin-LMR product [93.21 (59.50, 143.98)g/L] vs. [187.89 (137.67, 260.90)]g/L, Z=-5.68, -7.05, P<0.001 for all}. Further subgroup analysis revealed that LMR and albumin-LMR product levels in severe, moderate, and mild active disease were also significantly decreased compared to the non-active disease group {LMR:3.83(1.78, 5.09)、3.09(2.06, 4.90)、3.65(2.45, 5.03) vs. 5.68(3.89, 7.00); albumin-LMR product: [95.69(66.57, 121.61)]g/L、[79.82(49.02, 126.91)]g/L、[104.73(69.21, 169.01)]g/L vs. [187.89(137.67, 260.90)]g/L, H=34.27, 58.29, P<0.001 for all}. A significant disparity in the levels of LMR and albumin-LMR product was detected between the non-LN and LN, with statistical significance ( Z=-3.44, P=0.001 and Z=-7.06, P<0.001). Correlation analysis indicated that LMR negatively correlated with SLEDAI( r=-0.31), urea( r=-0.29), creatinine ( r=-0.28) and 24-hour urinary protein level ( r=-0.27), all P<0.001, with no significant correlation to complement C3 or C4. Albumin-LMR product showed stronger negative correlations with SLEDAI ( r=-0.44), urea ( r=-0.40), creatinine ( r=-0.37), and 24-hour urinary protein ( r=-0.55), all P<0.001, and a positive correlation with complement C3 ( r=0.18, P=0.004). The areas under the ROC curves for LMR and LMR combined with complement C3 were 0.749 and 0.795, respectively, while for albumin-LMR product and its combination with complement C3, they were 0.809 and 0.833, indicating superior diagnostic efficacy for the modified albumin-LMR product. Conclusion:LMR and albumin-LMR product levels are significantly associated with SLE disease activity and may serve as potential biomarkers for assessing SLE activity and the degree of 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.

Objective To investigate the application value of pan-immune-inflammation value(PIV),hypersensitive C-reactive protein(hs-CRP)/albumin(ALB)ratio(HCAR)and procalcitonin/platelet count ratio(PCT/PLT)in pulmonary infection in elderly patients with chronic obstructive pulmonary disease(COPD).Methods A total of 143 elderly patients with COPD and pulmonary bacterial infection were selected as the infected group.Meanwhile,143 elderly patients with COPD and without pulmonary infection were selected as the uninfected group.Patients in the infected group were furthrer divided into the mild group(47 cases),the moderate group(51 cases)and the severe group(45 cases)according to the degree of pulmonary infection.They were divided into the favorable prognosis group(112 cases)and the poor prognosis group(31 cases)according to the prognosis.Blood biochemical indicators,PIV,HCAR and PCT/PLT were compared between groups.The relationship between above indicators and pulmonary infection,infection degree and the prognosis was analyzed.Results Compared with the uninfected group,neutrophil(NEU),monocyte(MON),hs-CRP and PCT levels were higher,while lymphocyte(LYM)and ALB levels were lower in the infected group(P＜0.05).PIV,HCAR and PCT/PLT were higher in the infected group and the poor prognosis group than those in the uninfected group and the favorable prognosis group,respectively(P＜0.05).PIV,HCAR and PCT/PLT in the mild group,the moderate group and the severe group increased in sequence(P＜0.05).Spearman correlation analysis showed that PIV,HCAR and PCT/PLT were positively correlated with the degree of pulmonary infection(P＜0.05).Multivariate Logistic regression analysis showed that high levels of PIV,HCAR and PCT/PLT were independent risk factors for pulmonary infection in elderly patients with COPD(P＜0.05),and also independent risk factors for poor prognosis in patients with pulmonary infection(P＜0.05).The areas under the curve(AUC)of PIV combined with HCAR and PCT/PLT for diagnosing pulmonary infection in elderly patients with COPD and predicting poor prognosis in patients with pulmonary infection were 0.980 and 0.910(P＜0.05).Conclusion PIV,HCAR and PCT/PLT are related to COPD with pulmonary infection in the elderly.They can help to identify pulmonary infection,judge the condition of pulmonary infection and evaluate the prognosis in patients with pulmonary infection.

Objective To investigate the application value of pan-immune-inflammation value(PIV),hypersensitive C-reactive protein(hs-CRP)/albumin(ALB)ratio(HCAR)and procalcitonin/platelet count ratio(PCT/PLT)in pulmonary infection in elderly patients with chronic obstructive pulmonary disease(COPD).Methods A total of 143 elderly patients with COPD and pulmonary bacterial infection were selected as the infected group.Meanwhile,143 elderly patients with COPD and without pulmonary infection were selected as the uninfected group.Patients in the infected group were furthrer divided into the mild group(47 cases),the moderate group(51 cases)and the severe group(45 cases)according to the degree of pulmonary infection.They were divided into the favorable prognosis group(112 cases)and the poor prognosis group(31 cases)according to the prognosis.Blood biochemical indicators,PIV,HCAR and PCT/PLT were compared between groups.The relationship between above indicators and pulmonary infection,infection degree and the prognosis was analyzed.Results Compared with the uninfected group,neutrophil(NEU),monocyte(MON),hs-CRP and PCT levels were higher,while lymphocyte(LYM)and ALB levels were lower in the infected group(P＜0.05).PIV,HCAR and PCT/PLT were higher in the infected group and the poor prognosis group than those in the uninfected group and the favorable prognosis group,respectively(P＜0.05).PIV,HCAR and PCT/PLT in the mild group,the moderate group and the severe group increased in sequence(P＜0.05).Spearman correlation analysis showed that PIV,HCAR and PCT/PLT were positively correlated with the degree of pulmonary infection(P＜0.05).Multivariate Logistic regression analysis showed that high levels of PIV,HCAR and PCT/PLT were independent risk factors for pulmonary infection in elderly patients with COPD(P＜0.05),and also independent risk factors for poor prognosis in patients with pulmonary infection(P＜0.05).The areas under the curve(AUC)of PIV combined with HCAR and PCT/PLT for diagnosing pulmonary infection in elderly patients with COPD and predicting poor prognosis in patients with pulmonary infection were 0.980 and 0.910(P＜0.05).Conclusion PIV,HCAR and PCT/PLT are related to COPD with pulmonary infection in the elderly.They can help to identify pulmonary infection,judge the condition of pulmonary infection and evaluate the prognosis in patients with pulmonary infection.

Objective:To investigate the potential utility of the lymphocyte to monocyte ratio (LMR) and its modified index, albumin-LMR product, as predictive biomarkers for disease activity and presence of lupus nephritis in systemic lupus erythematosus (SLE).Methods:A total of 264 patients with newly diagnosed SLE who were treated at Henan Provincial People′s Hospital between December 2016 and September 2022 were included in this study. Their clinical data were subsequently collected for analysis. Patients were classified into non-active disease group (SLEDAI<5, n=55) and active disease group(SLEDAI≥5, n=209) based on the SLE SLEDAI. The Mann-Whitney U test was employed to compare the differences in clinical parameter levels, LMR and albumin-LMR product between the two groups. Additionally, the active disease group was further stratified into mild(SLEDAI 5~9, n=86), moderate(SLEDAI 10~14, n=96) and severe (SLEDAI≥15, n=27) subgroups to assess differences in LMR and albumin-LMR product. Further more patients were stratified into two groups based on renal involvement: those without lupus nephritis(non-LN) and those with lupus nephritis (LN). For non-parametric comparisons, the Mann-Whitney U test was used for intergroup comparisons between two groups, while the Kruskal-Wallis H test was applied for comparisons among three groups. If the Kruskal-Wallis H test revealed statistically significant differences ( P<0.05), pairwise comparisons were performed using the Mann-Whitney U test, and the significance level was adjusted using the Bonferroni method to account for multiple testing.Correlations between LMR, albumin-LMR product, and disease activity indicators were analyzed using Spearman′s correlation. The diagnostic value of LMR and albumin-LMR product for SLE activity was evaluated using the receiver operating characteristic (ROC) curves. Results:Both LMR and albumin-LMR product were significantly lower in active disease group compared to the non-active group {LMR:3.56 (2.15, 5.00) vs. 5.68 (3.89, 7.00); albumin-LMR product [93.21 (59.50, 143.98)g/L] vs. [187.89 (137.67, 260.90)]g/L, Z=-5.68, -7.05, P<0.001 for all}. Further subgroup analysis revealed that LMR and albumin-LMR product levels in severe, moderate, and mild active disease were also significantly decreased compared to the non-active disease group {LMR:3.83(1.78, 5.09)、3.09(2.06, 4.90)、3.65(2.45, 5.03) vs. 5.68(3.89, 7.00); albumin-LMR product: [95.69(66.57, 121.61)]g/L、[79.82(49.02, 126.91)]g/L、[104.73(69.21, 169.01)]g/L vs. [187.89(137.67, 260.90)]g/L, H=34.27, 58.29, P<0.001 for all}. A significant disparity in the levels of LMR and albumin-LMR product was detected between the non-LN and LN, with statistical significance ( Z=-3.44, P=0.001 and Z=-7.06, P<0.001). Correlation analysis indicated that LMR negatively correlated with SLEDAI( r=-0.31), urea( r=-0.29), creatinine ( r=-0.28) and 24-hour urinary protein level ( r=-0.27), all P<0.001, with no significant correlation to complement C3 or C4. Albumin-LMR product showed stronger negative correlations with SLEDAI ( r=-0.44), urea ( r=-0.40), creatinine ( r=-0.37), and 24-hour urinary protein ( r=-0.55), all P<0.001, and a positive correlation with complement C3 ( r=0.18, P=0.004). The areas under the ROC curves for LMR and LMR combined with complement C3 were 0.749 and 0.795, respectively, while for albumin-LMR product and its combination with complement C3, they were 0.809 and 0.833, indicating superior diagnostic efficacy for the modified albumin-LMR product. Conclusion:LMR and albumin-LMR product levels are significantly associated with SLE disease activity and may serve as potential biomarkers for assessing SLE activity and the degree of 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.

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

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.