Objective:To explore the impact of the two-way referral model on compliance and prognosis in patients with heart failure.Methods:This bidirectional cohort study enrolled chronic heart failure (CHF) patients treated at Qilu Hospital of Shandong University or designated primary hospitals between March 2018 and March 2022. Patients were categorized into two groups based on referral status: two-way referral group (participating in the referral model with≥1 follow-up visit at primary hospitals) and the core hospital group (receiving treatment and follow-up exclusively at Qilu Hospital). Baseline clinical characteristics were collected and compared between groups. Patients underwent followed-up, with primary endpoints including follow-up rate, drug (β-blockers, angiotension converting enzyme inhibitor (ACEI)/angiotensin Ⅱ receptor blockers (ARB)/angiotensin receptor-neprilysin inhibitor (ARNI), sodium-glucose cotransporter 2 inhibitors and mineralocorticoid receptor antagonists) utilization rate and target dose achievement rate. Secondary endpoints encompassed changes from baseline in left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDd), and N-terminal pro-brain natriuretic peptide (NT-proBNP), plus cardiovascular mortality and heart failure rehospitalization. Generalized linear mixed models analyzed longitudinal trends in LVEF, LVEDd, and NT-proBNP levels. Kaplan-Meier curves and Cox regression evaluated LVEF recovery rates, supplemented by subgroup analyses. Multivariate logistic regression was used to identify factors influencing target dose achievement rate for β-blockers and ACEI/ARB/ARNI therapies in CHF patients.Results:A total of 357 patients were enrolled, aged 53 (41, 63) years, including 256 males (71.7%). 157 patients were in the two-way referral group and 200 patients in the core hospital-treated group. Compared with the core hospital-treated group, the two-way referral group had lower baseline LVEF (28 (22, 34)% vs. 31 (23, 36)%, P=0.021) and systolic blood pressure (116 (104, 125) mmHg vs. 121 (109, 134) mmHg (1 mmHg=0.133 kPa), P=0.010). The 12-month follow-up rate of the two-way referral group was higher than the core hospital-treated group (73.8% vs. 56.0%, P=0.004). No significant between-group differences were observed in drug utilization rate of β-blockers, ACEI/ARB/ARNI, or sodium-glucose cotransporter 2 inhibitors during follow-up (all P>0.05), while mineralocorticoid receptor antagonists use showed a declining trend in both groups. Although the core hospital-treated group had higher target dose achievement rates for β-blockers (65.4% vs. 49.3%, P=0.042) and ACEI/ARB/ARNI (79.8% vs. 65.8%, P=0.046) than the two-way referral group, multivariate logistic regression indicated that the two-way referral model was not a negative predictor for these outcomes (all P>0.05). Both groups showed improved NT-proBNP, LVEDd, and LVEF from baseline (all P<0.001) with no significant difference in trends between groups (all P>0.05). There was no significant difference in the composite incidence (7.6% vs. 6.5%, P=0.674) and cumulative incidence (log-rank P=0.684) of cardiovascular death and heart failure rehospitalization at 12 months between two groups. Conclusion:The two-way referral model demonstrates advantages in improving medication adherence, drug utilization rates, and targetdoseachievement rates among CHF patients. This model not only promotes cardiac functional recovery but also reduces risks of cardiovascular mortality and heart failure rehospitalization, achieving comparable therapeutic and management outcomes to those observed in core hospital-treated patients.

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.

Objective:To explore the impact of the two-way referral model on compliance and prognosis in patients with heart failure.Methods:This bidirectional cohort study enrolled chronic heart failure (CHF) patients treated at Qilu Hospital of Shandong University or designated primary hospitals between March 2018 and March 2022. Patients were categorized into two groups based on referral status: two-way referral group (participating in the referral model with≥1 follow-up visit at primary hospitals) and the core hospital group (receiving treatment and follow-up exclusively at Qilu Hospital). Baseline clinical characteristics were collected and compared between groups. Patients underwent followed-up, with primary endpoints including follow-up rate, drug (β-blockers, angiotension converting enzyme inhibitor (ACEI)/angiotensin Ⅱ receptor blockers (ARB)/angiotensin receptor-neprilysin inhibitor (ARNI), sodium-glucose cotransporter 2 inhibitors and mineralocorticoid receptor antagonists) utilization rate and target dose achievement rate. Secondary endpoints encompassed changes from baseline in left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDd), and N-terminal pro-brain natriuretic peptide (NT-proBNP), plus cardiovascular mortality and heart failure rehospitalization. Generalized linear mixed models analyzed longitudinal trends in LVEF, LVEDd, and NT-proBNP levels. Kaplan-Meier curves and Cox regression evaluated LVEF recovery rates, supplemented by subgroup analyses. Multivariate logistic regression was used to identify factors influencing target dose achievement rate for β-blockers and ACEI/ARB/ARNI therapies in CHF patients.Results:A total of 357 patients were enrolled, aged 53 (41, 63) years, including 256 males (71.7%). 157 patients were in the two-way referral group and 200 patients in the core hospital-treated group. Compared with the core hospital-treated group, the two-way referral group had lower baseline LVEF (28 (22, 34)% vs. 31 (23, 36)%, P=0.021) and systolic blood pressure (116 (104, 125) mmHg vs. 121 (109, 134) mmHg (1 mmHg=0.133 kPa), P=0.010). The 12-month follow-up rate of the two-way referral group was higher than the core hospital-treated group (73.8% vs. 56.0%, P=0.004). No significant between-group differences were observed in drug utilization rate of β-blockers, ACEI/ARB/ARNI, or sodium-glucose cotransporter 2 inhibitors during follow-up (all P>0.05), while mineralocorticoid receptor antagonists use showed a declining trend in both groups. Although the core hospital-treated group had higher target dose achievement rates for β-blockers (65.4% vs. 49.3%, P=0.042) and ACEI/ARB/ARNI (79.8% vs. 65.8%, P=0.046) than the two-way referral group, multivariate logistic regression indicated that the two-way referral model was not a negative predictor for these outcomes (all P>0.05). Both groups showed improved NT-proBNP, LVEDd, and LVEF from baseline (all P<0.001) with no significant difference in trends between groups (all P>0.05). There was no significant difference in the composite incidence (7.6% vs. 6.5%, P=0.674) and cumulative incidence (log-rank P=0.684) of cardiovascular death and heart failure rehospitalization at 12 months between two groups. Conclusion:The two-way referral model demonstrates advantages in improving medication adherence, drug utilization rates, and targetdoseachievement rates among CHF patients. This model not only promotes cardiac functional recovery but also reduces risks of cardiovascular mortality and heart failure rehospitalization, achieving comparable therapeutic and management outcomes to those observed in core hospital-treated patients.

Background Grassroots medical and health personnel are an important component of China's public health system, and guaranteeing their physical and mental health will have a profound impact on the development of China's health service. Objective To identify potential influencing factors of occupational stress, anxiety, depression, and insomnia as well as their interactions. Methods In August 2021, a cross-sectional survey was conducted among all the staff (2675 medical and health personnel) at 7 secondary public hospitals and 8 health institutions in Xiong’an New Area of Hebei Province by cluster sampling. Occupational stress, anxiety, depression, and insomnia were evaluated by the Effort-Reward Imbalance Questionnaire (ERI), the Generalized Anxiety Disorder-7 (GAD-7), the Patient Health Questionnaire-9 (PHQ-9), and the Athens Insomnia Scale (AIS). The bnlearn and gmodels packages of R4.2.1 software were used to construct Bayesian networks on work and personal factors-occupational stress- health effects. The Bayesian network model was visualized by Netica32.0. Results Among the 3018 questionnaires distributed, a total of 2675 valid questionnaires were recovered, with an effective recovery rate of 88.63%. The positive rates of occupational stress, anxiety, depression, and insomnia were 51.48%, 62.13%, 62.50%, and 56.37%, respectively in the grassroots medical and health personnel. The positive rate of occupational stress among the medical and health workers varied by age, educational level, and organization type (P<0.05); the positive rate of anxiety varied by age, marital status, and daily working hours (P<0.05); and the positive rates of depression and insomnia varied by gender, educational level, and overcommitment (P<0.05). The final Bayesian network contained 14 nodes and 18 directed edges, and its prediction accuracy was 85.4%. .Job title, daily working hours, overcommitment and exercise directly associated with occupational stress, and other work and personal factors associated with occupational stress indirectly. Occupational stress associated with insomnia directly or indirectly via anxiety. Anxiety and insomnia associated with reporting depression. Conclusion Grassroots medical and health personnel of Xiong'an New Area, Hebei Province report high levels of occupational stress, anxiety, depression, and insomnia. Occupational stress directly or indirectly associates with anxiety, depression, and insomnia. Work and personal factors associate with occupational stress, and associate with anxiety, depression, and insomnia via occupational stress. Occupational stress may be an early warning factor of general work and personal factors affecting anxiety, depression, insomnia, and other mental health problems. Attention should be paid to the assessment and intervention of occupational stress in medical and health personnel to prevent mental health problems.

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.

Background::Alterations in the placental expression of glucose transporters (GLUTs), the crucial maternal-fetal nutrient transporters, have been found in women with hyperglycemia in pregnancy (HIP). However, there is still uncertainty about the underlying effect of the high-glucose environment on placental GLUTs expression in HIP.Methods::We quantitatively evaluated the activity of mammalian target of rapamycin (mTOR) and expression of GLUTs (GLUT1, GLUT3, and GLUT4) in the placenta of women with normal pregnancies (CTRL, n = 12) and pregnant women complicated with poorly controlled type 2 diabetes mellitus (T2DM, n = 12) by immunohistochemistry. In addition, BeWo cells were treated with different glucose concentrations to verify the regulation of hyperglycemia. Then, changes in the expression of GLUTs following the activation or suppression of the mTOR pathway were also assessed using MHY1485/rapamycin (RAPA) treatment or small interfering RNA (siRNA)-mediated silencing approaches. Moreover, we further explored the alteration and potential upstream regulatory role of methyltransferase-like 3 (METTL3) when exposed to hyperglycemia. Results::mTOR, phosphorylated mTOR (p-mTOR), and GLUT1 protein levels were upregulated in the placenta of women with T2DM compared with those CTRL. In BeWo cells, mTOR activity increased with increasing glucose concentration, and the expression of GLUT1, GLUT3, and GLUT4 as well as GLUT1 cell membrane translocation were upregulated by hyperglycemia to varying degrees. Both the drug-mediated and genetic depletion of mTOR signaling in BeWo cells suppressed GLUTs expression, whereas MHY1485-induced mTOR activation upregulated GLUTs expression. Additionally, high glucose levels upregulated METTL3 expression and nuclear translocation, and decreasing METTL3 levels suppressed GLUTs expression and mTOR activity and vice versa. Furthermore, in METTL3 knockdown BeWo cells, the inhibitory effect on GLUTs expression was eliminated by activating the mTOR signaling pathway using MHY1485. Conclusion::High-glucose environment-induced upregulation of METTL3 in trophoblasts regulates the expression of GLUTs through mTOR signaling, contributing to disordered nutrient transport in women with HIP.

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.

Pregnancy ; Female ; Humans ; Fetal Blood/metabolism* ; DNA Methylation/genetics* ; Epigenesis, Genetic ; Placenta/metabolism* ; Exercise Therapy