Short-term reproductive outcomes analysis and prediction of the modified uterine stent treatment for mild to moderate intrauterine adhesions: experience at a single institution.

BACKGROUND: To evaluate the efficacy of modified uterine stent in the treatment of mild-to-moderate intrauterine adhesions and explore the relative indicators affecting prognosis prediction. METHODS: A total of 115 patients with mild-to-moderate intrauterine adhesions received a modified uterine stent placement after hysteroscopy adhesiolysis. The second-look hysteroscopy operated after 3 months surgery, and the third-look hysteroscopy operated after 6 months surgery if necessary. The stent was removed when the cavity shape was repaired, then the reproductive outcomes were followed up one year. RESULTS: Menstrual blood volume, endometrial thickness and volume had increased significantly after 3 months surgery. The rates of cavity repaired were 86.96% (100/115) after 3 months surgery and 100% (115/115) after 6 months surgery cumulatively. Endometrial thickness after 3-months surgery was positively associated with uterine cavity shape repaired (P<0.01). The receive operating characteristic (ROC) curve showed the rate of uterine cavity shape repaired predicted by the model was 0.92, based on the endometrial thickness after 3-months surgery. The rate of pregnancy was 86.09% (99/115) in one year, while the rate of miscarriage accounted for 26.26% (26/99). The median time interval between stent removal and subsequent conception was 3 months. It showed adhesion recurrence was the risk factor for subsequent pregnancy (P<0.01). CONCLUSIONS: A modified uterine stent placement under hysteroscopy was an effective approach for mild-to-moderate intrauterine adhesions, which is easy to operate and worthy for clinical promotion. Endometrial thickness measured by ultrasonography probably has predictive value in adhesion recurrence and subsequent pregnancy. TRIAL REGISTRATION: ChiCTR2100051524. Date of registration (retrospectively registered): 26/09/2021.

The influence of social participation and depressive symptoms on cognition among middle-aged and older adults.

Background: The global aging phenomenon has raised concerns about the cognitive abilities of older individuals. This study aimed to explore the relationship between social participation, depressive symptoms, and cognitive function among middle-aged and older adults. Methods: This study utilized data from the China Longitudinal Study of Health and Retirement (CHARLS) from wave 1 to wave 4. We used linear regression and generalized estimation equations to investigate the correlation between social participation, depressive symptoms, and cognitive function. Moreover, three models were constructed by adjusting covariates, and we used the sobel test and bootstrap method to analyze the mediating effects of depressive symptoms on social activities and cognitive function. Results: The results of both linear regression and generalized estimation equation showed that social participation had a positive correlation with cognitive function (P < 0.05), and the impact of social participation on cognition increased with the number of social activity types. Meanwhile, depressive symptoms had a negative association with cognitive function (P < 0.05). Furthermore, there was no interaction between social participation and depressive symptoms on cognitive function. Finally, after adjusting the model, social participation could affect cognitive function by affecting depressive symptoms (P < 0.05). Conclusion: The study emphasizes the mediating role of depressive symptoms in the relationship between social participation and cognitive function. Notably, no interaction was observed between social participation and depressive symptoms. These findings highlight the potential of active social participation in reducing depressive symptoms and enhancing cognitive function in middle-aged and older adults.

Development and validation of a nomogram model for predicting the risk of venous thromboembolism in lymphoma patients undergoing chemotherapy: a prospective cohort study conducted in China.

BACKGROUND: The mechanism of Venous thromboembolism (VTE) is complicated and difficult to prevent due to factors such as bone marrow invasion, therapy, and immune-mediated effects. This study aims to establish a nomogram model for predicting the risk of thrombosis in lymphoma patients undergoing chemotherapy, which has been increasing over the past 30 years. METHODS: The data of lymphoma patients from the Affiliated Cancer Hospital of Chongqing University in China between 2018 and 2020 were analyzed. This included age, sex, body mass index, ECOG score, histological type, Ann Arbour Stage, white blood cells count, haemoglobin level, platelet count, D-dimer level, and chemotherapy cycle. Univariate and multivariate cox analysis was used to determine the risk factors for VTE. Characteristic variables were selected to construct a nomogram model which was then evaluated using ROC curve and calibration. RESULTS: Age, sex, PLT, D-dimer and chemotherapy cycle were considered as independent influencing factors of VTE. The mean (standard deviation) of the C index, AUC and Royston D statistics of 1000 cross-validations of the Nomogram model were 0.78 (0.01), 0.81 (0.01) and 1.61(0.07), respectively. It indicates a good calibration degree and applicability value as shown by the calibration curve. The DCA curve showed a rough threshold range of 0.05-0.60 with a good model. CONCLUSIONS: We have established and validated a nomogram model for predicting the risk of thrombosis in lymphoma patients. This model can assess the risk of thrombosis in each individual patient, enabling the identification of high-risk groups and targeted preventive treatment.

Development and validation of nomogram prognostic model for predicting OS in patients with diffuse large B-cell lymphoma: a cohort study in China.

This study comprehensively incorporates pathological parameters and novel clinical prognostic factors from the international prognostic index (IPI) to develop a nomogram prognostic model for overall survival in patients with diffuse large B-cell lymphoma (DLBCL). The aim is to facilitate personalized treatment and management strategies. This study enrolled a total of 783 cases for analysis. LASSO regression and stepwise multivariate COX regression were employed to identify significant variables and build a nomogram model. The calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve were utilized to assess the model's performance and effectiveness. Additionally, the time-dependent concordance index (C-index) and time-dependent area under the ROC curve (AUC) were computed to validate the model's stability across different time points. The study utilized 8 selected clinical features as predictors to develop a nomogram model for predicting the overall survival of DLBCL patients. The model exhibited robust generalization ability with an AUC exceeding 0.7 at 1, 3, and 5 years. The calibration curve displayed evenly distributed points on both sides of the diagonal, and the slopes of the three calibration curves were close to 1 and statistically significant, indicating high prediction accuracy of the model. Furthermore, the model demonstrated valuable clinical significance and holds the potential for widespread adoption in clinical practice. The novel prognostic model developed for DLBCL patients incorporates readily accessible clinical parameters, resulting in significantly enhanced prediction accuracy and performance. Moreover, the study's use of a continuous general cohort, as opposed to clinical trials, makes it more representative of the broader lymphoma patient population, thus increasing its applicability in routine clinical care.

Causal association between major depressive disorder and coronary heart disease: a two-sample bidirectional mendelian randomization study.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous mental illness and a major public health problem worldwide. A large number of observational studies have demonstrated a clear association between MDD and coronary heart disease (CHD), and some studies have even suggested that the relationship is bidirectional. However, it was unknown whether any causal relationship existed between them and whether causality was bidirectional in such an instance. Thus, we aimed to determine whether there is a bidirectional causal relationship between major depressive disorders and coronary heart disease. METHODS: Our two-sample Bidirectional Mendelian Randomization Study consisted of two parts: forward MR analysis regarded MDD as exposure and CHD as the outcome, and reverse MR analysis considered CHD as exposure and MDD as the outcome. Summary data on MDD and CHD were obtained from the IEU Open GWAS database. After screening criteria(P < [Formula: see text]), 47 MDD-associated SNPs and 39 CHD-associated SNPs were identified. The inverse-variance weighted (IVW) method, ME-Egger regression, and weighted median method were used to estimate causality. In addition, sensitivity methods, including the heterogeneity test, horizontal pleiotropy test, and leave-one-out method, were applied to ensure the robustness of causal estimation. RESULTS: Based on the MR-Egger regression intercept test results, there did not appear to be any horizontal pleiotropy in this study (MDD: intercept = -0.0000376, P = 0.9996; CHD: intercept = -0.0002698, P = 0.920). Accordingly, IVW results suggested consistent estimates of causal effect values. The results showed that people with MDD increased the risk of CHD by 14.7% compared with those without MDD (OR = 1.147, 95%CI: 1.045-1.249, P = 0.009). But there was no direct evidence that CHD would increase the risk of MDD(OR = 1.008, 95%CI: 0.985-1.031, P = 0.490). The heterogeneity test and funnel plot showed no heterogeneity in 47 SNPs of MDD (Q = 42.28, [Formula: see text]=0, P = 0.629), but there was heterogeneity in 39 SNPs of CHD (Q = 62.48, [Formula: see text]=39.18%, P = 0.007). The leave-one-out method failed to identify instances where a single SNP was either biased toward or dependent on the causation. CONCLUSION: Our study supports a one-way causal relationship between MDD and CHD, but there is no bidirectional causal relationship. MDD increases the risk of CHD, but there is no evidence that CHD increases the risk of MDD. Therefore, the influence of psychological factors should also be considered in the prevention and treatment of CHD. For MDD patients, it is necessary to prevent cardiovascular diseases.

Public health insurance and the risk of cancer-specific mortality in patients with cervical cancer: A Chinese prospective cohort study.

Objective: Cervical cancer has one of the highest incidence and mortality rates of any malignant tumor of the female reproductive tract, and its longer treatment period will place significant financial strain on patients and their families. Little is known about how health insurance policies influence cervical cancer prognosis, particularly in developing countries. The relationship between cervical cancer specific death and cervical cancer all-cause mortality with public health insurance, self-payment rate, and the combined effect of public health insurance and self-payment rate was investigated in this study. Materials and methods: From 2015 to 2019, a prospective longitudinal cohort study on cervical cancer was carried out in Chongqing, China. We chose 4,465 Chongqing University Cancer Hospital patients who had been diagnosed with cervical cancer between 2015 and 2019. The self-payment rate and public health insurance are taken into account in our subgroup analysis. After applying the inclusion and exclusion criteria, we describe the demographic and clinical traits of patients with various insurance plans and self-payment rates using the chi-square test model. The relationship between cervical cancer patients with various types of insurance, the self-payment rate, and treatment modalities is examined using the multivariate logistic regression model. After applying the inclusion and exclusion criteria, we summarize the demographic and clinical traits of patients with various insurance plans and self-payment rates using the chi-square test model. The association between cervical cancer patients with various types of insurance, the self-payment rate, and treatment modalities is examined using the multivariate logistic regression model. The cumulative hazard ratio of all-cause death and cervical cancer-specific mortality for various insurance types and self-payment rates was then calculated using the Cox proportional hazard model and the competitive risk model. Results: This study included a total of 3,982 cervical cancer patients. During the follow-up period (median 37.3 months, 95% CI: 36.40-38.20), 774 deaths were recorded, with cervical cancer accounting for 327 of them. Patients who obtained urban employee-based basic medical insurance (UEBMI) had a 37.1% lower risk of all-cause death compared to patients who received urban resident-based basic medical insurance (URBMI) (HRs = 0.629, 95% CI: 0.508-0.779, p = 0.001). Patients with a self-payment rate of more than 60% had a 26.9% lower risk of cervical cancer-specific mortality (HRs = 0.731, 95% CI: 0.561-0.952, p <0.02). Conclusions: The National Medical Security Administration should attempt to include the more effective self-paid anti-tumor medications into national medical insurance coverage within the restrictions of restricted medical insurance budget. This has the potential to reduce not only the mortality rate of cervical cancer patients, but also their financial burden. High-risk groups, on the other hand, should promote cervical cancer screening awareness, participate actively in the state-led national cancer screening project and enhance public awareness of HPV vaccine. This has the potential to reduce both cervical cancer patient mortality and the financial burden and impact.

Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients.

Stomach cancer (GC) has one of the highest rates of thrombosis among cancers and can lead to considerable morbidity, mortality, and additional costs. However, to date, there is no suitable venous thromboembolism (VTE) prediction model for gastric cancer patients to predict risk. Therefore, there is an urgent need to establish a clinical prediction model for VTE in gastric cancer patients. We collected data on 3092 patients between January 1, 2018 and December 31, 2021. And after feature selection, 11 variables are reserved as predictors to build the model. Five machine learning (ML) algorithms are used to build different VTE predictive models. The accuracy, sensitivity, specificity, and AUC of these five models were compared with traditional logistic regression (LR) to recommend the best VTE prediction model. RF and XGB models have selected the essential characters in the model: Clinical stage, Blood Transfusion History, D-Dimer, AGE, and FDP. The model has an AUC of 0.825, an accuracy of 0.799, a sensitivity of 0.710, and a specificity of 0.802 in the validation set. The model has good performance and high application value in clinical practice, and can identify high-risk groups of gastric cancer patients and prevent venous thromboembolism.