Assessing the risk of concurrent mycoplasma pneumoniae pneumonia in children with tracheobronchial tuberculosis: retrospective study.

Objective: This study aimed to create a predictive model based on machine learning to identify the risk for tracheobronchial tuberculosis (TBTB) occurring alongside Mycoplasma pneumoniae pneumonia in pediatric patients. Methods: Clinical data from 212 pediatric patients were examined in this retrospective analysis. This cohort included 42 individuals diagnosed with TBTB and Mycoplasma pneumoniae pneumonia (combined group) and 170 patients diagnosed with lobar pneumonia alone (pneumonia group). Three predictive models, namely XGBoost, decision tree, and logistic regression, were constructed, and their performances were assessed using the receiver's operating characteristic (ROC) curve, precision-recall curve (PR), and decision curve analysis (DCA). The dataset was divided into a 7:3 ratio to test the first and second groups, utilizing them to validate the XGBoost model and to construct the nomogram model. Results: The XGBoost highlighted eight significant signatures, while the decision tree and logistic regression models identified six and five signatures, respectively. The ROC analysis revealed an area under the curve (AUC) of 0.996 for XGBoost, significantly outperforming the other models (p < 0.05). Similarly, the PR curve demonstrated the superior predictive capability of XGBoost. DCA further confirmed that XGBoost offered the highest AIC (43.226), the highest average net benefit (0.764), and the best model fit. Validation efforts confirmed the robustness of the findings, with the validation groups 1 and 2 showing ROC and PR curves with AUC of 0.997, indicating a high net benefit. The nomogram model was shown to possess significant clinical value. Conclusion: Compared to machine learning approaches, the XGBoost model demonstrated superior predictive efficacy in identifying pediatric patients at risk of concurrent TBTB and Mycoplasma pneumoniae pneumonia. The model's identification of critical signatures provides valuable insights into the pathogenesis of these conditions.

Rituximab treatment for lupus nephritis: A systematic review.

PURPOSE: We used the Cochrane systematic review to analyze the effectiveness and safety of rituximab for lupus nephritis. METHODS: Systematic search was performed among Cochrane clinical controlled trials database, MEDLINE, MEDLINE-IN-Process and Other Non-Indexed Citations, EMBASE, EBSCO CINAHL, CNKI, VIP and Wanfang database from the establishment of the database to February 2016. The effectiveness and safety were evaluated in terms of the complete remission rate, total remission rate, urinary protein, Systemic Lupus Erythematosus Disease Activity Index changes and adverse events rate. Data were analyzed by the Review Manager Software version 5.3. RESULTS: Five RCTs that met the inclusion criteria, including a total of 238 patients, were enrolled in our study. The results showed that the complete remission rate in rituximab group was a significantly higher than that of cyclophosphamide group. The difference between the two groups was statistically significant (OR=2.80, 95%CI(1.08,7.26), P=0.03). But there was no significant difference between the two groups in partial and total remission rate. The complete remission rate, partial remission rate and total remission rate in rituximab treatment group was similar compared with mycophenolate mofetil group and rituximab combined with cyclophosphamide group. The adverse reaction rate was also similar among the groups. CONCLUSION: The study systematically analyzed the effectiveness and safety of rituximab for lupus nephritis, which suggested that the complete remission rate of rituximab in the treatment of lupus nephritis was a significantly higher than that of cyclophosphamide group, while the effectiveness and safety was of no difference compared with cyclophosphamide and mycophenolate mofetil.