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Abstract Background Pulmonary tuberculosis (TB) is a contagious bacterial infection caused by Mycobacterium tuberculosis that primarily affects the lungs. Despite advances in treatment, TB remains a major public health challenge, particularly in developing countries. This retrospective cohort study aimed to identify factors that influence the time to relapse in patients with pulmonary tuberculosis. Methods All smear-positive pulmonary tuberculosis patients (1548 patients) whose data were recorded in the tuberculosis registration system of Golestan University of Medical Sciences and Health Services from March 2014 to March 2019, and followed up until February 2021. To analyze the data, we applied LASSO, MCP, and SCAD penalized Cox models. Results Based on the AIC and BIC criteria, the MCP penalized model was better than the LASSO and SCAD penalized models. The variables of age at diagnosis (> 35 vs. $$\le$$ ≤ 35, HR = 2.77), marital status (married vs. single, HR = 8.49), chronic renal failure (Yes vs. No, HR = 5.36), and pre-treatment smear test results (+ 1 vs. 1-9Basil, HR = 0.65 and + 2 vs. 1-9Basil, HR = 0.96) influenced time to relapse according to the MCP penalized Cox regression model. Conclusions Health care systems should focus on identification of factors that influence time to relapse and developing interventions to reduce relapse rates.

Abstract Background Predicting mortality and relapse in children with acute lymphoblastic leukemia (ALL) is crucial for effective treatment and follow-up management. ALL is a common and deadly childhood cancer that often relapses after remission. In this study, we aimed to apply and evaluate machine learning-based models for predicting mortality and relapse in pediatric ALL patients. Methods This retrospective cohort study was conducted on 161 children aged less than 16 years with ALL. Survival status (dead/alive) and patient experience of relapse (yes/no) were considered as the outcome variables. Ten machine learning (ML) algorithms were used to predict mortality and relapse. The performance of the algorithms was evaluated by cross-validation and reported as mean sensitivity, specificity, accuracy and area under the curve (AUC). Finally, prognostic factors were identified based on the best algorithms. Results The mean accuracy of the ML algorithms for prediction of patient mortality ranged from 64 to 74% and for prediction of relapse, it varied from 64 to 84% on test data sets. The mean AUC of the ML algorithms for mortality and relapse was above 64%. The most important prognostic factors for predicting both mortality and relapse were identified as age at diagnosis, hemoglobin and platelets. In addition, significant prognostic factors for predicting mortality included clinical side effects such as splenomegaly, hepatomegaly and lymphadenopathy. Conclusions Our results showed that artificial neural networks and bagging algorithms outperformed other algorithms in predicting mortality, while boosting and random forest algorithms excelled in predicting relapse in ALL patients across all criteria. These results offer significant clinical insights into the prognostic factors for children with ALL, which can inform treatment decisions and improve patient outcomes.

Abstract Background Pregnant women experience several changes in their appearance, body shape and body image. In some studies, there has been a relationship between these changes and the type of delivery. This study aimed to investigate the relationship of the prenatal body image and genital image with the mode of delivery preferred by pregnant women in Gorgan in 2020. Methods In this cross-sectional study, 334 pregnant women were selected by stratified sampling. The Prenatal Body Image Questionnaire (PBIQ), Female Genital Self-Image Scale (FGSIS), pregnant women’s preferences for mode of delivery questionnaire (PPMDQ) and DASS-21 were completed on line. The data was analyzed using Spearman test and linear regression. Results The average score of PBIQ, FGSIS, and PPMDQ was 68.24 (standard deviation = 17.71), 19.25 (standard deviation = 3.3), and 63.12 (standard deviation = 3.3) respectively. Vaginal delivery as a preferred mode of delivery was inversely correlated with dissatisfaction with body image (r=-0.32, P