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Background: Diarrhea-dominant irritable bowel syndrome (IBS-D) is a deliberating and chronic condition that can impair social activities. Determining proper medication with satisfactory outcomes has been a challenge. The 5-hydroxytryptamine 3 receptor antagonist (5-HT3 RA) drugs have demonstrated favorable outcomes on IBS-D in the last 3 decades. Ondansetron, also a 5-HT3 RA is known as an antiemetic. Our aim was to evaluate the efficacy of ondansetron in IBS-D. Methods: In this single-center, double-blind, randomized controlled trial, patients with IBS-D were recruited. Patients were randomized on a 1:1 ratio and assigned into two groups: imipramine 25 mg/daily plus ondansetron 4 mg/3 times per day and imipramine 25 mg/daily plus placebo. The primary endpoint was the frequency of diarrhea per day after 8 weeks of treatment. The secondary endpoints consisted of changes in the frequency of defecation urgency per day, the number of days with gastrointestinal pain and bloating, and the patients' overall satisfaction regarding bowel habits after 8 weeks of the treatment. Results: Data from 98 patients were analyzed. Ondansetron, compared to placebo, improved the primary outcome, and the stool consistency was increased significantly (3.29 ± 1.19 vs 4.55 ± 1.17, P < 0.001). Moreover, the response rate for the diarrhea frequency was significantly higher in the ondansetron group compared to the placebo (77.5% vs 34.7%, P < 0.001). In the ondansetron group, fewer urgencies were experienced, and pain severity and feeling of bloating declined as well (P < 0.01). Conclusion: Ondansetron can mitigate almost all IBS-D-related symptoms, which may indicate it as a drug of choice; however, further evidence is required to ascertain its safety. [ABSTRACT FROM AUTHOR]

Background & Objective: Machine learning and artificial intelligence are useful tools to analyze data with multiple variables. It has been shown that the prediction models obtained by Machine learning have better performance than the conventional statistical methods. This study was aimed to assess the risk factors and determine the best machine learning prediction model/s for in-hospital mortality among patients who underwent prosthetic valve replacement surgery. Materials & Methods: In this retrospective cross-sectional study, patient’s preoperative, intra-operative and post-operative data underwent univariate analysis. Feature importance determination was carried out using algorithms including principal component analysis (PCA), support vector machine (SVM), random forest (RF) model-based, and recursive feature elimination (RFE). Then, 13 machine learning classifiers were implemented for in-hospital prediction model. Results: The In-hospital mortality rate was 6.36%. Data from 2455 patients underwent final analysis. The machine learning results revealed that among preoperative features, Adaptive boost (AB) and RF classifiers (AUC: 0.82±0.033; 0.78±0.028, respectively); among intra-operative features, AB and K-nearest neighbors (KNN) classifiers (AUC: 0.68±0.014); among postoperative features, AB and RF classifiers (AUC: 0.9±0.1; 0.88±0.095, respectively); and among all features, AB and LR classifiers (AUC: 0.93±0.049; 0.93±0.055, respectively) had the best performance in prediction of in-hospital mortality. Conclusion: The AB classifier was determined as the best model in prediction of in-hospital mortality in all 4 datasets. [ABSTRACT FROM AUTHOR]

Shone syndrome is a rare congenital cardiac abnormality; however, many of the cases remain undiagnosed until early and middle adulthood. Different imaging modalities are used to assess the related structural abnormalities. In this case study, we report a 32-year-old woman who was planning her first pregnancy. In light of her childhood heart problems, in addition to a history of extended penicillin prescriptions for several years, she was referred for complementary assessments. At the time of presentation, she was asymptomatic. Imaging results showed several structural obstructive left-sided lesions and pulmonary artery hypertension. Ultimately, the patient was diagnosed with congenital shone syndrome, which was initially misdiagnosed. Shone complex in our case was presented in its full form, which could be potentially fatal in case of pregnancy. [ABSTRACT FROM AUTHOR]

Objectives This study aimed to investigate the effect of Crataegus monogyna (C. monogyna) on controlling the blood pressure in patients with hypertension along with sleep disorder. Methods This was a parallel double-blind, placebo-controlled trial study from May, 2018 to December, 2019. Total of 60 patients with hypertension and sleep disorder were randomly assigned to C. monogyna and placebo groups. The groups received C. monogyna and placebo capsules twice-daily for 8 weeks. The primary outcomes were the changes of systolic and diastolic blood pressure and Pittsburgh questionnaire's scores from baseline measurements. The secondary outcomes were changes in serum biochemical markers from baseline. Results Before treatment, there was no significant difference between study groups in terms of demographic data and outcomes (P > 0.05) except body mass index (P = 0.02). After treatments the intervention group had a significant improvement in systolic and diastolic blood pressure compared to placebo ((118.14 ± 6.76 vs 129.76 ± 8.28, 95% CI: 7.54-15.69, P < 0.001 and 77.14 ± 5.32 vs 83.57 ± 6.73, 95% CI: 3.17-9.68, P < 0.001, respectively) analysis revealed no significant difference observed between study groups in terms of baseline characteristics, and pre- and posttreatment laboratory tests. Also, none of the groups had superiority in terms of Pittsburgh questionnaire's scores (P = 0.44); however, the intragroup analysis revealed a significant improvements in the intervention group (P = 0.001). Conclusion C. monogyna fruit extract as a supplementary medication has beneficial effects on controlling blood pressure and quality of sleep in patients with hypertension along with sleep disorders. [ABSTRACT FROM AUTHOR]

Background: Thrombotic and thromboembolic events are important causes of mortality and morbidity in patients with prosthetic heart valve. The aim of this study is to evaluate the factors that may contribute to prosthetic heart valve thrombosis. Methods: This was a cross-sectional study in Rajaie Heart Center on patients with prosthetic heart valve malfunction, within a year. According to the echocardiographic and fluoroscopic findings, the patients were divided into two groups (thrombosis and non-thrombosis groups). The patients' demographic, clinical and laboratory data were recorded and analyzed with SPSS software. Results: A total of 142 patients participated in this study. Ninety-four patients (66.2%) were diagnosed with thrombosis. There was a significant relationship between thrombosis and inadequate anti-coagulation (international normalized rati [INR] <2.5) (odds ratio [OR]: 4.15, 95% CI: 1.98-9.87, P = 0.003), history of infection (OR: 12.81, 95% CI: 3.52-19.02, P < 0.001), prothrombin time (PT) check interval (OR: 2.38, 95% CI: 1.63-8.47, P = 0.019), atrial fibrillation (AF) rhythm (OR: 3.96, 95% CI: 1.75-8.09, P = 0.019), and plasma fibrinogen level (OR: 6.90, 95% CI: 2.58-14.69). Conclusion: Based on this study, inadequate anti-coagulation, AF rhythm, recent infection and plasma fibrinogen level were the factors most contributing to prosthetic valve thrombosis. As there were many cases of thrombosis in patients with history of infection, this factor can be considered for risk assessment in prosthetic valve. [ABSTRACT FROM AUTHOR]