BackgroundFinasteride is a competitive inhibitor of 5 alpha-reductase enzyme, and is used for treatment of benign prostatic hyperplasia and androgenetic alopecia. Animal studies have shown that finasteride might induce behavioral changes. Additionally, some cases of finasteride-induced depression have been reported in humans. The purpose of this study was to examine whether depressive symptoms or anxiety might be induced by finasteride administration.MethodsOne hundred and twenty eight men with androgenetic alopecia, who were prescribed finasteride (1 mg/day) were enrolled in this study. Information on depressed mood and anxiety was obtained by Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). Participants completed BDI and HADS questionnaires before beginning the treatment and also two months after it.ResultsMean age of the subjects was 25.8(± 4.4) years. At baseline, mean BDI and HADS depression scores were 12.11(± 7.50) and 4.04(± 2.51), respectively. Finasteride treatment increased both BDI (p < 0.001) and HADS depression scores significantly (p = 0.005). HADS anxiety scores were increased, but the difference was not significant (p = 0.061).ConclusionThis preliminary study suggests that finasteride might induce depressive symptoms; therefore this medication should be prescribed cautiously for patients with high risk of depression. It seems that further studies would be necessary to determine behavioral effects of this medication in higher doses and in more susceptible patients.

IL-6 and TNF-alpha proinflammatory cytokine gene polymorphisms could change individual susceptibility to IBS and might have a role in pathophysiology of disease.

Purpose: With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell carcinoma (RCC) by applying a residual convolutional neural network (ResNet) on routine MR imaging.Experimental Design: Preoperative MR images (T2-weighted and T1-postcontrast sequences) of 1,162 renal lesions definitely diagnosed on pathology or imaging in a multicenter cohort were divided into training, validation, and test sets (70:20:10 split). An ensemble model based on ResNet was built combining clinical variables and T1C and T2WI MR images using a bagging classifier to predict renal tumor pathology. Final model performance was compared with expert interpretation and the most optimized radiomics model.Results: Among the 1,162 renal lesions, 655 were malignant and 507 were benign. Compared with a baseline zero rule algorithm, the ensemble deep learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, P ¼ 0.004). Compared with all experts averaged, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.60, P ¼ 0.053), sensitivity (0.92 vs. 0.80, P ¼ 0.017), and specificity (0.41 vs. 0.35, P ¼ 0.450). Compared with the radiomics model, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.62, P ¼ 0.081), sensitivity (0.92 vs. 0.79, P ¼ 0.012), and specificity (0.41 vs. 0.39, P ¼ 0.770).Conclusions: Deep learning can noninvasively distinguish benign renal tumors from RCC using conventional MR imaging in a multiinstitutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.