Background: Liver diseases, mainly hepatitis B and C, commonly occur in patients with end-stage renal diseases (ESRD). Alanine and aspartate aminotransferase are important for the diagnosis and monitoring of liver diseases. Several studies demonstrated that patients with chronic kidney disease (CKD) have lower levels of serum aminotransferases than the normal population. The present study was designed to compare these enzymes in different types of dialysis in ESRD patients and the general population in Iran. Methods: In this cross-sectional study, ESRD patients who were candidates for organ transplants in Montaserieh Hospital in Mashhad (Iran) from 2007 to 2014 were enrolled. The data of 1116 patients were collected by reviewing their medical records. Patients were divided into two groups of hemodialysis (n = 1034) and peritoneal dialysis (n = 82); their liver enzymes were compared with 510 healthy individuals from the MASHAD study. Results: There was a significant difference between hemodialysis and peritoneal dialysis patients and the control group regarding the age (P < 0.0001) and gender (P = 0.005). Conclusions: The reduction in serum aminotransferase levels in ESRD cases compared to the control group suggested that renal failure influences liver enzymes that were mildly increased in peritoneal dialysis versus hemodialysis patients in samples provided before the dialysis session.

Background: Colorectal cancer (CRC) is the third prevalent cancer worldwide, and it includes 10% of all cancer mortality. In Iran, men and women have the third and the fourth incidence rate of CRC, respectively. Survival analysis methods deal with data that measure the time until an event occurs. Artificial neural networks (ANN) and Cox regression are methods for survival analysis. Objectives: The current study was designated to determine related factors to CRC patients' survival using ANN and Cox regression. Methods: In this historical cohort, information of patients who were diagnosed with CRC in Omid Hospital of Mashhad was collected. A total of 157 subjects were investigated from 2006 to 2011 and were followed up until 2016. In ANN, data were divided into two groups of training and testing, and the best neural network architecture was determined based on the area under the ROC curve (AUC). Cox regression model was also fitted and the accuracy of these two models in survival prediction was compared by AUC. Results: The mean and standard deviation of age was 56.4 ± 14.6 years. The three-, five-and seven-year survival rates of patients were 0.67, 0.62, and 0.58, respectively. Using test dataset, the area under curve was estimated 0.759 for the chosen model in ANN and 0.544 for Cox regression model. Conclusions: In this study, ANN is an appropriate approach for predicting CRC patients' survival which was superior to Cox regression. Thus, it is recommended for predicting and also determining the influence of risk factors on patients' survival.

Background: Depression is one of the most common mental disorders and it has the third rank of the cause of disability and has been considered to increase the years of life with disability in Iran. Objectives: The purpose of this study was to map the geographical distribution and find hot spots of depression and its relation to demographic and socioeconomic factors in Mashhad. Methods: A population-based cross-sectional study was conducted in Mashhad in 2010. In this study, 9704 individuals aged 35 to 65 years old were evaluated using Beck's depression inventory-II. A generalized linear mixed model with a logit link was fitted for the spatial modeling of depression. R and GIS software was used for spatial analysis and disease mapping, respectively. Results: The prevalence of depression was different in geographical areas, ranging from 13.29% to 26.67%. The spatial correlation in the prevalence of depression was significant. The fitted spatial model showed that the spatial adjusted associations between gender (P < 0.001), marital status (P < 0.001), socioeconomic status (P < 0.001), and depression were significant. Conclusions: The significant spatial correlation shows that depression is spatially contagious and it is important to find its hot spots in the population. Thus developing health policy for prevention, early diagnostics, and treatment programs is preferred in these resource-limited areas.

Purpose Accurate assessment of visual field (VF) trend may help clinicians devise the optimum treatment regimen. This study was conducted to investigate the behavior of VF sequences using pointwise and region-wise linear, exponential, and sigmoid regression models. Materials and Methods In a retrospective cohort study, 277 eyes of 139 patients with glaucoma who had been followed for at least 7 years were investigated. Linear, exponential, and sigmoid regression models were fitted for each VF test location and Glaucoma Hemifield Test (GHT) region to model the trend of VF loss. The model with the lowest root mean square error (RMSE) was selected as the best fit. Results The mean age (standard deviation [SD]) of the patients was 59.9 years (9.8) with a mean follow-up time of 9.3 (0.7) years. The exponential regression had the best fit based on pointwise and region-wise approaches in 39.3% and 38.1% of eyes, respectively. The results showed a better performance based on sigmoid regression in patients with initial VF sensitivity threshold greater than 22 dB (71.6% in pointwise and 62.2% in region-wise approaches). The overall RMSE of the region-wise regression model was lower than the overall RMSE of the pointwise model. Conclusions In the current study, nonlinear regression models showed a better fit compared to the linear regression models in tracking VF loss behavior. Moreover, findings suggest region-wise analysis may provide a more appropriate approach for assessing VF deterioration. Translational Relevance Findings may confirm a nonlinear progression of VF deterioration in patients with glaucoma.

Background: Hemodialysis (HD) patients face long-term complications which require ongoing dialysis and follow-up. The management of hypertension among HD populations has often been neglected. This study aimed at identifying the determinants of death in hypertensive HD (HTN-HD) patients. Methods: In a multicenter retrospective cohort study (conducted from 2005 to 2018 in thirty-four HD centers affiliated with Shiraz University of Medical Sciences), the data of 725 HTN-HD patients who had at least 3 months of regular dialysis and follow-up were analyzed. Accelerated failure time mixture split-population (AFTMSP) regression was utilized to identify the factors with significant effects on long- and short-term overall survival (OS) separately. Results: Among the different AFTMSP models, the extended generalized gamma (EGG) model outperformed the others. Sex (male: event time ratio [ETR]=1.30), initial vascular access type (arteriovenous fistula: ETR=1.50), and the type of membrane flux used for HD (high-flux: ETR=1.27) had important impacts on short-term OS. Moreover, age (OR=1.06), dialysis adequacy (Kt/ Vurea≤1.2: OR=2.30), initial vascular access type (central venous catheter: OR=2.08), serum sodium (OR=0.90) concentration, and potassium (OR=0.66) concentration had significant effects on long-term OS. Conclusion: The split-population analyses were able to demonstrate that the predictors of long-term OS were different from those of short-term OS. Although the superiority of the parametric EGG model was proved in this study, further research with different databases is suggested. Moreover, these findings can be considered by health policy decision-makers to create a new guideline to enhance the long-term OS of HTN-HD patients.