Acute poisoning is a common chief complaint leading to emergency department visits and hospital admissions in developing countries such as Iran. Data describing the epidemiology of different poisonings, characteristics of the clinical presentations, and the predictors of outcome are lacking. Such data can help develop more efficient preventative and management strategies to decrease morbidity and mortality related to these poisonings. This manuscript describes the epidemiology of acute poisoning among patients admitted to the intensive care unit (ICU) in Birjand, Iran. This retrospective, cross-sectional study was conducted to characterize acute poisonings managed in the ICU during a 7-year period from March 2010 to March 2017 in a single center in Birjand, Iran. Patient characteristics, suspected exposure, the route of exposure, and outcome data were collected from hospital medical records. During the study period, 267 (64% male and 36% female) patients met inclusion criteria. Pharmaceutical medication (36.6%), opioids (26.2%) followed by pesticides (13.9%) were the most common exposures 38.2% of these cases were identified as suicide attempts. There were different frequencies in terms of xenobiotic exposure in relation to gender (p = 0.04) and the survival (p = 0.001). There was a significant difference between various xenobiotics identified as the cause of poisoning (p = 0.001). Mortality rate in our study was 19.5%. The incidence of outcomes was significantly higher in patients poisoned with opioids, pesticides, benzodiazepines, and tricyclic antidepressants (p < 0.05). The median length of hospital stay was higher in pesticide-poisoned patients (p = 0.04). Opioids and pesticides were the most common exposures. The mortality rate of the poisoned patients in the ICU was proportionately high. The mortality rate due to opioid poisoning is a major concern and the most significant cause death due to poisoning in the region. Further monitoring and characterization of acute poisoning in Birjand, Iran is needed. These data can help develop educational and preventative programs to reduce these exposures and improve management of exposures in the prehospital and hospital settings.

Objective.The present study uses simulated data to find what the optimal number of response categories is to achieve adequate power in ordinal logistic regression (OLR) model for differential item functioning (DIF) analysis in psychometric research.Methods.A hypothetical ten-item quality of life scale with three, four, and five response categories was simulated. The power and type I error rates of OLR model for detecting uniform DIF were investigated under different combinations of ability distribution (θ), sample size, sample size ratio, and the magnitude of uniform DIF across reference and focal groups.Results.Whenθwas distributed identically in the reference and focal groups, increasing the number of response categories from 3 to 5 resulted in an increase of approximately 8% in power of OLR model for detecting uniform DIF. The power of OLR was less than 0.36 when ability distribution in the reference and focal groups was highly skewed to the left and right, respectively.Conclusions.The clearest conclusion from this research is that the minimum number of response categories for DIF analysis using OLR is five. However, the impact of the number of response categories in detecting DIF was lower than might be expected.

Introduction: Emotional intelligence (EI) constitutes a whole set of non-cognitive capabilities, competencies, and skills that affect one’s ability to deal successfully with environmental demands and pressures. Different factors such as gender, age, education, place of residence, etc. can influence this variable. Nevertheless, the influence of a multitude of factors involved in behavioral phenomena cannot often be controlled. Purpose: Therefore, some difficulty may often raise in finding associations between these variables using regression models as regression models are built on restrictive assumptions. Methods: In these cases, models such as artificial neural networks are excellent alternatives to regression models. In this study, the neural network model was used in SPSS software to predict the pattern held among the variables of age, gender, occupation, marital status, and education for predicting the EI of 901 individuals aged from 17 to 73 years. Results: The appropriate neural network model for EI prediction is a hyperbolic tangent transfer function with two neaurons in the hidden layer and a sigmoid transfer function in the output layer. This network was able to predict EI in most of its dimentions with significant correlations and could demonstrate the neural network’s advantage over regression models in predicting EI using sociological variables. Conclusion: This model is able to estimate the EI level in different occupational, educational, gender, and age groups, and provide the ground for planning to address potential deficiencies in each group.

To evaluate aqueous and lipid tear film parameters and the meibomian glands (MGs) with non-contact meibography in patients with vitiligo. This case-control study was conducted in the right (OD) and left (OS) eyes of 43 patients with vitiligo and 43 controls in Birjand, Iran. In addition to demographic information and skin disease characteristics, the Ocular Surface Disease Index (OSDI) questionnaire was completed for each patient, followed by eye examinations including slit lamp examination, Schirmer test, strip meniscometry (SMTube), and tear break-up time (TBUT) measurement. The MGs were also imaged using a non-contact meibography system (SBM System, Italy). The data were analyzed using SPSS version 22.0 with a significant level of less than 0.05. Patients had higher OSDI score than controls but it was not significant (10.90±13.03 vs. 5.57±6.85; p=0.07). There were significant differences between the groups in mean Schirmer test values for both eyes (OD: 8.07±5.47 vs. 17.37±6.52; OS: 7.60±5.00 vs. 17.30±6.44, p<0.001) and mean SMTube results (OD: 4.49±2.40 vs. 9.74±3.67; OS: 4.30±2.81 vs. 9.65±4.52; p<0.001). However, mean TBUT did not differ between the groups (OD: 9.14±3.17 vs. 10.12±2.08, p=0.27; OS: 9.16±3.30 vs. 10.05±2.10, p=0.25). Meibography also showed no significant difference in MG dropout between the groups (OD: 20.86±9.79 vs. 21.05±12.07; p=0.74; OS: 18.16±8.83 vs. 19.53±10.30; p=0.51). Vitiligo is associated with a reduction in the production of aqueous tear film, but does not affect the structure and function of the MGs.