Behavior disorders in children with significant refractive errors.

Purpose: To evaluate the frequency of behavioral disorders in children with significant refractive error and to compare the results with those of emmetropic children.

Methods: In this prospective, comparative study from January to September 2013, refractive errors of all 5-12-year-old children who referred to a general eye clinic were recorded. A validated Persian version of the Rutter A scale was filled out by the parents for the evaluation of the child's behavioral disorders.

Tear Film Break-up Time in Bipolar Disorder.

Objective: Ocular dryness is a well-recognized adverse side effect of many topical and systemic medications. In psychiatry, patients who have consumed such drugs as lithium carbonate and sodium valproate frequently experience dry eye symptoms. The purpose of this study was to compare tear film stability between patients who use these drugs with those patients who are not on medication.

Determination of sensitivity and specificity of a novel gene dosage assay for prenatal screening of trisomy 21 syndrome.

Objective: To compare the gene dosage results achieved by a novel multiplex quantitative assay with cytogenetic and quantitative fluorescent polymerase chain reaction (QF-PCR) analysis for prenatal screening of trisomy 21 syndrome on corresponding fetal samples.

Design And Methods: Fetal samples (n=134) were collected from pregnant women considered high risk for having trisomy 21 affected fetus. Cytogenetic analysis and QF-PCR were performed.

Automatic classification of hyperactive children: comparing multiple artificial intelligence approaches.

Automatic classification of different behavioral disorders with many similarities (e.g. in symptoms) by using an automated approach will help psychiatrists to concentrate on correct disorder and its treatment as soon as possible, to avoid wasting time on diagnosis, and to increase the accuracy of diagnosis.

Designing a decision support system for distinguishing ADHD from similar children behavioral disorders.

In this study, a decision support system was designed to distinguish children with ADHD from other similar children behavioral disorders such as depression, anxiety, comorbid depression and anxiety and conduct disorder based on the signs and symptoms. Accuracy of classifying with Radial basis function and multilayer neural networks were compared. Finally, the average accuracy of the networks in classification reached to 95.