Objective: A depressive disorder is an illness that involves the body, mood, thoughts and behaviors. This study was performed to identify the presence of depression among medical students of Urmia University of Medical Sciences.
Methods: A descriptive cross-sectional study was conducted on 700 undergraduate medical and basic sciences students. Beck depression inventory (BDI) used for data gathering.
Results: Mean score of BDI was 10.4 ± 0.8 and 52.6% of students scored under the depression threshold. Four of them had severe depression. RESULTS showed no significant relationship between depression and age, education, sex, rank of birth or duration of education.
Conclusion: Prevalence of depression that can affect the students' quality of education and social behavior was high in Urmia University of Medical Sciences.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3939964 | PMC |
Publication Analysis
Top Keywords
Similar Publications
Recent Advances in the Application of Engineered Exosomes from Mesenchymal Stem Cells for Regenerative Medicine.
Eur J Pharmacol
January 2025
Solid Tumor Research Center, Cellular and Molecular Medicine Research Institute, Urmia University of Medical Sciences, Urmia, Iran. Electronic address:
Exosomes, cell-derived vesicles produced by cells, are fascinating and drawing growing interest in the field of biomedical exploration due to their exceptional properties. There is fascinating evidence that exosomes are involved in major biological processes, including diseases and regeneration. Exosomes from mesenchymal stem cells (MSCs) have shown promising outcomes in regenerative medicine.
View Article and Find Full Text PDFHormonal disorders in autism spectrum disorders.
Horm Mol Biol Clin Investig
January 2025
Department of Biochemistry, Faculty of Medicine, 37555 Urmia University of Medical Sciences, Urmia, Iran.
Autism spectrum disorder (ASD) is a pervasive neurobehavioral condition characterized by disruption of behavioral and emotional patterns in individuals with this condition. Given that various environmental and genetic factors play a fundamental role in the pathophysiology of ASD, it can be said that ASD is a multifaceted disease. This study used scientific databases including Google Scholar, PubMed, Scopus, and Semantic Scholar.
View Article and Find Full Text PDFThe effect of mechanical milling for enhanced recycling Ti6Al4V powder from machining chips.
Sci Rep
January 2025
Department of Materials Science and Engineering, Inha University, Incheon, 22212, Korea.
This study investigates the optimization of mechanical milling parameters to enhance the recycling of Ti6Al4V machining chips, addressing a significant challenge in sustainable materials processing. The influence of ball-to-powder ratio (BPR) and ball size distribution on powder characteristics, including crystallite size, particle size, and phase composition, was systematically examined. Key findings include a 30% reduction in crystallite size, with the smallest crystallite size of 51.
View Article and Find Full Text PDFA novel electrochemical sensor for the determination of cadmium ions based on nitrogen-enriched carbon modified electrode.
Sci Rep
January 2025
Department of Nanotechnology, Faculty of Chemistry, Urmia University, Urmia, Iran.
In the present work, nitrogen-doped carbon was synthesized starting from a chitosan/urea mixture and immobilized at the surface of a bare glassy carbon electrode to detect Cd(II) ions using differential pulse-anodic stripping voltammetry method (DP-ASV). The synthesized nitrogen-doped carbon showed a significant potential for determining Cd(II) ions. Doping carbon with nitrogen atoms gives a structure with increased valence band energy, leading to acceleration of the electron transfer by creating an interaction of nitrogen's free electrons with Cd(II), which subsequently increases the peak current value.
View Article and Find Full Text PDFApplication of the Lasso regularisation technique in mitigating overfitting in air quality prediction models.
Sci Rep
January 2025
Department of Statistics, Faculty of Science, Fasa University, Fasa, 74616-86131, Iran.
As a significant global concern, air pollution triggers enormous challenges in public health and ecological sustainability, necessitating the development of precise algorithms to forecast and mitigate its impacts, which has led to the development of many machine learning (ML)-based models for predicting air quality. Meanwhile, overfitting is a prevalent issue with ML algorithms that decreases their efficacy and generalizability. The present investigation, using an extensive collection of data from 16 sensors in Tehran, Iran, from 2013 to 2023, focuses on applying the Least Absolute Shrinkage and Selection Operator (Lasso) regularisation technique to enhance the forecasting precision of ambient air pollutants concentration models, including particulate matter (PM and PM), CO, NO, SO, and O while decreasing overfitting.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!