BACKGROUND Breast cancer is the most common cause of cancer death among women in the world. With prevention and examinations, including breast self-examination, the death rate will be reduced. OBJECTIVE The purpose of this study was to determine the effect of health-belief-model-based training on the performance of women in breast self-examination in the province of Fars (Iran). METHODS An ...

abstract background: the reduction of suicidal attempts as one of the most important goals is improvement of societies’ health status. the present study aimed to assess the epidemiologic aspects of suicide cases in fars province in 2010-2011. methods: a total of 9714 suicide cases in fars province, south of iran, in 2010-2011 were enrolled in this cross-sectional study through census. the study...

BACKGROUND Toxoplasmosis is a parasitic disease caused by the protozoan Toxoplasma gondii. This parasite infects most of warm-blooded animals, including birds. Turkeys are one of these animals which might be infected by this parasite. Little is known about the prevalence of T. gondii in turkeys in Iran. OBJECTIVES The current study aimed to evaluate the rate of Toxoplasma infection in turkeys...

Since October 1987, the University of Utah Facility for Atmospheric Remote Sensing (FARS) has been applied to the probing of the atmosphere, concentrating on the study of high-level clouds. Regular FARS measurements, which currently total ~3000 h of ruby lidar polarization data, have been directed toward basic cloud research, remote sensing techniques development, and to improving satellite clo...

Despite malaria control programs in recent years, malaria transmission has not been eliminated in Iran. Molecular techniques including PCR, which has proved more sensitive and specific than microscopic examination methods, help to detect infection in low levels of parasitemia and mixed infections. Main our objectives were setting up nested PCR for detection of malaria and evaluating PCR based o...

Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study ...