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 Background and objective: Cystic Echinococcosis (CE) is a cosmopolitan and prevalent zoonosis in the world. It caused by larva of Echinococcus genus�species. CE is medically and economically one of the important parasitic zoonoses in Iran especially Moghan Plain. So far no survey was conducted to determine the rate of CE in this region.

 Method: The subjects of this descriptive- analytic study were people of the Moghan plain. The prevalence of the disease was determined with serologic examination using ELISA test. ELISA was carried out using HCF-Ag that obtained from a diseased sheep. Household information was recorded in questionnaires before collecting serum samples. The results were analyzed by SPSS using statistical tests.

 Result: Out of 2008 samples, 1267 were female and 734 were male. At all 9.2 % (184) of sera were found to be positive.

 Conclusion: This survey determined more prevalence of Cystic Echinococcosis in women (%10) than men (%7.9) and infection is more in places that keep much Dogs.

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Background & objectives: Obesity and osteoporosis are major problem that their prevalence is increasing. Physical activity can be effective in the prevention of osteoporosis by some mechanisms such as changes in anthropometrics variables. Body mass index (BMI) in men and women is closely related to bone mineral density (BMD). However, the relationship varies between BMI and BMD according to the  different studies. Therefore, the aim of this study was to investigate the relationship between body mass index and lumbar bone mineral density in active and inactive middle-aged women.
Methods: Sixty active, middle-aged women and 60 inactive women in the age range of 50 to 65 years, with medical records and clinical trials were selected in Milad Hospital in Tehran. Body mass index was calculated and bone mineral density was measured by DEXA scan in the lumbar spine (L₁ to L₄). Independent t-test and Pearson correlation coefficient were used to evaluate the data. SPSS software version 26 was used for data analysis.
Results: The results of this study showed that in the active group in L₁, L₃ and L₄ lumbar vertebrae (p=0.034, p=0.017, p=0.019, respectively), and in the inactive group in L₃ and L₄ vertebrae (p=0.034, p=0.022, respectively), there was a positive and significant relationship between body mass index and bone mineral density. No significant relationship was found in other lumbar vertebrae of both groups.
Conclusion: The results of the present study showed that weight gain and consequently BMI among active women reduce the risk of osteoporosis. Physical activity in women seems to lead to more muscle mass, which in turn leads to an increase in bone mineral density. Considering this issue, it can be said that one of the applications of the results of the present study is the use of this index in predicting the bone density of individuals

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Background & objectives: Coronary heart disease (CHD) is an important medical disorder and one of the most common heart diseases worldwide, which causes disability and economic burden. The medical and research community is increasingly interested in computer-aided coronary heart disease diagnosis through the use of machine learning methods. This study aimed to diagnose coronary heart disease using a discriminant analysis algorithm in active elderly men.
Methods: This analytical study was conducted on 351 patients of Ayatollah Kashani Hospital in Tehran. This work used discriminant analysis algorithm to diagnose coronary artery disease. Python software was used for data analysis.
Results: The results showed that by using 14 characteristics as risk factors related to the subjects' laboratory, personal and lifestyle information. The discriminant analysis algorithm could distinguish healthy and sick people with 94.4% accuracy and 88.9% precision.
Conclusion: The results of the present study showed that this system can probably be used as an effective and intelligent method along with other diagnostic methods by cardiologists to predict coronary artery disease. Also, new data mining methods can be effective in reducing invasive risks.