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Introduction: Male infertility constitutes the primary cause of infertility for up to 30% of couples amongst many other reasons. Genetic disorders are too important because DNA integrity in sperm is necessary for having healthy next generation. The aim of this study was to evaluate the baseline DNA damage in sperm samples obtained from normal and infertile individuals.

Methods: Baseline DNA damage in spermatozoa from fertile (n=30) and infertile (n=90) individuals was compared using a modified alkaline single cell gel electrophoresis (comet assay).This technique was used to assess DNA integrity in the cells by measuring damages reflected as strand break under alkaline conditions. The cells were embedded in agarose on glass slides followed by lysis of the cell membranes after which, damaged DNA strands migrate under electrophoresis from nucleus towards the anode and deposited to one side giving the appearance of a tail. DNA damage in each group was calculated following visual observation and grading of comets under a fluorescence microscope. The significance of inter-group differences was statistically evaluated using analysis of variance (ANOVA) and Post hoc tests.

Results: Results indicate that the average DNA damage in normal sample is less than any other groups. Samples from oligospermic, oligoasthenospermic and asthenospermic patients showed various degrees of DNA damage significantly different from normal (p<0.05). The highest degree of DNA damage was seen in asthenospermic samples.

Conclusion: DNA damage in infertile individuals is found to be higher than normal. The reason for this observation may be due to a deficiency in antioxidants which is essential present during spermatogenesis. The presence of antioxidants prevents DNA damages in sperms due to oxidative agents.

 

  Hakim Research Journal 2005 8(2) 17- 2 4.

 

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Introduction: Male external genitourinary anomalies are common and important. Eearly diagnosis and treatment of the diseases can prevent late organic and psycho– economic complications.

Methods: The most simple way of diagnosis is a thorough physical examination. In this study we examined 4260 young male students (6-12 yrs old) from 16 primary schools in Tehan. Then the parents of the children with anomaly were asked about their history of anomalies.

Results: Anomalies were found in 156 (3.6%) students. The common anomalies were inguinal hernia in 80 cases (51.3%), unilateral undescended testis in 35 (22.3%). Hypospadiasis in 13 (8.3%), bilateral undescended testes in 4 (2.56%) and epispadiasis as well as chordoe in 3 (1.9%), respectively . In previous operated cases, 102 (66%) had emergent operation, 24% had been diagnosed by their physicans and 10% with their parents by chance.

Conclusion: According to easy availability of this system and complications of delayed management, it is recommended that routine physical examinations must be done.

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Hakim Research Journal 2005 8(2) 13-16.

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Asilian Bidgoli A (PhD), Ebrahimpour-Komleh H (PhD), Mousavirad SJ* (PhD)

Department of Computer Engineering, University of Kashan, Kashan, Iran

Original Article

Received: 23 Jan 2016, Accepted: 3 May 2016

Abstract

Introduction: Infertility is one of the most important problems, especially among males which has received special attention recently. Male infertility can be affected by different factors. There is now a large body of evidence to support the effects of life styles and environmental factors on semen quality. Data mining methods in artificial intelligence, as a decision support system, could be helpful in medical diagnosis of male infertility.

Methods: The influencing factors of seminal quality, and as a result ability to detect the infertility in males was assessed in this study using data mining algorithms in artificial intelligence. The dataset of 100 volunteers among students of University of Alicante were used. After data balancing different classifiers such as neural network were used to find the best classifier to predict the male infertility.

Results: The proposed algorithm is evaluated with different data mining algorithms to find a better analytical application of the algorithm. Support vector machine had the best accuracy (%95.15) to predict proposed infertility in male compared to the other classifiers. The proposed algorithm has a competitive accuracy compared to other algorithms.

Conclusion: The proposed algorithm is able to predict male infertility from lifestyle and environmental factors using data mining algorithms.

Key words: Male Infertility, Artificial intelligence, Data mining algorithms, Support vector machine, Support decision system

Please cite this article as follows:

Asilian Bidgoli A, Ebrahimpour-Komleh H, Mousavirad SJ. Male Infertility Prediction from Environmental Factors and Lifestyle Using Artificial Intelligence Algorithms. Hakim Health Sys Res 2016; 19(2): 72- 80.

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Please cite this article as follows: Alami A, Hosseini Z, Vahedian-Shahroodi M, Jafari A. Knowledge, attitude and practice of Iranian female high school students about iron supplementation: evaluation of Iron Supplementation National Program. Hakim Health Sys Res 2019; 22(1): 43- 51.

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Social Capital and Single Child in Employed Women
Mojgan Firouzbakht^1,2, Aram Tirgar ³*, Karimolah Hajian-Tilaki ⁴, Fatemeh Bakouei ⁵, Maryam Nikpour^6
1PhD by research of Reproductive Health, Social Determinants of Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran.
²Department of Nursing- Midwifery, Islamic Azad University, Babol Branch, Babol, Iran.
³Professor, Department of Community Medicine, School of Medicine Social Determinants of Health Research Center, Babol University of Medical Sciences.
⁴Professor of Biostatistics, Department of Epidemiology and Biostatistics, School of Medicine, Social Determinants of Health Research Center, Babol University of Medical Sciences.
⁵Assistant Professor of Reproductive Health, School of Nursing and Midwifery, Infertility and Reproductive Health Research Center, Babol University of Medical Sciences
⁶PhD by research of Reproductive Health, Social Determinants of Health Research Center, Health
Research Institute, Babol University of Medical Sciences, Babol, Iran
Abstract
Introduction: The rapid decline in the fertility rate has placed Iran among the countries with “below replacement fertility”, which represents a single-child behavior. Social networks (as one of the components of social capital) have been considered in recent years to understand fertility behaviors and changes in the levels of fertility. This study was conducted to investigate the relationship between social capital and single-child behavior in Iranian women workers.
Methods: In a cross-sectional study, 600 female workers were recruited by stratified sampling in health care settings in Babol, a city in northern Iran, in 2018. Data collection tools consisted of a questionnaire for demographic characteristics, fertility behavior, and social capital. Data analysis was performed by SPSS-21 software using Poisson regression and logistic regression at a significance level of 0.05.
Results: In this study, 45% of women were single-child and 54.5% did not intend to be pregnant again. There was a significant relationship between the number of pregnancies and social capital (P = 0.039). Similarly, in women with low social capital, the odds of single-child behavior increased although it was not significant (95% CI: 0.899 - 2.220, OR = 1.41).
Conclusion: The results of this study showed that increased social capital was associated with an increase in the number of pregnancies. Attention to social capital should be taken into account in population planning and policy-making.
Keywords: Fertility; Social Capital; Single Child; Female Workers; Iran
 

Please cite this article as follows: Firouzbakht M, Tirgar A, Hajian- Tilaki K, Riahi M E, Bakouei F, Nikpour M. Social capital and single child in employed women. Hakim Health Sys Res. 2019; 22(3): 241-248.

 
*Corresponding Author: Department of Community Medicine, Social Determinants of Health Research Center, School of Medicine, Babol University of Medical Sciences, Babol, Iran. Tel: +98-9113133781, Email: a.tirgar@mubabol.ac.ir
 

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Introduction: Organizational leadership is ‘the science and art of influencing employees, and mobilizing them to achieve organizational goals’. The purpose of this research was to identify the leadership challenges of female managers in healthcare organizations.
Methods: This interpretive phenomenological research was conducted through in-depth semi-structured face-to-face interviews with 27 female managers in healthcare organizations in Tehran, Iran. The research sample was selected by purposeful and snowball methods. Thematic analysis method and MAXQDA2018 were used to analyze the data.
Results: Overall, 20 challenges were identified in the leadership of female managers of healthcare organizations, which were classified into four groups of structural, process, human, and cultural challenges. Personal and personality characteristics of female managers such as kindness, compassion, and flexibility, along with little management experience, insufficient authority, inconsistency with superior and peer managers, lack of resources, high workload, low motivation, knowledge and skills of subordinates, informal groups, organizational masculinity, gender misconceptions, and organizational discrimination were among the most important leadership challenges of female managers of healthcare organizations. Some of the solutions to solve these challenges include strengthening intelligence, knowledge, and management skills, adhering to moral values, managing gender characteristics, delegating authority, creating resources, increasing risk-taking, improving communication, effective conflict management, developing communication networks, meditation, coaching, correcting gender stereotypes and removing gender discrimination.
Conclusion: Female managers face several challenges in leading their employees in healthcare organizations that need to be identified and resolved. A model was developed to strengthen the leadership of female managers of healthcare organizations, which will lead to an increase in their productivity.