Association of vitamin D, retinol and zinc deficiencies with stunting in toddlers: findings from a national study in Iran.

OBJECTIVE: The objective of this study was to investigate the association of serum levels of 25(OH)D3 (vitamin D), retinol (vitamin A) and zinc with stunting in a large sample of Iranian toddlers. STUDY DESIGN: This was a cross-sectional study. METHODS: A total of 4261 children, aged 10-36 months, who had Iranian birth certificates were included in the present study. Weight and height were measured by experienced professionals in accordance with standard protocols. Stunting was defined as a height-for-age z-score of <-1 standard deviation (SD) based on the World Health Organization (WHO) criteria (the WHO Child Growth Standards median). Serum levels of 25(OH)D3, retinol and zinc were examined based on standard methods. RESULTS: The mean age of the study participants was 19.2 ± 8.4 months. A significant inverse association was found between serum retinol concentrations and the odds of stunting such that after controlling for potential confounders, toddlers in the highest quartile of serum retinol levels had 29% lower odds of stunting than those in the lowest quartile (odds ratio [OR]: 0.71, 95% confidence interval [CI]: 0.53-0.97). Furthermore, a significant inverse association was found between serum levels of retinol and stunting in girls (OR: 0.57, 95% CI: 0.34-0.94), urban toddlers (OR: 0.66, 95% CI: 0.44-0.99) and those who did not use nutritional supplements (OR: 0.70, 95% CI: 0.52-0.95). Although serum 25(OH)D3 levels were not significantly associated with stunting in the overall study population, we found a positive association among toddlers who used nutritional supplements. No significant association was found between serum levels of zinc and stunting. CONCLUSION: We found a significant inverse association between serum levels of retinol and stunting in toddlers aged 10-36 months.

Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

BACKGROUND: The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. METHODS: Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. RESULTS: The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. CONCLUSIONS: Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

Introducing a quantitative method to calculate the rate of primary infertility.

BACKGROUND: In the previous studies, the rate of primary infertility was reported differently. It seems the main reasons are related to the different methods of data collection and information analysis. Therefore, introducing a precise method to determine the infertile couples and the population exposed to the risk of infertility is an important issue to study primary infertility. METHODS: The proposed methodology for assessing primary infertility rate has been designed and applied by Avicenna Research Institute in a national survey. Sampling was conducted based on probability proportional to size cluster method. In this survey, after reviewing the former studies, the reproductive history was used as a basis for data collection. Every reproductive event was recorded with a code and a date in the questionnaire. To introduce a precise method, all possible events were considered thoroughly and for each situation, it was determined whether these cases should be considered in numerator, denominator or it should be eliminated from the study. Also in some situations where the correct diagnosis of infertility was not possible, a sensitivity analysis was recommended to see the variability of results under different scenarios. CONCLUSION: The proposed methodology can precisely define the infertile women and the population exposed to the risk of infertility. So, this method is more accurate than other available data collection strategies. To avoid bias and make a consistent methodology, using this method is recommended in future prevalence studies.

Study of genetic variation in sesame (Sesamum indicum L.) using agro-morphological traits and ISSR markers.

This research was conducted to study the genetic variation among eighteen genotypes of sesame (Sesamum indicum L.) collected from various agro-climatic regions of Iran along with six exotic genotypes from the Asian countries using both agro-morphological and ISSR marker traits. The results showed significant differences among genotypes for all agro-morphological traits and a relatively high genetic coefficient of variation observed for number of fruiting branches per plant, capsules per plant, plant height and seed yield per plant. Cluster analysis based on these traits grouped the genotypes into five separate clusters. Larger inter- than intra cluster distances implies the presence of higher genetic variability between the genotypes of different groups. Genotypes of two clusters with a good amount of genetic divergence and desirable agronomic traits were detected as promising genotypes for hybridization programs. The 13 ISSR primers chosen for molecular analysis revealed 170 bands, of which 130 (76.47%) were polymorphic. The generated dendrogram based on ISSR profiles divided the genotypes into seven groups. A principal coordinate analysis confirmed the results of clustering. The agro-morphological traits and ISSR markers reflected different aspects of genetic variation among the genotypes as revealed by a non significant cophenetic correlation in the Mantel test. Therefore the complementary application of both types of information is recommended to maximize the efficiency of sesame breeding programs. The discordance among diversity patterns and geographical distribution of genotypes found in this investigation implies that the parental lines for hybridization should be selected based on genetic diversity rather than the geographical distribution.