Jorjani Biomedicine Journal

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Background and objectives: Cryptorchidism or undescended testicle, with a prevalence of 33 percent in preterm and 3-5 percent in term infants, is the most common congenital abnormality in newborn boys. The present study aimed to assess the recovery rate and urinary tract infection among infants with cryptorchidism during the first 15 months of their life.

       

 Methods: This cross-sectional descriptive study was conducted on 47 infants with cryptorchidism in Zahedan city (Iran) in 2012. The infants’ birth weight, preterm/term birth, delivery method, and affected testicle along with maternal age, history of urinary tract infection during pregnancy, and number of pregnancies were collected. Information about the infants’ urinary tract infection and recovery from cryptorchidism was collected through observations and trimonthly phone calls until the 15th month after birth. Percentage and mean were used for data analysis.

 Results: Of the 47 studied infants, 63.82 percent were premature, 59.57 percent had right-side cryptorchidism, and 80.60 percent developed urinary tract infection at least once. The highest incidence of urinary tract infection (29.8 percent) was seen at the age of three months old. The majority of infants (91.5 percent) recovered during the course of the study and the recovery rate at the fifth, 10th, and 15th months were 31.9 percent, 38.3 percent and 21.3 percent,respectively.

 Conclusion: This study revealed the high prevalence of urinary tract infection among infants with cryptorchidism. It also showed that most infants with cryptorchidism recover within 15 months of age.

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Background and objectives:

Almost 130 million infants are born each year, more than 8 million of whom

die before their first year of life. In the developing countries, two thirds of these deaths occur in the first month

of their life. Reduced infant mortality is among the Millennium Development indicators, and this rate is high

in Kalaleh city. This study aimed to identify the most common causes of infant death, so that the avoidable

deaths be prevented by offering intervention plans.

Methods:

information was collected with the designed form. The data were, then, coded and entered into the SPSS 17

software, and analyzed using independent statistical chi-square test.

In this study, all causes of infant death in Kalaleh city during 2003-2013 were investigated. The

Results:

prematurity (47.42%), congenital abnormalities (22.42%), and disasters and accidents (9.79%). 82.73%

of the infants weighed below 2500 g. 60% of the prematurity deaths occurred to primigravid women. There

388 infant deaths accounted for 83% of under-5 mortality. The most common causes of death include

was a statistically significant relationship between primigravity and prematurity infant death with a 95% confidence

(P=0.003). 74.74% of the infant deaths occurred in the first week, and 58.96% in the first 24 hours.

Conclusion:

As 38.4% of the infant deaths occurred in the first pregnancy, and 60% of prematurity deaths

happened to primigravid women, and there was a relationship between primigravity and prematurity infant

deaths, the importance of the particular care of these mothers comes into sight. Planning for teaching the im

of caring the primigravid mothers and making their families more sensitive about the significance of

portance

caring these mothers can be effective in reducing premature infant mortality.

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Background & Objective: Prediction of health status in newborns and also identification of its affecting factors is of the utmost importance. There are different ways of prediction. In this study, effective models and patterns have been studied using decision tree algorithm. Method: This study was conducted on 1,668 childbirths in three hospitals of Shohada, Omidi and Mehr in city of Behshahr. Variables such as baby's gender, birth weight, birth order, maternal age, maternal history of illness, gestational diseases, type of delivery, reason of caesarean section, maternal age, family relationship of father and mother, mother's blood type, mother's occupation and blood pressure and place of residence were chosen as predictive factors of decision tree categorization method. The health status of the baby was used as a dependent dual-mode variable. All variables were used in clustering and correlation rules. Prediction was done and then compared using 4 decision-tree algorithms. Results: In the clustering method, the optimal number of clusters was determined as 8, using the Dunn index measurement. Among all the implemented algorithms of CART, QUEST, CHAID and C5.0, C5.0 algorithm with detection rate of 94.44% was identified as the best algorithm. By implementing the Apriori algorithm, strong correlation rules were extracted with regard to the threshold for Support and Confidence. Among the characteristics, maternal age, birth weight and reason of caesarean section with the highest impacts were found as the most important factors in the prediction. Conclusion: Due to the simple interpretation of the decision tree and understandability of the extracted rules derived from it, this model can be used for (most individuals) professionals and pregnant women at different levels.