Implications of maternal vitamin D administration for the neonatal respiratory distress syndrome: A randomized clinical trial.

BACKGROUND: Vitamin D deficiency has been suggested to be a risk factor for neonatal respiratory distress syndrome (RDS). This study aimed to evaluate the effect of 25 (OH) D administrations in pregnant women with findings of preterm labor on the incidence of RDS in their preterm neonates. MATERIALS AND METHODS: A randomized controlled clinical trial was conducted on pregnant mothers with gestational age (GA) of less than 34 weeks at risk of preterm delivery. 175 subjects were randomly assigned into two groups, including intervention (intramuscular injection of 50,000 units of 25(OH) D during 72 hours before delivery) and control (no injections). Serum concentrations of 25(OH) D were measured shortly after birth in both mothers and neonates. Then, clinical and laboratory results of mothers and their offspring were recorded (in a checklist). Short-term outcomes and the need for respiratory support were also assessed. Data were analyzed by independent t-test, Mann-Whitney U test, Fisher's exact test, and chi-square test. RESULTS: Even though gestational age, birth weight, delivery method, and serum vitamin D levels are consistent among both groups, 45% of neonates in the control group and 20% in the intervention group developed respiratory distress syndrome (P = 0.05). The mean 25(OH) D level in neonates was 17.7±10.5 and 19.29±9.94 ng/mL in the intervention and control groups, respectively (P > 0.05). CONCLUSION: A single dose of 50,000 units of intramuscular 25(OH)D in pregnant women at risk of preterm labor can lower the risk of RDS in the infant.

A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices.

Due to inherent uncertainties in measurement and analysis, groundwater quality assessment is a difficult task. Artificial intelligence techniques, specifically fuzzy inference systems, have proven useful in evaluating groundwater quality in uncertain and complex hydrogeological systems. In the present study, a Mamdani fuzzy-logic-based decision-making approach was developed to assess groundwater quality based on relevant indices. In an effort to develop a set of new hybrid fuzzy indices for groundwater quality assessment, a Mamdani fuzzy inference model was developed with widely-accepted groundwater quality indices: the Groundwater Quality Index (GQI), the Water Quality Index (WQI), and the Ground Water Quality Index (GWQI). In an effort to present generalized hybrid fuzzy indices a significant effort was made to employ well-known groundwater quality index acceptability ranges as fuzzy model output ranges rather than employing expert knowledge in the fuzzification of output parameters. The proposed approach was evaluated for its ability to assess the drinking water quality of 49 samples collected seasonally from groundwater resources in Iran's Sarab Plain during 2013-2014. Input membership functions were defined as "desirable", "acceptable" and "unacceptable" based on expert knowledge and the standard and permissible limits prescribed by the World Health Organization. Output data were categorized into multiple categories based on the GQI (5 categories), WQI (5 categories), and GWQI (3 categories). Given the potential of fuzzy models to minimize uncertainties, hybrid fuzzy-based indices produce significantly more accurate assessments of groundwater quality than traditional indices. The developed models' accuracy was assessed and a comparison of the performance indices demonstrated the Fuzzy Groundwater Quality Index model to be more accurate than both the Fuzzy Water Quality Index and Fuzzy Ground Water Quality Index models. This suggests that the new hybrid fuzzy indices developed in this research are reliable and flexible when used in groundwater quality assessment for drinking purposes.