Maternal urinary bisphenols and phthalates in relation to estimated fetal weight across mid to late pregnancy.

BACKGROUND: Bisphenols and phthalates are high production volume chemicals used as additives in a variety of plastic consumer products leading to near ubiquitous human exposure. These chemicals have established endocrine disrupting properties and have been linked to a range of adverse reproductive and developmental outcomes. Here, we investigated exposure in relation to fetal growth. METHODS: Participants included 855 mother-fetal pairs enrolled in the population-based New York University Children's Health and Environment Study (NYU CHES). Bisphenols and phthalates were measured in maternal urine collected repeatedly during pregnancy. Analyses included 15 phthalate metabolites and 2 bisphenols that were detected in 50 % of participants or more. Fetal biometry data were extracted from electronic ultrasonography records and estimated fetal weight (EFW) was predicted for all fetuses at 20, 30, and 36 weeks gestation. We used quantile regression adjusted for covariates to model exposure-outcome relations across percentiles of fetal weight at each gestational timepoint. We examined sex differences using stratified models. RESULTS: Few statistically significant associations were observed across chemicals, gestational time periods, percentiles, and sexes. However, within gestational timepoints, we found that among females, the molar sums of the phthalates DiNP and DnOP were generally associated with decreases in EFW among smaller babies and increases in EFW among larger babies. Among males, the opposite trend was observed. However, confidence intervals were generally wide at the tails of the distribution. CONCLUSION: In this sample, exposure to bisphenols and phthalates was associated with small sex-specific shifts in fetal growth; however, few associations were observed at the median of fetal weight and confidence intervals in the tails were wide. Findings were strongest for DiNP and DnOP, which are increasingly used as replacements for DEHP, supporting the need for future research on these contaminants.

A hybrid fuzzy-ontology based intelligent system to determine level of severity and treatment recommendation for Benign Prostatic Hyperplasia.

This paper deals with application of fuzzy intelligent systems in diagnosing severity level and recommending appropriate therapies for patients having Benign Prostatic Hyperplasia. Such an intelligent system can have remarkable impacts on correct diagnosis of the disease and reducing risk of mortality. This system captures various factors from the patients using two modules. The first module determines severity level of the Benign Prostatic Hyperplasia and the second module, which is a decision making unit, obtains output of the first module accompanied by some external knowledge and makes an appropriate treatment decision based on its ontology model and a fuzzy type-1 system. In order to validate efficiency and accuracy of the developed system, a case study is conducted by 44 participants. Then the results are compared with the recommendations of a panel of experts on the experimental data. Then precision and accuracy of the results were investigated based on a statistical analysis.