ABSTRACT
The effects of route and vehicle on blood and milk levels of decabromodiphenyl ether (DecaBDE; CASRN 1163-19-5) were investigated in the rat to assist in the design and conduct of a developmental neurotoxicity study. Blood plasma and/or milk concentrations were determined in dams, fetuses, and/or nursing pups after repeated DecaBDE administration by gavage throughout gestation or gestation and lactation using corn oil (CO) or soyaphospholipon/Lutrol F 127-water (SPL) as the vehicle. The impact of vehicle on plasma levels was also investigated in pups derived from naive dams after a single postnatal dose. This study reports for the first time fetal and neonatal plasma concentrations concurrent with those of maternal plasma and/or milk. Higher concentrations of DecaBDE were achieved in plasma and in milk with CO than with SPL. Furthermore, pups derived from dams treated with only SPL were lower in body weight, compared with those from dams treated with either CO, CO and DecaBDE, or SPL and DecaBDE. The study further shows that exposure to DecaBDE is relatively consistent across the dose range of 100 to 1000 mg/(kg · day) when administered in CO.
Subject(s)
Fetal Blood/metabolism , Flame Retardants/pharmacokinetics , Halogenated Diphenyl Ethers/blood , Maternal Exposure/adverse effects , Milk/metabolism , Toxicity Tests/methods , Administration, Oral , Animals , Animals, Newborn , Corn Oil/chemistry , Dose-Response Relationship, Drug , Female , Flame Retardants/toxicity , Gestational Age , Halogenated Diphenyl Ethers/pharmacokinetics , Halogenated Diphenyl Ethers/toxicity , Maternal-Fetal Exchange , Polyethylenes/chemistry , Polypropylenes/chemistry , Pregnancy , Rats , Rats, Sprague-DawleyABSTRACT
A novel method has been developed for the extraction, analysis and identification of petroleum-based fuels using solid-phase microextraction with analysis by GC-FID. Multivariate data analysis is employed to simplify these data allowing for more accurate classification. Principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) are explored for their effectiveness in establishing accelerant groupings based on the current and previous ASTM International guidelines. The SIMCA models developed for the previous and current ASTM system were 98.5% and 97.2% accurate in unknown sample class prediction. SPME in conjunction with multivariate data analysis is a new approach in accelerant sampling and classification.
