The Minnesota Children's Pesticide Exposure Study (MNCPES) of the National Human Exposure Assessment Survey (NHEXAS) was conducted in Minnesota to evaluate children's pesticide exposure. This study complements and extends the populations and chemicals included in the NHEXAS Region V study. One of the goals of the study was to test protocols for acquiring exposure measurements and developing databases for use in exposure models and assessments. Analysis of the data quality is one element in assessing the performance of the collection and analysis protocols used in this study. Data quality information must also be available to investigators to guide analysis of the study data. During the planning phase of MNCPES, quality assurance (QA) goals were established for precision, accuracy, and quantification limits. The data quality was assessed against these goals. The assessment is complex. First, data are not available for all analytes and media sampled. In addition, several laboratories were responsible for the analysis of the collected samples. Each laboratory provided data according to their standard operating procedures (SOPs) and protocols. Detection limits were authenticated for each analyte in each sample type. The approach used to calculate detection limits varied across the different analytical methods. The analytical methods for pesticides in air, food, hand rinses, dust wipe and urine were sufficiently sensitive and met the QA goals, with very few exceptions. This was also true for polynuclear aromatic hydrocarbons (PAHs) in air and food. The analytical methods for drinking water and beverages had very low detection limits; however, there were very little measurable data for these samples. The collection and analysis methods for pesticides in surface press samples and soil, and for PAHs in dust wipes were not sufficiently sensitive. Accuracy was assessed primarily as recovery from field controls. The results were good for pesticides and PAHs in air (75-125% recovery). Recovery was lower (<75%) for pesticides in drinking water and beverages. The recovery of pesticides from hand rinses met QA goals (75-100%), but surface press samples showed lower recovery (50-70%). Analysis by gas chromatography-mass spectrometry (GC-MS) did not confirm the presence of atrazine and other pesticides in hand rinse and surface press samples that had been detected by GC-ECD, but instead GC-MS confirmed background interferences. Assessment of the precision of sample collection and analysis is based on the percent relative standard deviation (%RSD) between the results for duplicate samples. Data are available only for pesticides and PAHs in air. Precision was good (<20% RSD) for analytes with measurable data. There were a few analytes with %RSD >20%, but the number of data pairs was very small in these cases. Precision for instrumental analysis of food sample extracts was excellent, with the median %RSD < 20 for all measurable pesticides. The median %RSD for the analysis of replicate aliquots of food from the same sample composite was considerably higher, indicating the potential for inhomogeneity of food homogenates.
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http://dx.doi.org/10.1038/sj.jea.7500315 | DOI Listing |
J Cancer Res Ther
December 2024
Department of Interventional Ultrasound, Fifth Center of Chinese People's Liberation Army General Hospital, Beijing, China.
Objective: To examine the diagnostic efficacy of contrast-enhanced ultrasound (CEUS) with Sonazoid (Sonazoid-CEUS) for endometrial lesions.
Methods: In this prospective and multicenter study, data were collected from 84 patients with endometrial lesions from 11 hospitals in China. All the patients received a conventional US and Sonazoid-CEUS examination.
JAMA Netw Open
January 2025
Division of Endocrinology, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
Importance: Data characterizing the severity and changing prevalence of bone mineral density (BMD) deficits and associated nonfracture consequences among childhood cancer survivors decades after treatment are lacking.
Objective: To evaluate risk for moderate and severe BMD deficits in survivors and to identify long-term consequences of BMD deficits.
Design, Setting, And Participants: This cohort study used cross-sectional and longitudinal data from the St Jude Lifetime (SJLIFE) cohort, a retrospectively constructed cohort with prospective follow-up.
JAMA Netw Open
January 2025
National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
Importance: Sleep disorders and mild cognitive impairment (MCI) commonly coexist in older adults, increasing their risk of developing dementia. Long-term tai chi chuan has been proven to improve sleep quality in older adults. However, their adherence to extended training regimens can be challenging.
View Article and Find Full Text PDFCancer Nurs
January 2025
Author Affiliations: Department of Nursing, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine (Dr Kim); and College of Nursing, Hanyang University (Dr Hwang), Seoul, Republic of Korea.
Background: Although the survival rate for thyroid cancer is high, a nursing intervention that enhances autonomous motivation is needed for patients with jobs to improve their long-term self-management abilities in the early postoperative period.
Objectives: This study aims to develop a mobile application (app) based on the Self-Determination Theory for patients returning to work after thyroid cancer surgery and to verify its effectiveness.
Methods: We developed an app to promote self-management and verify its effectiveness after 12 weeks in early outpatients who underwent thyroid cancer surgery through a randomized controlled trial design.
J Occup Health
January 2025
Panasonic Corporation, Department Electric Works Company/Engineering Division, Osaka, Japan.
Background: Falls are among the most prevalent workplace accidents, necessitating thorough screening for susceptibility to falls and customization of individualized fall prevention programs. The aim of this study was to develop and validate a high fall risk prediction model using machine learning (ML) and video-based first three steps in middle-aged workers.
Methods: Train data (n=190, age 54.
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