Publications (18) View all
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Article: Quantitative criteria for improving performance of buccal DNA for high-throughput genetic analysis.
Jessica G Woo, Lisa J Martin, Lili Ding, W Mark Brown, Timothy D Howard, Carl D Langefeld, Charles J Moomaw, Mary Haverbusch, Guangyun Sun, Subba R Indugula, Hong Cheng, Ranjan Deka, Daniel Woo[show abstract] [hide abstract]
ABSTRACT: DNA from buccal brush samples is being used for high-throughput analyses in a variety of applications, but the impact of sample type on genotyping success and downstream statistical analysis remains unclear. The objective of the current study was to determine laboratory predictors of genotyping failure among buccal DNA samples, and to evaluate the successfully genotyped results with respect to analytic quality control metrics. Sample and genotyping characteristics were compared between buccal and blood samples collected in the population-based Genetic and Environmental Risk Factors for Hemorrhagic Stroke (GERFHS) study (https://gerfhs.phs.wfubmc.edu/public/index.cfm). Seven-hundred eight (708) buccal and 142 blood DNA samples were analyzed for laboratory-based and analysis metrics. Overall genotyping failure rates were not statistically different between buccal (11.3%) and blood (7.0%, p = 0.18) samples; however, both the Contrast Quality Control (cQC) rate and the dynamic model (DM) call rates were lower among buccal DNA samples (p < 0.0001). The ratio of double-stranded to total DNA (ds/total ratio) in the buccal samples was the only laboratory characteristic predicting sample success (p < 0.0001). A threshold of at least 34% ds/total DNA provided specificity of 98.7% with a 90.5% negative predictive value for eliminating probable failures. After genotyping, median sample call rates (99.1% vs. 99.4%, p < 0.0001) and heterozygosity rates (25.6% vs. 25.7%, p = 0.006) were lower for buccal versus blood DNA samples, respectively, but absolute differences were small. Minor allele frequency differences from HapMap were smaller for buccal than blood samples, and both sample types demonstrated tight genotyping clusters, even for rare alleles. We identified a buccal sample characteristic, a ratio of ds/total DNA <34%, which distinguished buccal DNA samples likely to fail high-throughput genotyping. Applying this threshold, the quality of final genotyping resulting from buccal samples is somewhat lower, but compares favorably to blood. Caution is warranted if cases and controls have different sample types, but buccal samples provide comparable results to blood samples in large-scale genotyping analyses.BMC Genetics 08/2012; 13(1):75. · 2.47 Impact Factor -
Article: Prevalence of human papillomavirus infection in young women receiving the first quadrivalent vaccine dose.
Lea E Widdice, Darron R Brown, David I Bernstein, Lili Ding, Deesha Patel, Marcia Shew, J Dennis Fortenberry, Jessica A KahnArchives of pediatrics & adolescent medicine 08/2012; 166(8):774-6. · 3.73 Impact Factor -
Article: Vaccine-type human papillomavirus and evidence of herd protection after vaccine introduction.
Jessica A Kahn, Darron R Brown, Lili Ding, Lea E Widdice, Marcia L Shew, Susan Glynn, David I Bernstein[show abstract] [hide abstract]
ABSTRACT: The aims of this study were to compare prevalence rates of human papillomavirus (HPV) in young women before and after HPV vaccine introduction to determine the following: (1) whether vaccine-type HPV infection decreased, (2) whether there was evidence of herd protection, and (3) whether there was evidence for type-replacement (increased prevalence of nonvaccine-type HPV). Young women 13 to 26 years of age who had had sexual contact were recruited from 2 primary care clinics in 2006-2007 for a prevaccination surveillance study (N = 368, none were vaccinated) and 2009-2010 for a postvaccination surveillance study (N = 409, 59% were vaccinated). Participants completed a questionnaire and were tested for cervicovaginal HPV DNA. HPV prevalence rates were compared in the pre- versus postsurveillance studies by using χ(2) tests. Propensity score weighting was used to balance differences in covariates between the 2 surveillance studies. The mean age was ∼19 years for both groups of participants and most were African American and non-Hispanic. After propensity score weighting, the prevalence rate for vaccine-type HPV decreased substantially (31.7%-13.4%, P < .0001). The decrease in vaccine-type HPV not only occurred among vaccinated (31.8%-9.9%, P < .0001) but also among unvaccinated (30.2%-15.4%, P < .0001) postsurveillance study participants. Nonvaccine-type HPV increased (60.7%-75.9%, P < .0001) for vaccinated postsurveillance study participants. Four years after licensing of the quadrivalent HPV vaccine, there was a substantial decrease in vaccine-type HPV prevalence and evidence of herd protection in this community. The increase in nonvaccine-type HPV in vaccinated participants should be interpreted with caution but warrants further study.PEDIATRICS 07/2012; 130(2):e249-56. · 4.47 Impact Factor -
Article: Correspondence Between Gonadal Steroid Hormone Concentrations and Secondary Sexual Characteristics Assessed by Clinicians, Adolescents, and Parents.
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ABSTRACT: Adolescent sexual maturation is staged using Tanner criteria assessed by clinicians, parents, or adolescents. The physiology of sexual maturation is driven by gonadal hormones. We investigate Tanner stage progression as a function of increasing gonadal hormone concentration and compare performances of different raters. Fifty-six boys (mean age, 12.7±1.3 years) and 52 girls (mean age, 12.0±1.6 years) were seen at baseline, 6, and 12 months. Estradiol and testosterone concentrations were determined from 3 morning serum samples and Tanner stage by three different raters (clinician, parent, adolescent). Results confirm that Tanner criteria reflect gonadal hormone concentrations, and clinician rating provides optimal assessment. Detailed insight about the strengths and limitations of different raters is provided, augmenting the scientific understanding of pubertal development.Journal of Research on Adolescence 06/2012; 22(2):381-391. · 1.99 Impact Factor -
Article: Novel real-time feedback and integrated simulation model for teaching and evaluating ultrasound-guided regional anesthesia skills in pediatric anesthesia trainees.
David L Moore, Lili Ding, Senthilkumar Sadhasivam[show abstract] [hide abstract]
ABSTRACT: To assess, teach, and improve core competencies and skills sets associated with ultrasound-guided regional anesthesia (UGRA) of pediatric anesthesia trainees. To effectively assess and improve UGRA-associated cognitive and technical skills and proficiency of pediatric anesthesia trainees using simulators and real-time feedback. Ultrasound usage has been increasingly adopted by anesthesiologists to perform regional anesthesia. Pediatric UGRA performance significantly lags behind adult UGRA practice. Lack of effective UGRA training is the major reason for this unfortunate lag. Integration of ultrasound imaging, target location, and needling skills are crucial in safely performing UGRA. However, there are no standards to ensure proficiency in practice, nor in training. We implemented an UGRA instructional program for all trainees, in two parts. First, we used a unique training model for initial assessment and training of technical skills. Second, we used an instructional program that encompasses UGRA and equipment-associated cognitive skills. After baseline assessment at 0 months, we retested these trainees at 6 and 12 months to identify progression of proficiency over time. Cognitive and technical UGRA skills of trainees improved significantly over the course of time. UGRA performance average accuracy improved to 79% at 12 months from the baseline accuracy of 57%. Cognitive UGRA-related skills of trainees improved from baseline results of 52.5-79.2% at 12 months. Implementing a multifaceted assessment and real-time feedback-based training has significantly improved UGRA-related cognitive and technical skills and proficiency of pediatric anesthesia trainees.Pediatric Anesthesia 05/2012; 22(9):847-53. · 2.10 Impact Factor