Detecting spatiotemporal clusters of accidental poisoning mortality among Texas counties, U.S., 1980 – 2001

Background Accidental poisoning is one of the leading causes of injury in the United States, second only to motor vehicle accidents. According to the Centers for Disease Control and Prevention, the rates of accidental poisoning mortality have been increasing in the past fourteen years nationally. In Texas, mortality rates from accidental poisoning have mirrored national trends, increasing linearly from 1981 to 2001. The purpose of this study was to determine if there are spatiotemporal clusters of accidental poisoning mortality among Texas counties, and if so, whether there are variations in clustering and risk according to gender and race/ethnicity. The Spatial Scan Statistic in combination with GIS software was used to identify potential clusters between 1980 and 2001 among Texas counties, and Poisson regression was used to evaluate risk differences. Results Several significant (p < 0.05) accidental poisoning mortality clusters were identified in different regions of Texas. The geographic and temporal persistence of clusters was found to vary by racial group, gender, and race/gender combinations, and most of the clusters persisted into the present decade. Poisson regression revealed significant differences in risk according to race and gender. The Black population was found to be at greatest risk of accidental poisoning mortality relative to other race/ethnic groups (Relative Risk (RR) = 1.25, 95% Confidence Interval (CI) = 1.24 – 1.27), and the male population was found to be at elevated risk (RR = 2.47, 95% CI = 2.45 – 2.50) when the female population was used as a reference. Conclusion The findings of the present study provide evidence for the existence of accidental poisoning mortality clusters in Texas, demonstrate the persistence of these clusters into the present decade, and show the spatiotemporal variations in risk and clustering of accidental poisoning deaths by gender and race/ethnicity. By quantifying disparities in accidental poisoning mortality by place, time and person, this study demonstrates the utility of the spatial scan statistic combined with GIS and regression methods in identifying priority areas for public health planning and resource allocation.

Knowledge and Perception of the Role of Targeted Ultrasound in Detecting Down Syndrome Among a High Risk Population

The purpose of this study was to determine the perception and knowledge of targeted ultrasound in women who screen positive for Down syndrome in the first or second trimester, and to assess the perceived detection rate of Down syndrome by targeted ultrasound in this population. While several studies have reported patient perceptions’ of routine ultrasound, no study has specifically examined knowledge regarding the targeted ultrasound and its role in detecting Down syndrome. A targeted ultrasound is a special ultrasound during the second trimester offered to women who may be at a higher-than-average risk of having a baby with some type of birth defect or complication. The purpose of the ultrasound is to evaluate the overall growth and development of the baby as well as screen for birth defects and genetic conditions. Women under the age of 35 referred for an abnormal first or second trimester maternal serum screen to several Houston area clinics were asked to complete a questionnaire to obtain demographic and ultrasound knowledge information as well as assess perceived detection rate of Down syndrome by ultrasound. Seventy-seven women completed the questionnaire and participated in the study. Our findings revealed that women have limited background knowledge about the targeted ultrasound and its role in detecting Down syndrome. These findings are consistent with other studies that have reported a lack of understanding about the purpose of ultrasound examinations. One factor that seems to increase background knowledge about the targeted ultrasound is individuals having a higher level of education. However, most participants regardless of race, education, income, and exposure to targeted ultrasound information did not know the capabilities of a targeted ultrasound. This study confirmed women lack background knowledge about the targeted ultrasound and do not know enough about the technology to form a perception regarding its ability to detect Down syndrome. Additional studies to identify appropriate education techniques are necessary to determine how to best inform our patient population about targeted ultrasound.

COMPARISON OF DNA TRANSFECTION AND MICROCELL-MEDIATED CHROMOSOME TRANSFER FOR DETECTING TRANSFORMING GENES IN HUMAN TUMOR CELL LINES

DNA mediated gene transfection is an important tool for moving and isolating genes from one cell type and putting them into a foreign genetic background. DNA transfection studies have been done routinely in many laboratories to identify and isolate transforming sequences in human tumors and tumor cell lines. A second technique, microcell-mediated chromosome transfer, allows the transfer of small numbers of intact human chromosome from one cell to another. This work was done to compare the efficiency of these two techniques in the transformation of NIH 3T3 mouse fibroblast cells.^ My intent in comparing these two techniques was to see if there was a difference in the transforming capability of DNA which has been purified of all associated protein and RNAs, and that of DNA which is introduced into a cell in its native form, the chromosome. If chromosomal sequences were capable of transforming the 3T3 cells in culture, the method could then be used as a way to isolate the relevant tumorigenic chromosomes from human tumors.^ The study shows, however, that even for those cell lines that contain transforming sequences identified by DNA-mediated gene transfer, those same sequences were unable to transform 3T3 cells when introduced to the cells by somatic fusion of human tumor microcells. I believe that the human transforming sequences in their original genetic conformation are not recognized by the mouse cell as genes which should be expressed; therefore, no noticeable transformation event was selected by this technique. ^

Detecting the signature of natural selection with microsatellites

Natural selection is one of the major factors in the evolution of all organisms. Detecting the signature of natural selection has been a central theme in evolutionary genetics. With the availability of microsatellite data, it is of interest to study how natural selection can be detected with microsatellites. ^ The overall aim of this research is to detect signatures of natural selection with data on genetic variation at microsatellite loci. The null hypothesis to be tested is the neutral mutation theory of molecular evolution, which states that different alleles at a locus have equivalent effects on fitness. Currently used tests of this hypothesis based on data on genetic polymorphism in natural populations presume that mutations at the loci follow the infinite allele/site models (IAM, ISM), in the sense that at each site at most only one mutation event is recorded, and each mutation leads to an allele not seen before in the population. Microsatellite loci, which are abundant in the genome, do not obey these mutation models, since the new alleles at such loci can be created either by contraction or expansion of tandem repeat sizes of core motifs. Since the current genome map is mainly composed of microsatellite loci and this class of loci is still most commonly studied in the context of human genome diversity, this research explores how the current test procedures for testing the neutral mutation hypothesis should be modified to take into account a generalized model of forward-backward stepwise mutations. In addition, recent literature also suggested that past demographic history of populations, presence of population substructure, and varying rates of mutations across loci all have confounding effects for detecting signatures of natural selection. ^ The effects of the stepwise mutation model and other confounding factors on detecting signature of natural selection are the main results of the research. ^

Genome-wide algorithm for detecting CNV associations with diseases

SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). The quality of the inferences about copy number can be affected by many factors including batch effects, DNA sample preparation, signal processing, and analytical approach. Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP genotyping data. However, these algorithms lack specificity to detect small CNVs due to the high false positive rate when calling CNVs based on the intensity values. Association tests based on detected CNVs therefore lack power even if the CNVs affecting disease risk are common. In this research, by combining an existing Hidden Markov Model (HMM) and the logistic regression model, a new genome-wide logistic regression algorithm was developed to detect CNV associations with diseases. We showed that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than an existing popular algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.^

A MOLECULAR APPROACH FOR DETECTING GENETIC DAMAGE USING THE ECO R1 FAMILY OF HUMAN DNA (SEQUENCING)

A clone of the primary Eco R1 family of human DNA sequences has been used as an indicator sequence for detecting alterations induced by a toxic agent. Specific clones of this family have been examined and compared to the consensus sequence to determine the normal variability of this family. Though variations were observed, data indicated that such clones can be used to study induced DNA modifications. This DNA was exposed to the toxic agent dimethyl sulfate under various conditions and a distinct pattern of aberrations was shown to occur. It is suggested that this approach be used to characterize patterns of damage induced by various agents in the ultimate development of a system capable of monitoring human genotoxic exposure. ^

Improved Techniques for Acquisition and Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Detecting Vascular Permeability in the Central Nervous System

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive technique for quantitative assessment of the integrity of blood-brain barrier and blood-spinal cord barrier (BSCB) in the presence of central nervous system pathologies. However, the results of DCE-MRI show substantial variability. The high variability can be caused by a number of factors including inaccurate T1 estimation, insufficient temporal resolution and poor contrast-to-noise ratio. My thesis work is to develop improved methods to reduce the variability of DCE-MRI results. To obtain fast and accurate T1 map, the Look-Locker acquisition technique was implemented with a novel and truly centric k-space segmentation scheme. In addition, an original multi-step curve fitting procedure was developed to increase the accuracy of T1 estimation. A view sharing acquisition method was implemented to increase temporal resolution, and a novel normalization method was introduced to reduce image artifacts. Finally, a new clustering algorithm was developed to reduce apparent noise in the DCE-MRI data. The performance of these proposed methods was verified by simulations and phantom studies. As part of this work, the proposed techniques were applied to an in vivo DCE-MRI study of experimental spinal cord injury (SCI). These methods have shown robust results and allow quantitative assessment of regions with very low vascular permeability. In conclusion, applications of the improved DCE-MRI acquisition and analysis methods developed in this thesis work can improve the accuracy of the DCE-MRI results.

Detecting genetic and nutritional lung cancer risk factors related to folate metabolism using Bayesian generalized linear models

Complex diseases, such as cancer, are caused by various genetic and environmental factors, and their interactions. Joint analysis of these factors and their interactions would increase the power to detect risk factors but is statistically. Bayesian generalized linear models using student-t prior distributions on coefficients, is a novel method to simultaneously analyze genetic factors, environmental factors, and interactions. I performed simulation studies using three different disease models and demonstrated that the variable selection performance of Bayesian generalized linear models is comparable to that of Bayesian stochastic search variable selection, an improved method for variable selection when compared to standard methods. I further evaluated the variable selection performance of Bayesian generalized linear models using different numbers of candidate covariates and different sample sizes, and provided a guideline for required sample size to achieve a high power of variable selection using Bayesian generalize linear models, considering different scales of number of candidate covariates. ^ Polymorphisms in folate metabolism genes and nutritional factors have been previously associated with lung cancer risk. In this study, I simultaneously analyzed 115 tag SNPs in folate metabolism genes, 14 nutritional factors, and all possible genetic-nutritional interactions from 1239 lung cancer cases and 1692 controls using Bayesian generalized linear models stratified by never, former, and current smoking status. SNPs in MTRR were significantly associated with lung cancer risk across never, former, and current smokers. In never smokers, three SNPs in TYMS and three gene-nutrient interactions, including an interaction between SHMT1 and vitamin B12, an interaction between MTRR and total fat intake, and an interaction between MTR and alcohol use, were also identified as associated with lung cancer risk. These lung cancer risk factors are worthy of further investigation.^