Kawasaki syndrome: Cluster analysis of incident cases presenting to a pediatric tertiary care hospital in Texas from January 1, 2005 to December 31, 2009

Background: Despite almost 40 years of research into the etiology of Kawasaki Syndrome (KS), there is little research published on spatial and temporal clustering of KS cases. Previous analysis has found significant spatial and temporal clustering of cases, therefore cluster analyses were performed to substantiate these findings and provide insight into incident KS cases discharged from a pediatric tertiary care hospital. Identifying clusters from a single institution would allow for prospective analysis of risk factors and potential exposures for further insight into KS etiology. ^ Methods: A retrospective study was carried out to examine the epidemiology and distribution of patients presenting to Texas Children’s Hospital in Houston, Texas, with a diagnosis of Acute Febrile Mucocutaneous Lymph Node Syndrome (MCLS) upon discharge from January 1, 2005 to December 31, 2009. Spatial, temporal, and space-time cluster analyses were performed using the Bernoulli model with case and control event data. ^ Results: 397 of 102,761 total patients admitted to Texas Children’s Hospital had a principal or secondary diagnosis of Acute Febrile MCLS upon over the 5 year period. Demographic data for KS cases remained consistent with known disease epidemiology. Spatial, temporal, and space-time analyses of clustering using the Bernoulli model demonstrated no statistically significant clusters. ^ Discussion: Despite previous findings of spatial-temporal clustering of KS cases, there were no significant clusters of KS cases discharged from a single institution. This implicates the need for an expanded approach to conducting spatial-temporal cluster analysis and KS surveillance given the limitations of evaluating data from a single institution.^

Application of stellar photometry to the analysis of microarray images

Improvements in the analysis of microarray images are critical for accurately quantifying gene expression levels. The acquisition of accurate spot intensities directly influences the results and interpretation of statistical analyses. This dissertation discusses the implementation of a novel approach to the analysis of cDNA microarray images. We use a stellar photometric model, the Moffat function, to quantify microarray spots from nylon microarray images. The inherent flexibility of the Moffat shape model makes it ideal for quantifying microarray spots. We apply our novel approach to a Wilms' tumor microarray study and compare our results with a fixed-circle segmentation approach for spot quantification. Our results suggest that different spot feature extraction methods can have an impact on the ability of statistical methods to identify differentially expressed genes. We also used the Moffat function to simulate a series of microarray images under various experimental conditions. These simulations were used to validate the performance of various statistical methods for identifying differentially expressed genes. Our simulation results indicate that tests taking into account the dependency between mean spot intensity and variance estimation, such as the smoothened t-test, can better identify differentially expressed genes, especially when the number of replicates and mean fold change are low. The analysis of the simulations also showed that overall, a rank sum test (Mann-Whitney) performed well at identifying differentially expressed genes. Previous work has suggested the strengths of nonparametric approaches for identifying differentially expressed genes. We also show that multivariate approaches, such as hierarchical and k-means cluster analysis along with principal components analysis, are only effective at classifying samples when replicate numbers and mean fold change are high. Finally, we show how our stellar shape model approach can be extended to the analysis of 2D-gel images by adapting the Moffat function to take into account the elliptical nature of spots in such images. Our results indicate that stellar shape models offer a previously unexplored approach for the quantification of 2D-gel spots. ^

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

Molecular epidemiology of vancomycin-resistant Enterococcus faecium: a prospective, multicenter study in South American hospitals.

Enterococcus faecium has emerged as an important nosocomial pathogen worldwide, and this trend has been associated with the dissemination of a genetic lineage designated clonal cluster 17 (CC17). Enterococcal isolates were collected prospectively (2006 to 2008) from 32 hospitals in Colombia, Ecuador, Perú, and Venezuela and subjected to antimicrobial susceptibility testing. Genotyping was performed with all vancomycin-resistant E. faecium (VREfm) isolates by pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing. All VREfm isolates were evaluated for the presence of 16 putative virulence genes (14 fms genes, the esp gene of E. faecium [espEfm], and the hyl gene of E. faecium [hylEfm]) and plasmids carrying the fms20-fms21 (pilA), hylEfm, and vanA genes. Of 723 enterococcal isolates recovered, E. faecalis was the most common (78%). Vancomycin resistance was detected in 6% of the isolates (74% of which were E. faecium). Eleven distinct PFGE types were found among the VREfm isolates, with most belonging to sequence types 412 and 18. The ebpAEfm-ebpBEfm-ebpCEfm (pilB) and fms11-fms19-fms16 clusters were detected in all VREfm isolates from the region, whereas espEfm and hylEfm were detected in 69% and 23% of the isolates, respectively. The fms20-fms21 (pilA) cluster, which encodes a putative pilus-like protein, was found on plasmids from almost all VREfm isolates and was sometimes found to coexist with hylEfm and the vanA gene cluster. The population genetics of VREfm in South America appear to resemble those of such strains in the United States in the early years of the CC17 epidemic. The overwhelming presence of plasmids encoding putative virulence factors and vanA genes suggests that E. faecium from the CC17 genogroup may disseminate in the region in the coming years.

A functional collagen adhesin gene, acm, in clinical isolates of Enterococcus faecium correlates with the recent success of this emerging nosocomial pathogen.

Enterococcus faecium recently evolved from a generally avirulent commensal into a multidrug-resistant health care-associated pathogen causing difficult-to-treat infections, but little is known about the factors responsible for this change. We previously showed that some E. faecium strains express a cell wall-anchored collagen adhesin, Acm. Here we analyzed 90 E. faecium isolates (99% acm(+)) and found that the Acm protein was detected predominantly in clinically derived isolates, while the acm gene was present as a transposon-interrupted pseudogene in 12 of 47 isolates of nonclinical origin. A highly significant association between clinical (versus fecal or food) origin and collagen adherence (P

Development of a rapid bioassessment method and index of biotic integrity for estuaries located along the northwest Gulf of Mexico

Recently it has been proposed that the evaluation of effects of pollutants on aquatic organisms can provide an early warning system of potential environmental and human health risks (NRC 1991). Unfortunately there are few methods available to aquatic biologists to conduct assessments of the effects of pollutants on aquatic animal community health. The primary goal of this research was to develop and evaluate the feasibility of such a method. Specifically, the primary objective of this study was to develop a prototype rapid bioassessment technique similar to the Index of Biotic Integrity (IBI) for the upper Texas and Northwestern Gulf of Mexico coastal tributaries. The IBI consists of a series of "metrics" which describes specific attributes of the aquatic community. Each of these metrics are given a score which is then subtotaled to derive a total assessment of the "health" of the aquatic community. This IBI procedure may provide an additional assessment tool for professionals in water quality management.^ The experimental design consisted primarily of compiling previously collected data from monitoring conducted by the Texas Natural Resource Conservation Commission (TNRCC) at five bayous classified according to potential for anthropogenic impact and salinity regime. Standardized hydrological, chemical, and biological monitoring had been conducted in each of these watersheds. The identification and evaluation of candidate metrics for inclusion in the estuarine IBI was conducted through the use of correlation analysis, cluster analysis, stepwise and normal discriminant analysis, and evaluation of cumulative distribution frequencies. Scores of each included metric were determined based on exceedances of specific percentiles. Individual scores were summed and a total IBI score and rank for the community computed.^ Results of these analyses yielded the proposed metrics and rankings listed in this report. Based on the results of this study, incorporation of an estuarine IBI method as a water quality assessment tool is warranted. Adopted metrics were correlated to seasonal trends and less so to salinity gradients observed during the study (0-25 ppt). Further refinement of this method is needed using a larger more inclusive data set which includes additional habitat types, salinity ranges, and temporal variation. ^

Statistical characterization and development of summary measures of non-normal distributions of nuclear morphometry data from prostate cancer cells for use as prognostic indicators

Nuclear morphometry (NM) uses image analysis to measure features of the cell nucleus which are classified as: bulk properties, shape or form, and DNA distribution. Studies have used these measurements as diagnostic and prognostic indicators of disease with inconclusive results. The distributional properties of these variables have not been systematically investigated although much of the medical data exhibit nonnormal distributions. Measurements are done on several hundred cells per patient so summary measurements reflecting the underlying distribution are needed.^ Distributional characteristics of 34 NM variables from prostate cancer cells were investigated using graphical and analytical techniques. Cells per sample ranged from 52 to 458. A small sample of patients with benign prostatic hyperplasia (BPH), representing non-cancer cells, was used for general comparison with the cancer cells.^ Data transformations such as log, square root and 1/x did not yield normality as measured by the Shapiro-Wilks test for normality. A modulus transformation, used for distributions having abnormal kurtosis values, also did not produce normality.^ Kernel density histograms of the 34 variables exhibited non-normality and 18 variables also exhibited bimodality. A bimodality coefficient was calculated and 3 variables: DNA concentration, shape and elongation, showed the strongest evidence of bimodality and were studied further.^ Two analytical approaches were used to obtain a summary measure for each variable for each patient: cluster analysis to determine significant clusters and a mixture model analysis using a two component model having a Gaussian distribution with equal variances. The mixture component parameters were used to bootstrap the log likelihood ratio to determine the significant number of components, 1 or 2. These summary measures were used as predictors of disease severity in several proportional odds logistic regression models. The disease severity scale had 5 levels and was constructed of 3 components: extracapsulary penetration (ECP), lymph node involvement (LN+) and seminal vesicle involvement (SV+) which represent surrogate measures of prognosis. The summary measures were not strong predictors of disease severity. There was some indication from the mixture model results that there were changes in mean levels and proportions of the components in the lower severity levels. ^

Relationship between enterotoxigenic Escherichia coli and travelers' diarrhea in Mexico, from 1992 to 1997

The purpose of this study was to examine the relationship between enterotoxigenic ETEC and travelers' diarrhea over a period of five years in Guadalajara, Mexico. Specifically, this study identified and characterized ETEC from travelers with diarrhea. The objectives were to study the colonization factor antigens, toxins and antibiotic sensitivity patterns in ETEC from 1992 to 1997 and to study the molecular epidemiology of ETEC by plasmid content and DNA restriction fragment patterns. ^ In this survey of travelers' diarrhea in Guadalajara, Mexico, 928 travelers with diarrhea were screened for enteric pathogens between 1992 and 1997. ETEC were isolated in 195 (19.9%) of the patients, representing the most frequent enteric pathogen identified. ^ A total of 31 antimicrobial susceptibility patterns were identified among ETEC isolates over the five-year period. ^ The 195 ETEC isolates contained two to six plasmids each, which ranged in size from 2.0 to 23 kbp. ^ Three different reproducible rRNA gene restriction patterns (ribotypes R-1 to R-3) were obtained among the 195 isolates with the enzyme, HindIII. ^ Colonization factor antigens (CFAs) were identified in 99 (51%) of the 195 ETEC strains studied. ^ Cluster analysis of the observations seen in the four assays all confirmed the five distinct groups of study-year strains of ETEC. Each group had a >95% similarity level of strains within the group and <60% similarity level between the groups. In addition, discriminant analysis of assay variables used in predicting the ETEC strains, reveal a >80% relationship between both the plasmid and rRNA content of ETEC strains and study-year. ^ These findings, based on laboratory observations of the differences in biochemical, antimicrobial susceptibility, plasmid and ribotype content, suggest complex epidemiology for ETEC strains in a population with travelers' diarrhea. The findings of this study may have implications for our understanding of the epidemiology, transmission, treatment, control and prevention of the disease. It has been suggested that an ETEC vaccine for humans should contain the most prevalent CFAs. Therefore, it is important to know the prevalence of these factors in ETEC in various geographical areas. ^ CFAs described in this dissertation may be used in different epidemiological studies in which the prevalence of CFAs and other properties on ETEC will be evaluated. Furthermore, in spite of an intense search in near 200 ETEC isolates for strains that may have clonal relationship, we failed to identify such strains. However, further studies are in progress to construct suitable live vaccine strains and to introduce several of CFAs in the same host organism by recombinant DNA techniques (Dr. Ann-Mari Svennerholm's lab). (Abstract shortened by UMI.)^

Development and validation of the Neuro Symptom Inventory in the primary brain tumor patient population

Symptoms has been shown to predict quality of life, treatment course and survival in solid tumor patients. Currently, no instrument exists that measures both cancer-related symptoms and the neurologic symptoms that are unique to persons with primary brain tumors (PBT). The aim of this study was to develop and validate an instrument to measure symptoms in patients who have PBT. A conceptual analysis of symptoms and symptom theories led to defining the symptoms experience as the perception of the frequency, intensity, distress, and meaning that occurs as symptoms are produced, perceived, and expressed. The M.D. Anderson Symptom Inventory (MDASI) measures both symptoms and how they interfere with daily functioning in patients with cancer, which is similar to the situational meaning defined in the analysis. A list of symptoms pertinent to the PBT population was added to the core MDASI and reviewed by a group of experts for validity. As a result, 18 items were added to the core MDASI (the MDASI-BT) for the next phase of instrument development, establishing validity and reliability through a descriptive, cross-sectional approach with PBT patients. Data were collected with a patient completed demographic data sheet, an investigator completed clinician checklist, and the MDASI-BT. Analysis evaluated the reliability and validity of the MDASI-BT in PBT patients. Data were obtained from 201 patients. The number of items was reduced to 22 by evaluation of symptom severity as well as cluster analysis. Regression analysis showed more than half (56%) of the variability in symptom severity was explained by the brain tumor module items. Factor analysis confirmed that the 22 item MDASI-BT measured six underlying constructs: (a) affective; (b) cognitive; (c) focal neurologic deficits; (d) constitutional symptoms; (e) treatment-related symptoms; and (f) gastrointestinal symptoms. The MDASI-BT was sensitive to disease severity and if the patient was hospitalized. The MDASI-BT is the first instrument to measure symptoms in PBT patients that has demonstrated reliability and validity. It is the first step in a program of research to evaluate the occurrence of symptoms and plan and evaluate interventions for PBT patients. ^

Catchment area of oral cleft cases for two major hospitals in Harris County

Background. Orofacial clefts are among the most common birth defects and considered to be of complex etiology with both genetic and environmental factors.^ Objectives. The purpose of this study was to describe maternal and infant characteristics, examine the catchment area, and determine if there are any geospatial patterns among infants with an orofacial cleft delivered at two major hospitals in Harris County, The Woman's Hospital of Texas and Memorial Hermann Hospital-Texas Medical Center, from January 1, 2003 through December 31, 2007.^ Methods. Data were obtained from two major hospitals in Harris County and included all babies delivered in the period from 2003 through 2007 with an orofacial cleft. Residential addresses were mapped using MapInfo GIS software and the cluster analysis performed with SaTScan software.^ Results. Ninety-nine cases were identified spanning nine counties. 26% of cases resided within a 5-mile radius of the Texas Medical Center. Birth rates ranged from 1.4 to 16.5 per 10,000 total births. A cluster was identified in southwest Harris County, however, it was not significant (p=0.066).^ Conclusion. This study encourages further focus on linking cleft cases to environmental factors in order to determine potential risks. ^

A community-based assessment of the spatiotemporal distribution of influenza in a U.S.-Mexico border county during the spring 2009 novel H1N1 swine-origin influenza A pandemic

This study retrospectively evaluated the spatial and temporal disease patterns associated with influenza-like illness (ILI), positive rapid influenza antigen detection tests (RIDT), and confirmed H1N1 S-OIV cases reported to the Cameron County Department of Health and Human Services between April 26 and May 13, 2009 using the space-time permutation scan statistic software SaTScan in conjunction with geographical information system (GIS) software ArcGIS 9.3. The rate and age-adjusted relative risk of each influenza measure was calculated and a cluster analysis was conducted to determine the geographic regions with statistically higher incidence of disease. A Poisson distribution model was developed to identify the effect that socioeconomic status, population density, and certain population attributes of a census block-group had on that area's frequency of S-OIV confirmed cases over the entire outbreak. Predominant among the spatiotemporal analyses of ILI, RIDT and S-OIV cases in Cameron County is the consistent pattern of a high concentration of cases along the southern border with Mexico. These findings in conjunction with the slight northward space-time shifts of ILI and RIDT cluster centers highlight the southern border as the primary site for public health interventions. Finally, the community-based multiple regression model revealed that three factors—percentage of the population under age 15, average household size, and the number of high school graduates over age 25—were significantly associated with laboratory-confirmed S-OIV in the Lower Rio Grande Valley. Together, these findings underscore the need for community-based surveillance, improve our understanding of the distribution of the burden of influenza within the community, and have implications for vaccination and community outreach initiatives.^