IN-VIVO CT DOSIMETRY DURING VIRTUAL COLONOSCOPY

Virtual colonoscopy (VC) is a minimally invasive means for identifying colorectal polyps and colorectal lesions by insufflating a patient’s bowel, applying contrast agent via rectal catheter, and performing multi-detector computed tomography (MDCT) scans. The technique is recommended for colonic health screening by the American Cancer Society but not funded by the Centers for Medicare and Medicaid Services (CMS) partially because of potential risks from radiation exposure. To date, no in‐vivo organ dose measurements have been performed for MDCT scans; thus, the accuracy of any current dose estimates is currently unknown. In this study, two TLDs were affixed to the inner lumen of standard rectal catheters used in VC, and in-vivo rectal dose measurements were obtained within 6 VC patients. In order to calculate rectal dose, TLD-100 powder response was characterized at diagnostic doses such that appropriate correction factors could be determined for VC. A third-order polynomial regression with a goodness of fit factor of R2=0.992 was constructed from this data. Rectal dose measurements were acquired with TLDs during simulated VC within a modified anthropomorphic phantom configured to represent three sizes of patients undergoing VC. The measured rectal doses decreased in an exponential manner with increasing phantom effective diameter, with R2=0.993 for the exponential regression model and a maximum percent coefficient of variation (%CoV) of 4.33%. In-vivo measurements yielded rectal doses ranged from that decreased exponentially with increasing patient effective diameter, in a manner that was also favorably predicted by the size specific dose estimate (SSDE) model for all VC patients that were of similar age, body composition, and TLD placement. The measured rectal dose within a younger patient was favorably predicted by the anthropomorphic phantom dose regression model due to similarities in the percentages of highly attenuating material at the respective measurement locations and in the placement of the TLDs. The in-vivo TLD response did not increase in %CoV with decreasing dose, and the largest %CoV was 10.0%.

Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^

Development and Characterization of an in vitro Four-species Anaerobic Dental Biofilm Model

Dental caries is the most common chronic disease worldwide. It is characterized by the demineralization of tooth enamel caused by acid produced by cariogenic dental bacteria growing on tooth surfaces, termed bacterial biofilms. Cariogenesis is a complex biological process that is influence by multiple factors and is not attributed to a sole causative agent. Instead, caries is associated with multispecies microbial biofilm communities composed of some bacterial species that directly influence the development of a caries lesion and other species that are seemingly benign but must contribute to the community in an uncharacterized way. Clinical analysis of dental caries and its microbial populations is challenging due to many factors including low sensitivity of clinical measurement tools, variability in saliva chemistry, and variation in the microbiota. Our laboratory has developed an in vitro anaerobic biofilm model for dental carries to facilitate both clinical and basic research-based analyses of the multispecies dynamics and individual factors that contribute to cariogenicity. The rational for development of this system was to improve upon the current models that lack key elements. This model places an emphasis on physiological relevance and ease of maintenance and reproducibility. The uniqueness of the model is based on integrating four critical elements: 1) a biofilm community composed of four distinct and representative species typically associated with dental caries, 2) a semi-defined synthetic growth medium designed to mimic saliva, 3) physiologically relevant biofilm growth substrates, and 4) a novel biofilm reactor device designed to facilitate the maintenance and analysis. Specifically, human tooth sections or hydroxyapatite discs embedded into poly(methyl methacrylate) (PMMA) discs are incubated for an initial 24 hr in a static inverted removable substrate (SIRS) biofilm reactor at 37°C under anaerobic conditions in artificial saliva (CAMM) without sucrose in the presence of 1 X 106 cells/ml of each Actinomyces odontolyticus, Fusobacterium nucleatum, Streptococcus mutans, and Veillonella dispar. During days 2 and 3 the samples are maintained continually in CAMM with various exposures to 0.2% sucrose; all of the discs are transferred into fresh medium every 24 hr. To validate that this model is an appropriate in vitro representation of a caries-associated multispecies biofilm, research aims were designed to test the following overarching hypothesis: an in vitro anaerobic biofilm composed of four species (S. mutans, V. dispar, A. odontolyticus, and F. nucleatum) will form a stable biofilm with a community profile that changes in response to environmental conditions and exhibits a cariogenic potential. For these experiments the biofilms as described above were exposed on days 2 and 3 to either CAMM lacking sucrose (no sucrose), CAMM with 0.2% sucrose (constant sucrose), or were transferred twice a day for 1 hr each time into 0.2% sucrose (intermittent sucrose). Four types of analysis were performed: 1) fluorescence microscopy of biofilms stained with Syto 9 and hexidium idodine to determine the biofilm architecture, 2) quantitative PCR (qPCR) to determine the cell number of each species per cm2, 3) vertical scanning interferometry (VSI) to determine the cariogenic potential of the biofilms, and 4) tomographic pH imaging using radiometric fluorescence microscopy after exposure to pH sensitive nanoparticles to measure the micro-environmental pH. The qualitative and quantitative results reveal the expected dynamics of the community profile when exposed to different sucrose conditions and the cariogenic potential of this in vitro four-species anaerobic biofilm model, thus confirming its usefulness for future analysis of primary and secondary dental caries.

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^

The number and timing of time -dependent covariate measurements for the Cox regression model

The problem of analyzing data with updated measurements in the time-dependent proportional hazards model arises frequently in practice. One available option is to reduce the number of intervals (or updated measurements) to be included in the Cox regression model. We empirically investigated the bias of the estimator of the time-dependent covariate while varying the effect of failure rate, sample size, true values of the parameters and the number of intervals. We also evaluated how often a time-dependent covariate needs to be collected and assessed the effect of sample size and failure rate on the power of testing a time-dependent effect.^ A time-dependent proportional hazards model with two binary covariates was considered. The time axis was partitioned into k intervals. The baseline hazard was assumed to be 1 so that the failure times were exponentially distributed in the ith interval. A type II censoring model was adopted to characterize the failure rate. The factors of interest were sample size (500, 1000), type II censoring with failure rates of 0.05, 0.10, and 0.20, and three values for each of the non-time-dependent and time-dependent covariates (1/4,1/2,3/4).^ The mean of the bias of the estimator of the coefficient of the time-dependent covariate decreased as sample size and number of intervals increased whereas the mean of the bias increased as failure rate and true values of the covariates increased. The mean of the bias of the estimator of the coefficient was smallest when all of the updated measurements were used in the model compared with two models that used selected measurements of the time-dependent covariate. For the model that included all the measurements, the coverage rates of the estimator of the coefficient of the time-dependent covariate was in most cases 90% or more except when the failure rate was high (0.20). The power associated with testing a time-dependent effect was highest when all of the measurements of the time-dependent covariate were used. An example from the Systolic Hypertension in the Elderly Program Cooperative Research Group is presented. ^

The two -state model of receptor activation: The agonist and the efficacy

The Two State model describes how drugs activate receptors by inducing or supporting a conformational change in the receptor from “off” to “on”. The beta 2 adrenergic receptor system is the model system which was used to formalize the concept of two states, and the mechanism of hormone agonist stimulation of this receptor is similar to ligand activation of other seven transmembrane receptors. Hormone binding to beta 2 adrenergic receptors stimulates the intracellular production of cyclic adenosine monophosphate (cAMP), which is mediated through the stimulatory guanyl nucleotide binding protein (Gs) interacting with the membrane bound enzyme adenylylcyclase (AC). ^ The effects of cAMP include protein phosphorylation, metabolic regulation and transcriptional regulation. The beta 2 adrenergic receptor system is the most well known of its family of G protein coupled receptors. Ligands have been scrutinized extensively in search of more effective therapeutic agents at this receptor as well as for insight into the biochemical mechanism of receptor activation. Hormone binding to receptor is thought to induce a conformational change in the receptor that increases its affinity for inactive Gs, catalyzes the release of GDP and subsequent binding of GTP and activation of Gs. ^ However, some beta 2 ligands are more efficient at this transformation than others, and the underlying mechanism for this drug specificity is not fully understood. The central problem in pharmacology is the characterization of drugs in their effect on physiological systems, and consequently, the search for a rational scale of drug effectiveness has been the effort of many investigators, which continues to the present time as models are proposed, tested and modified. ^ The major results of this thesis show that for many b2 -adrenergic ligands, the Two State model is quite adequate to explain their activity, but dobutamine (+/−3,4-dihydroxy-N-[3-(4-hydroxyphenyl)-1-methylpropyl]- b -phenethylamine) fails to conform to the predictions of the Two State model. It is a weak partial agonist, but it forms a large amount of high affinity complexes, and these complexes are formed at low concentrations much better than at higher concentrations. Finally, dobutamine causes the beta 2 adrenergic receptor to form high affinity complexes at a much faster rate than can be accounted for by its low efficiency activating AC. Because the Two State model fails to predict the activity of dobutamine in three different ways, it has been disproven in its strictest form. ^