Effectiveness of Fluorescence-based Methods in Monitoring Progression of Noncavitated Caries-like Lesions on Smooth Surfaces

Although there has been a significant decrease in caries prevalence in developed countries, the slower progression of dental caries requires methods capable of detecting and quantifying lesions at an early stage. The aim of this study was to evaluate the effectiveness of fluorescence-based methods (DIAGNOdent 2095 laser fluorescence device [LF], DIAGNOdent 2190 pen [LFpen], and VistaProof fluorescence camera [FC]) in monitoring the progression of noncavitated caries-like lesions on smooth surfaces. Caries-like lesions were developed in 60 blocks of bovine enamel using a bacterial model of Streptococcus mutans and Lactobacillus acidophilus . Enamel blocks were evaluated by two independent examiners at baseline (phase I), after the first cariogenic challenge (eight days) (phase II), and after the second cariogenic challenge (a further eight days) (phase III) by two independent examiners using the LF, LFpen, and FC. Blocks were submitted to surface microhardness (SMH) and cross-sectional microhardness analyses. The intraclass correlation coefficient for intra- and interexaminer reproducibility ranged from 0.49 (FC) to 0.94 (LF/LFpen). SMH values decreased and fluorescence values increased significantly among the three phases. Higher values for sensitivity, specificity, and area under the receiver operating characteristic curve were observed for FC (phase II) and LFpen (phase III). A significant correlation was found between fluorescence values and SMH in all phases and integrated loss of surface hardness (ΔKHN) in phase III. In conclusion, fluorescence-based methods were effective in monitoring noncavitated caries-like lesions on smooth surfaces, with moderate correlation with SMH, allowing differentiation between sound and demineralized enamel.

Influence of contact points on the performance of caries detection methods in approximal surfaces of primary molars: an in vivo study

This in vivo study aimed to evaluate the influence of contact points on the approximal caries detection in primary molars, by comparing the performance of the DIAGNOdent pen and visual-tactile examination after tooth separation to bitewing radiography (BW). A total of 112 children were examined and 33 children were selected. In three periods (a, b, and c), 209 approximal surfaces were examined: (a) examiner 1 performed visual-tactile examination using the Nyvad criteria (EX1); examiner 2 used DIAGNOdent pen (LF1) and took BW; (b) 1 week later, after tooth separation, examiner 1 performed the second visual-tactile examination (EX2) and examiner 2 used DIAGNOdent again (LF2); (c) after tooth exfoliation, surfaces were directly examined using DIAGNOdent (LF3). Teeth were examined by computed microtomography as a reference standard. Analyses were based on diagnostic thresholds: D1: D 0 = health, D 1 –D 4 = disease; D2: D 0 , D 1 = health, D 2 –D 4 = disease; D3: D 0 –D 2 = health, D 3 , D 4 = disease. At D1, the highest sensitivity/specificity were observed for EX1 (1.00)/LF3 (0.68), respectively. At D2, the highest sensitivity/ specificity were observed for LF3 (0.69)/BW (1.00), respectively. At D3, the highest sensitivity/specificity were observed for LF3 (0.78)/EX1, EX2 and BW (1.00). EX1 showed higher accuracy values than LF1, and EX2 showed similar values to LF2. We concluded that the visual-tactile examination showed better results in detecting sound surfaces and approximal caries lesions without tooth separation. However, the effectiveness of approximal caries lesion detection of both methods was increased by the absence of contact points. Therefore, regardless of the method of detection, orthodontic separating elastics should be used as a complementary tool for the diagnosis of approximal noncavitated lesions in primary molars.

Treatment planning aspects and Monte Carlo methods in proton therapy

Over the last years, the interest in proton radiotherapy is rapidly increasing. Protons provide superior physical properties compared with conventional radiotherapy using photons. These properties result in depth dose curves with a large dose peak at the end of the proton track and the finite proton range allows sparing the distally located healthy tissue. These properties offer an increased flexibility in proton radiotherapy, but also increase the demand in accurate dose estimations. To carry out accurate dose calculations, first an accurate and detailed characterization of the physical proton beam exiting the treatment head is necessary for both currently available delivery techniques: scattered and scanned proton beams. Since Monte Carlo (MC) methods follow the particle track simulating the interactions from first principles, this technique is perfectly suited to accurately model the treatment head. Nevertheless, careful validation of these MC models is necessary. While for the dose estimation pencil beam algorithms provide the advantage of fast computations, they are limited in accuracy. In contrast, MC dose calculation algorithms overcome these limitations and due to recent improvements in efficiency, these algorithms are expected to improve the accuracy of the calculated dose distributions and to be introduced in clinical routine in the near future.

Explicit and Implicit Methods In Solving Differential Equations

Differential equations are equations that involve an unknown function and derivatives. Euler's method are efficient methods to yield fairly accurate approximations of the actual solutions. By manipulating such methods, one can find ways to provide good approximations compared to the exact solution of parabolic partial differential equations and nonlinear parabolic differential equations.

Genetic epidemiologic methods in risk determination in Li-Fraumeni syndrome

Li-Fraumeni syndrome (LFS) is characterized by a variety of neoplasms occurring at a young age with an apparent autosomal dominant transmission. Individuals in pedigrees with LFS have high incidence of second malignancies. Recently LFS has been found to be associated with germline mutations of a tumor-suppressor gene, p53. Because LFS is rare and indeed not a clear-cut disease, it is not known whether all cases of LFS are attributable to p53 germline mutations and how p53 plays in cancer occurrence in such cancer syndrome families. In the present study, DNAs from constitutive cells of two-hundred and thirty-three family members from ten extended pedigrees were screened for p53 mutations. Six out of the ten LFS families had germline mutations at the p53 locus, including point and deletion mutations. In these six families, 55 out of 146 members were carriers of p53 mutations. Except one, all mutations occurred in exons 5 to 8 (i.e., the "hot spot" region) of the p53 gene. The age-specific penetrance of cancer was estimated after the genotype for each family member at risk was determined. The penetrance was 0.15, 0.29, 0.35, 0.77, and 0.91 by 20, 30, 40, 50 and 60 year-old, respectively, in male carriers; 0.19, 0.44, 0.76, and 0.90 by 20, 30, 40, and 50 year-old, respectively, in female carriers. These results indicated that one cannot escape from tumorigenesis if one inherits a p53 mutant allele; at least ninety percent of p53 carriers will develop cancer by the age of 60. To evaluate the possible bias due to the unexamined blood-relatives in LFS families, I performed a simulation analysis in which a p53 genotype was assigned to each unexamined person based on his cancer status and liability to cancer. The results showed that the penetrance estimates were not biased by the unexamined relatives. I also determined the sex, site, and age-specific penetrance of breast cancer in female carriers and lung cancer in male carriers. The penetrance of breast cancer in female carriers was 0.81 by age 45; the penetrance of lung cancer in male carriers was 0.78 by age 60, indicating that p53 play a key role for tumorigenesis in common cancers. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Complications of misoprostol and other abortion induction methods in the developing world: A systematic review

OBJECTIVE: To systematically review published literature to examine the complications associated with the use of misoprostol and compare these complications to those associated with other forms of abortion induction. ^ DATA SOURCES: Studies were identified through searches of medical literature databases including Medline (Ovid), PubMed (NLM), LILACS, sciELO, and AIM (AFRO), and review of references of relevant articles. ^ STUDY SELECTION AND METHODS: A descriptive systematic review that included studies reported in English and published before December 2012. Eligibility criteria included: misoprostol (with or without other methods) and any other method of abortion in a developing country, as well as quantitative data on the complication of each method. The following is information extracted from each study: author/year, country/city, study design/study sample, age range, setting of data collection, sample size, the method of abortion induction, the number of cases for each method, and the percentage of complications with each method. RESULTS: A total of 4 studies were identified (all in Latin America) describing post-abortion complications of misoprostol and other methods in countries where abortion is generally considered unsafe and/or illegal. The four studies reported on a range of complications including: bleeding, infection, incomplete abortion, intense pelvic pain, uterine perforation, headache, diarrhea, nausea, mechanical lesions, and systemic collapse. The most prevalent complications of misoprostol-induced abortion reported were: bleeding (7-82%), incomplete abortion (33-70%), and infection (0.8-67%). The prevalence of these complications reported from other abortion methods include: bleeding (16-25%), incomplete abortion (15-82%), and infection (13-50%). ^ CONCLUSION: The literature identified by this systematic review is inadequate for determining the complications of misoprostol used in unsafe settings. Abortion is considered an illicit behavior in these countries, therefore making it difficult to investigate the details needed to conduct a study on abortion complications. Given the differences between the reviewed studies as well as a variety of study limitations, it is not possible to draw firm conclusions about the rates of specific-abortion related complications.^

Relative Distribution Methods in Stata

The concept of the relative density seems like a fruitful nonparametric approach to studying distributional differences between groups (Handcock and Morris 1999), yet it appears that the technique has gone more or less unnoticed in applied social science research. A scarcity of canned software might be one of the reasons the method is underutilized. Therefore, I present a new Stata command called reldist to plot the relative density, decompose distributional differences into location and shape effects, and compute relative distribution summary measures. The command is illustrated by an application comparing earnings by sex.