INTRODUÇÃO E OBJETIVO: Recidiva da hepatite C crônica com progressão acelerada, embora imprevisível, da fibrose é responsável por piora no prognóstico após o transplante de fígado (Tx). Biópsia hepática protocolar é considerada o padrão ouro para estadiamento da fibrose na recidiva da hepatite C pós-Tx. Para superar as limitações da biópsia, principalmente custo e complicações, marcadores simples e não invasivos de fibrose hepática têm sido propostos para pacientes imunocompetentes, porém com escassos estudos disponíveis no contexto pós-Tx. O objetivo desse estudo é avaliar o desempenho diagnóstico dos marcadores não-invasivos para estadiar fibrose hepática em pacientes pós-Tx. MÉTODOS: Pacientes consecutivos receptores de Tx com recidiva da hepatite C (n=45) que foram submetidos a 118 biópsias hepáticas foram incluídos. Variáveis laboratoriais dentro de trinta dias de cada biópsia foram consideradas. Índice da razão AST-plaqueta (APRI), razão AST/ALT, Escore discriminativo de Bonacini (EDB), Escore de Pohl e índice idade-plaqueta foram calculados para cada biópsia. Fibrose significante foi definida como estágio METAVIR 2. RESULTADO: A área sob a curva ROC (receiver operating characteristic) do Escore discriminativo de Bonacini para predizer fibrose significante foi 0,68, superior aos outros testes avaliados. Utilizando-se o melhor ponto de corte, um valor de Escore discriminativo de Bonacini 8 foi 42% sensível e 95% específico, com razão de verossimilhança positiva e negativa de 7,98 e 0,62, respectivamente. Análise multivariada identificou razão AST/ALT como preditor independente de fibrose significante (OR=4.2; CI 95%=1.5-11.4; p-valor=0.005, ponto de corte 0,89). Análise adicional considerando apenas uma biópsia por paciente confirmou o desempenho superior do Escore discriminativo de Bonacini em relaçãoaos outros testes avaliados, com uma área sob a curva de 0,76. CONCLUSÃO: Escore discriminativo de Bonacini foi o marcador laboratorial não invasivo com melhor desempenho diagnóstico para predizer fibrose hepática significante em pacientes com recidiva de hepatite C crônica pós-Tx / BACKGROUND AND AIM: Recurrent hepatitis C with accelerated, although unpredictable, fibrosis progression accounts for a poor prognosis after liver transplantation (LT). Per protocol liver biopsy is considered the gold standard for fibrosis staging in recurrent hepatitis C after LT to overcome the limitations of liver biopsy, mainly cost and complications, simple non-invasive liver fibrosis tests have been proposed for immunocompetent patients, butfew data are available in the post-transplant setting. The aim of this study was to evaluate diagnostic performance of noninvasive tests to stage liver fibrosis in LT setting. METHOD: Consecutive LT patients with recurrent hepatitis C (n=45) who have undergone 118 liver biopsy were included. Laboratory variables at the time of biopsies were recorded. AST to platelet ratio index (APRI), AST/ALT ratio, Bonacini discriminant score (BDS), Pohl score and age-platelet index were calculated at the time of biopsies. Significant fibrosis was defined as METAVIR stage 2. RESULT: The area under the receiver operating characteristic (ROC) curve (AUC) of Bonacini discriminant score for predicting significant fibrosis was 0,68, better than the other non-invasive liver fibrosis tests. Using the best cutoff value, Bonacini discriminant score value 8 was 42% sensitive and 95% specific, with positive and negative likelihood ratio of 7,98 and 0,62, respectively. Multivariate analysis identified AST/ALT ratio as an independent predictor of significant fibrosis (OR=4.2; CI 95%=1.5-11.4; p-value=0.005, cutoff point 0,89). Additional analysis considering only one biopsy per patient confirmed the superior performance of Bonacini discriminant score compared to the other non-invasive liver fibrosis tests, with an AUC of 0,76. CONCLUSION: Bonacini discriminant score was the non-invasive liver fibrosis test with the best performance for significant liver transplant patients with recurrent hepatitis C

Made available in DSpace on 2015-04-14T13:29:13Z (GMT). No. of bitstreams: 1 384677.pdf: 3951111 bytes, checksum: 684b9b53ca958aa688d3b2186dfb12ca (MD5) Previous issue date: 2006-10-06 / O objetivo do presente estudo foi comparar as imagens digitais com a radiografia convencional no diagn?stico de c?ries proximais. A amostra foi composta por 51 molares e 24 pr?-molares, distribu?dos em grupos de tr?s dentes, montados em blocos de silicone, e radiografados pela t?cnica interproximal, empregando-se o filme InSight (Kodak) e os sistemas digitais Digora? (Soredex), DenOptix (Gendex) e CygnusRay MPS (Progeny). Foram obtidas 25 radiografias em cada m?todo radiogr?fico e analisadas quatro faces proximais em cada radiografia, totalizando 400 faces. As radiografias foram examinadas individualmente, por um observador, em tr?s diferentes momentos para cada m?todo. As les?es de c?rie foram classificadas de acordo com sua profundidade em: (0), ausente; (1), restrita ao esmalte; (2), atingindo a jun??o amelodentin?ria e (3), estendendo-se para a dentina. Para a obten??o do padr?o-ouro, os dentes foram seccionados e desgastados, sendo examinados histologicamente por estereomicroscopia. Para an?lise da concord?ncia intra-observador utilizou-se o teste de Kendall, com o qual constatou-se uma boa concord?ncia (0,831) entre as tr?s avalia??es realizadas. Foram calculados a acur?cia, a sensibilidade, a especificidade, o valor preditivo positivo (VPP), o valor preditivo negativo (VPN) e o ?ndice Kappa, n?o se observando diferen?as estat?sticas significativas entre os valores para os quatro m?todos estudados (ANOVA, p≥0,05). Os m?todos radiogr?ficos tamb?m foram comparados utilizando-se o teste n?o-param?trico de Friedman, complementado pelo seu teste de compara??es m?ltiplas, e igualmente, n?o se observaram diferen?as estat?sticas significativas entre os quatro m?todos. Ainda, para cada m?todo radiogr?fico calculou-se a curva ROC e, na compara??o entre as ?reas sob estas curvas, n?o se verificaram diferen?as estat?sticas ao n?vel de signific?ncia de 5%. Concluiu-se que a acur?cia diagn?stica das imagens digitais na detec??o de c?ries proximais ? similar ?quela das radiografias convencionais.

Docking is a computer simulation method used to predict the preferred orientation of two interacting chemical species that has been successfully applied to numerous macromolecules over the years. However, non-traditional targets have inherent difficulties associated with their screening. Large interfaces, lack of obvious binding sites, and transient pockets are some examples. Additionally, most natural ligands of challenging targets are inadequate models for identifying or designing new ligands. Therefore, it is not surprising that customary techniques of structure-based virtual screening are incompatible with these non-traditional targets. We hypothesized that an integrative virtual screening campaign comprised of docking followed by refinement of best receptor–ligand complexes would effectively identify small-molecule ligands of challenging receptors. We targeted the single-stranded DNA (ssDNA) binding groove of the human RAD52, and a cryptic allosteric pocket of the Helicobacter pylori Glutamate Racemase (GR). In this project, we first determined which docking method was more appropriate for each studied non-traditional target, and then examined how good our two-step docking workflow was in finding novel active ligand scaffolds. This research developed a powerful layered virtual screening workflow for the discovery of lead compounds against challenging protein targets. Furthermore, we successfully applied a statistical analysis method, which used receiver operating characteristic (ROC) curves, to validate the selected docking protocol that would be used in the screening campaigns. Using the validated workflow, we identified a natural compound that competes with ssDNA to bind to RAD52. The performed screening campaigns also provided new insights into the studied binding pockets, as well as structure-activity relationships (SAR) and binding determinants of the ligands. Our achievements reinforce the power of the ROC curve analysis approach in directing the search for the most appropriate docking protocol and helping to speed up drug discovery in pharmaceutical research.

Introduction: The structural components variance estimator proposed by DeLong et al. (1988) is a popular approach used when comparing two correlated AUCs. However, this variance estimator is biased and could be problematic with small sample sizes. Methods: A U-statistics based variance estimator approach is presented and compared with the structural components variance estimator through a large-scale simulation study under different finite-sample size configurations. Results: The U-statistics variance estimator was unbiased for the true variance of the difference between correlated AUCs regardless of the sample size and had lower RMSE than the structural components variance estimator, providing better type 1 error control and larger power. The structural components variance estimator provided increasingly biased variance estimates as the correlation between biomarkers increased. Discussion: When comparing two correlated AUCs, it is recommended that the U-Statistics variance estimator be used whenever possible, especially for finite sample sizes and highly correlated biomarkers.

本論文基本上係在對冷戰後中日兩國關係演變上的中日兩國對對方國家政策取向之具體探討 根據實際資料分析研判 歸納現有問題與窒礙之處 並且探究可能之改進之道與發展趨勢其主要內容第一 回顧自第二次世界大戰以後之中日關係 而歸結其政治關係趨向 經濟關係趨向及政治與經濟之間的關係 第二 加以分析臺灣的對日政策之各因素 和日本對台政策的各項因素 如國際體系國際關係 社會 政府及個人等因素 第三 綜合上述分析 究明如所前述的因素之間的關係 及結構性質 最後根據這些分析展望未來的中日關係

本文係從與考績相關理論之探討著眼，研究我國公務人員考績制度的問題，希能透過理論的啟迪，歸納出理想型考績制度的模式、中心思想、指導原則及考績制度應達成之目標與功能，俾作為改進現行考績制度之指南。在現行考績制度之檢討上，本文除對現制作一說明外，為瞭解現制形成的原因，故摘要比較與舊制的關係，以歸納出現制的特色，並剖析其得失與施行的限制，作為改進的依據。復以全國最高人事主管機關--銓敘部曾於八十四年召開全國人事主管會報時，提出擬議修訂考績法的內容，其中固有頗多創見及銳意改革的意圖，惟其主要興革意見並未獲與會各級人事人員支持，仔細研議結果，亦發現若干難以契合之處，爰一併納入討論範圍，希能找出因應的對策。在考績制度的範圍上，為全面瞭解公務人員考核事宜，故本文除將公務人員考績制度納入研討範圍外，亦將若干與公務人員考核有關之人事制度列入討論範圍。最後，根據筆者多年實務經驗及相關理論之研究結果，研提落實考績制度之方向與作法，並於結論中研提具體實施步驟及相關人事制度應遵守之原則，希對公務人員考績制度之健全發展有所裨益。

Reliable results are crucial when working with medical decision support systems. A decision support system should be reliable but also be interpretable, i.e. able to show how it has inferred its conclusions. In this thesis, the preprocessing perceptron is presented as a simple but effective and efficient analysis method to consider when creating medical decision support systems. The preprocessing perceptron has the simplicity of a perceptron combined with a performance comparable to the multi-layer perceptron. The research in this thesis has been conducted within the fields of medical informatics and intelligent computing. The original idea of the production line as a tool for a domain expert to extract information, build decision support systems and integrate them in the existing system is described. In the introductory part of the thesis, an introduction to feed-forward neural networks and fuzzy logic is given as a background to work with the preprocessing perceptron. Input to a decision support system is crucial and it is described how to gather a data set, decide how many and what kind of inputs to use. Outliers, errors and missing data are covered as well as normalising of the input. Training is done in a backpropagation-like manner where the division of the data set into a training and a test set can be done in several different ways just as the training itself can have variations. Three major groups of methods to estimate the discriminance effect of the preprocessing perceptron are described and a discussion of the trade-off between complexity and approximation strength are included. Five papers are presented in this thesis. Case studies are shown where the preprocessing perceptron is compared to multi-layer perceptrons, statistical approaches and other mathematical models. The model is extended to a generalised preprocessing perceptron and the performance of this new model is compared to the traditional feed-forward neural networks. Results concerning the preprocessing layer and its connection to multivariate decision limits are included. The well-known ROC curve is described and introduced fully into the field of computer science as well as the improved curve, the QROC curve. Finally a tutorial to the program trainGPP is presented. It describes how to work with the preprocessing perceptron from the moment when a data file is provided to the moment when a new decision support system is built.

In this dissertation, we investigate jackknife empirical likelihood methods motivated by recent statistics research and other related fields. Computational intensity of empirical likelihood can be significantly reduced by using jackknife empirical likelihood methods without losing computational accuracy and stability. We demonstrate that proposed jackknife empirical likelihood methods are able to handle several challenging and open problems in terms of elegant asymptotic properties and accurate simulation result in finite samples. These interesting problems include ROC curves with missing data, the difference of two ROC curves in two dimensional correlated data, a novel inference for the partial AUC and the difference of two quantiles with one or two samples. In addition, empirical likelihood methodology can be successfully applied to the linear transformation model using adjusted estimation equations. The comprehensive simulation studies on coverage probabilities and average lengths for those topics demonstrate the proposed jackknife empirical likelihood methods have a good performance in finite samples under various settings. Moreover, some related and attractive real problems are studied to support our conclusions. In the end, we provide an extensive discussion about some interesting and feasible ideas based on our jackknife EL procedures for future studies.

Most of the cardiac structural abnormalities will be examined by echocardiography. With more understanding of heart diseases, it is commonly recognized that heart failures are closely related to left ventricular systolic and diastolic functions. This work discusses the association between gray-scale differences and the risk of heart disease from the changes in left ventricular systole and diastole of ultrasound image. Owing to the large dimension of data matrix, following Chen (2011), we also simplify the influence factors by factor analysis and calculate factor scores to present the characteristics of subjects. Two kinds of classification criteria are used in this work, namely logistic model with distance indicator and discriminant function. According to Guo et al. (2001), we calculate the Mahalanobis distance from each subject to the center of normal and abnormal group, then use logistic model to fit the distances for classification later. This is called logistic model with distance indicator. For the discriminant analysis, the regularized method by Friedman (1989) for estimation of covariance matrix is used, which is more flexible and can improve the covariance matrix estimates when the sample size is small. As far as the cut-point of ROC curve, following the approach as in Hanley et al. (1982), we find the most appropriate cut-point which has good performances for both sensitivity and specificity under the same classification criteria. Then the regularized method and the cut-point of ROC curve are combined to be a new classification criterion. The results under the new classification criterion are presented to classify normal and abnormal groups.

Title from PDF of title page. / Document formatted into pages; contains 112 pages. / Thesis (M.S.C.S.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Breast cancer is the second leading cause of cancer deaths among women in the United States and microcalcifications clusters are one of the most important indicators of breast disease. Computer methodologies help in the detection and differentiation between benign and malignant lesions and have the potential to improve radiologists' performance and breast cancer diagnosis significantly. A Computer-Aided Diagnosis (CAD-Dx) algorithm has been previously developed to assist radiologists in the diagnosis of mammographic clusters of calcifications with the modules: (a) detection of all calcification-like areas, (b) false-positive reduction and segmentation of the detected calcifications, (c) selection of morphological and distributional features and (d) classification of the clusters. Classification was based on an artificial neural network (ANN) with 14 input features and assigned a likelihood of malignancy to each cluster. / ABSTRACT: The purpose of this work was threefold: (a) optimize the existing algorithm and test on a large database, (b) rank classification features and select the best feature set, and (c) determine the impact of single and two-view feature estimation on classification and feature ranking. Classification performance was evaluated with the NevProp4 artificial neural network trained with the leave-one-out resampling technique. Sequential forward selection was used for feature selection and ranking. Mammograms from 136 patients, containing single or two views of a breast with calcification cluster were digitized at 60 microns and 16 bits per pixel. 260 regions of interest (ROI's) centered on calcification cluster were defined to build the single-view dataset. 100 of the 136 patients had a two-view mammogram which yielded 202 ROI's that formed the two-view dataset. Classification and feature selection were evaluated with both these datasets. / ABSTRACT: To decide on the optimal features for two-view feature estimation several combinations of CC and MLO view features were attempted. On the single-view dataset the classifier achieved an AZ =0.8891 with 88% sensitivity and 77% specificity at an operating point of 0.4; 12 features were selected as the most important. With the two-view dataset, the classifier achieved a higher performance with an AZ =0.9580 and sensitivity and specificity of 98% and 80% respectively at an operating point of 0.4; 10 features were selected as the most important. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.