In the fields of process control engineering and robotics, especially in automatic control, optimization challenges frequently manifest as complex problems with expensive evaluations. This thesis zeroes in on one such problem: the optimization of camera positions for Convolutional Neural Networks (CNNs). CNNs have specific attention points in images that are often not intuitive to human perception, making camera placement critical for performance. The research is guided by two primary questions. The first investigates the role of Explainable Artificial Intelligence (XAI), specifically GradCAM++ visual explanations, in Computer Vision for aiding in the evaluation of different camera positions. Building on this, the second question assesses a novel algorithm that leverages these XAI features against traditional black-box optimization methods. To answer these questions, the study employs a robotic auto-positioning system for data collection, CNN model training, and performance evaluation. A case study focused on classifying flow regimes in industrial-grade bioreactors validates the method. The proposed approach shows improvements over established techniques like Grid Search, Random Search, Bayesian optimization, and Simulated Annealing. Future work will focus on gathering more data and including noise for generalized conclusions.:Contents 1 Introduction 1.1 Motivation 1.2 Problem Analysis 1.3 Research Question 1.4 Structure of the Thesis 2 State of the Art 2.1 Literature Research Methodology 2.1.1 Search Strategy 2.1.2 Inclusion and Exclusion Criteria 2.2 Blackbox Optimization 2.3 Mathematical Notation 2.4 Bayesian Optimization 2.5 Simulated Annealing 2.6 Random Search 2.7 Gridsearch 2.8 Explainable A.I. and Saliency Maps 2.9 Flowregime Classification in Stirred Vessels 2.10 Performance Metrics 2.10.1 R2 Score and Polynomial Regression for Experiment Data Analysis 2.10.2 Blackbox Optimization Performance Metrics 2.10.3 CNN Performance Metrics 3 Methodology 3.1 Requirement Analysis and Research Hypothesis 3.2 Research Approach: Case Study 3.3 Data Collection 3.4 Evaluation and Justification 4 Concept 4.1 System Overview 4.2 Data Flow 4.3 Experimental Setup 4.4 Optimization Challenges and Approaches 5 Data Collection and Experimental Setup 5.1 Hardware Components 5.2 Data Recording and Design of Experiments 5.3 Data Collection 5.4 Post-Experiment 6 Implementation 6.1 Simulation Unit 6.2 Recommendation Scalar from Saliency Maps 6.3 Saliency Map Features as Guidance Mechanism 6.4 GradCam++ Enhanced Bayesian Optimization 6.5 Benchmarking Unit 6.6 Benchmarking 7 Results and Evaluation 7.1 Experiment Data Analysis 7.2 Recommendation Scalar 7.3 Benchmarking Results and Quantitative Analysis 7.3.1 Accuracy Results from the Benchmarking Process 7.3.2 Cumulative Results Interpretation 7.3.3 Analysis of Variability 7.4 Answering the Research Questions 7.5 Summary 8 Discussion 8.1 Critical Examination of Limitations 8.2 Discussion of Solutions to Limitations 8.3 Practice-Oriented Discussion of Findings 9 Summary and Outlook / Im Bereich der Prozessleittechnik und Robotik, speziell bei der automatischen Steuerung, treten oft komplexe Optimierungsprobleme auf. Diese Arbeit konzentriert sich auf die Optimierung der Kameraplatzierung in Anwendungen, die Convolutional Neural Networks (CNNs) verwenden. Da CNNs spezifische, für den Menschen nicht immer ersichtliche, Merkmale in Bildern hervorheben, ist die intuitive Platzierung der Kamera oft nicht optimal. Zwei Forschungsfragen leiten diese Arbeit: Die erste Frage untersucht die Rolle von Erklärbarer Künstlicher Intelligenz (XAI) in der Computer Vision zur Bereitstellung von Merkmalen für die Bewertung von Kamerapositionen. Die zweite Frage vergleicht einen darauf basierenden Algorithmus mit anderen Blackbox-Optimierungstechniken. Ein robotisches Auto-Positionierungssystem wird zur Datenerfassung und für Experimente eingesetzt. Als Lösungsansatz wird eine Methode vorgestellt, die XAI-Merkmale, insbesondere solche aus GradCAM++ Erkenntnissen, mit einem Bayesschen Optimierungsalgorithmus kombiniert. Diese Methode wird in einer Fallstudie zur Klassifizierung von Strömungsregimen in industriellen Bioreaktoren angewendet und zeigt eine gesteigerte performance im Vergleich zu etablierten Methoden. Zukünftige Forschung wird sich auf die Sammlung weiterer Daten, die Inklusion von verrauschten Daten und die Konsultation von Experten für eine kostengünstigere Implementierung konzentrieren.:Contents 1 Introduction 1.1 Motivation 1.2 Problem Analysis 1.3 Research Question 1.4 Structure of the Thesis 2 State of the Art 2.1 Literature Research Methodology 2.1.1 Search Strategy 2.1.2 Inclusion and Exclusion Criteria 2.2 Blackbox Optimization 2.3 Mathematical Notation 2.4 Bayesian Optimization 2.5 Simulated Annealing 2.6 Random Search 2.7 Gridsearch 2.8 Explainable A.I. and Saliency Maps 2.9 Flowregime Classification in Stirred Vessels 2.10 Performance Metrics 2.10.1 R2 Score and Polynomial Regression for Experiment Data Analysis 2.10.2 Blackbox Optimization Performance Metrics 2.10.3 CNN Performance Metrics 3 Methodology 3.1 Requirement Analysis and Research Hypothesis 3.2 Research Approach: Case Study 3.3 Data Collection 3.4 Evaluation and Justification 4 Concept 4.1 System Overview 4.2 Data Flow 4.3 Experimental Setup 4.4 Optimization Challenges and Approaches 5 Data Collection and Experimental Setup 5.1 Hardware Components 5.2 Data Recording and Design of Experiments 5.3 Data Collection 5.4 Post-Experiment 6 Implementation 6.1 Simulation Unit 6.2 Recommendation Scalar from Saliency Maps 6.3 Saliency Map Features as Guidance Mechanism 6.4 GradCam++ Enhanced Bayesian Optimization 6.5 Benchmarking Unit 6.6 Benchmarking 7 Results and Evaluation 7.1 Experiment Data Analysis 7.2 Recommendation Scalar 7.3 Benchmarking Results and Quantitative Analysis 7.3.1 Accuracy Results from the Benchmarking Process 7.3.2 Cumulative Results Interpretation 7.3.3 Analysis of Variability 7.4 Answering the Research Questions 7.5 Summary 8 Discussion 8.1 Critical Examination of Limitations 8.2 Discussion of Solutions to Limitations 8.3 Practice-Oriented Discussion of Findings 9 Summary and Outlook

Pre-surgical diffusion weighted imaging (DWI) is increasingly important in the context of thyroid cancer for identification of the optimal treatment strategy. It has exemplarily been shown that DWI at 3T can distinguish undifferentiated from well-differentiated thyroid carcinoma, which has decisive implications for the magnitude of surgery. This study used DWI histogram analysis of whole tumor apparent diffusion coefficient (ADC) maps. The primary aim was to discriminate thyroid carcinomas which had already gained the capacity to metastasize lymphatically from those not yet being able to spread via the lymphatic system. The secondary aim was to reflect prognostically important tumor-biological features like cellularity and proliferative activity with ADC histogram analysis. Fifteen patients with follicular-cell derived thyroid cancer were enrolled. Lymph node status, extent of infiltration of surrounding tissue, and Ki-67 and p53 expression were assessed in these patients. DWI was obtained in a 3T system using b values of 0, 400, and 800 s/mm2 . Whole tumor ADC volumes were analyzed using a histogram-based approach. Several ADC parameters showed significant correlations with immunohistopathological parameters. Most importantly, ADC histogram skewness and ADC histogram kurtosis were able to differentiate between nodal negative and nodal positive thyroid carcinoma. Conclusions: histogram analysis of whole ADC tumor volumes has the potential to provide valuable information on tumor biology in thyroid carcinoma. However, further studies are warranted.

Die historische Holzkohleproduktion spielte eine bedeutende Rolle in der industriellen Entwicklung. Traditionell wurde Holzkohle in sogenannten Meilern, aufrechtstehenden Öfen, hergestellt. Diese Praxis führte zur weitreichenden Abholzung und veränderte die Vegetationszusammensetzung. Um die historische Waldbedeckung und historischen Landnutzungspraktiken besser zu verstehen, ist es notwendig, die räumliche Verteilung der Meiler zu analysieren. Die manuelle Kartierung der Meilerüberreste mittels DGM-Visualisierungstechniken ist sehr zeit- und arbeitsintensiv. Diese Arbeit untersucht daher den Einsatz von Deep Learning zur automatischen Detektion von Meilerplätzen basierend auf LiDAR-Datenprodukten. Hierfür wurden vortrainierte Modelle der Toolbox MMDetection mit DGM-Bildern trainiert, um ein spezifisch auf Meiler abgestimmtes Modell zu entwickeln. Insgesamt wurden vier Experimente durchgeführt, die den Einfluss verschiedener DGM-Visualisierungen, die Größe der Bounding Boxen und Hyperparameter unter Verwendung des FoveaBox-Detektors sowie die Leistung unterschiedlicher Modelle (ATSS, VFNet, RetinaNet) analysierten. Die Ergebnisse zeigen, dass ein 3-Band Bild bestehend aus Hügelschattierung, Sky-View Faktor und Neigung sowie eine Bounding Box Größe von 50 m optimal für die Detektion von Meilern sind. Der FoveaBox-Detektor erzielte die beste Leistung mit dem RAdam-Optimierer und einer Lernrate von 0.0001, wobei das ATSS-Modell mit den gleichen Hyperparametern die schlüssigsten Ergebnisse mit einer Genauigkeit von 93 % erreichte und nur 7 % der Meiler übersah. Das ATSS-Modell zeigte im Gegensatz zu anderen Studien eine um bis zu 10 % bessere Leistung. Ausschlaggebende Faktoren für diese Verbesserungen waren der verwendete Datensatz aus den 3-Band Bildern, die Größe der Bounding Boxen und die umfangreichere Datenaugmentierung, insbesondere die ergänzende Nutzung radiometrischer Techniken. Durch die experimentelle Herangehensweise konnte die Erkennungsgenauigkeit um 13 % gesteigert werden. Im Vergleich zur manuellen Kartierung hat das Modell viele zusätzliche Meiler identifiziert, obwohl es gelegentlich zu Verwechslungen mit angehäufter Erde am Hang und Fehldetektionen in unebenem Gelände mit geringen Höhenunterschieden kam. Die Eignung des Algorithmus zur verbesserten Erkennung von Meilerplätzen anstelle der manuellen Kartierung wird als effizienter, aber nicht zwangsläufig als präziser eingeschätzt:Selbständigkeitserklärung II Weitergabe der Arbeit II Kurzfassung IV Abstract V Abbildungsverzeichnis VIII Tabellenverzeichnis X Abkürzungsverzeichnis XI 1 Einleitung 1 1.1 Problemstellung und Zielsetzung 1 1.2 Aufbau der Arbeit 2 2 Grundlagen 3 2.1 Historischer und archäologischer Kontext von Meilerplätzen 3 2.1.1 Holzkohleproduktion und ihre Auswirkungen auf die Umwelt 3 2.1.2 Wichtigkeit der Erforschung von Meilerplätzen 4 2.1.3 Aussehen der Meilerüberreste 5 2.2 Einsatz von LiDAR-Daten für die Detektion von Meilerplätzen 6 2.2.1 Einführung in LiDAR 6 2.2.2 LiDAR in der archäologischen Praxis 8 2.2.3 Visualisierungstechniken von Höhenmodellen 10 2.2.4 Automatisierte Detektion von Meilerplätzen 15 2.3 Objekterkennung mit Deep Learning 16 2.3.1 Einführung in Deep Learning 16 2.3.2 Bildbasierte Objekterkennung von kleinen Objekten 17 2.3.3 Training eines Deep Learning-Modells 18 2.3.4 Datenaugmentierung 19 2.3.5 Hyperparameter 21 2.3.6 Bewertungsmetriken 21 2.3.7 Kategorisierung von Deep Learning-Modellen 23 2.3.8 Verwendete Modelle 25 3 Daten und Methoden 31 3.1 Datengrundlage und Computer-Hardware 31 3.2 Aufbereitung der Daten 32 3.2.1 Bearbeitung der Meilerdaten 32 3.2.2 Vorverarbeitung der DGM-Bilder 33 3.2.3 Aufteilung in Trainings-, Test- und Validierungsdatensatz 34 3.2.4 Datenaugmentierung des Trainingsdatensatzes 35 3.2.5 Verwendete DGM-Visualisierungstechniken 37 3.2.6 COCO-Format und Normalisierung 38 3.3 Experimentelles Vorgehen 39 3.3.1 Experiment 1: Verschiedene Eingangsdaten 39 3.3.2 Experiment 2: Verschiedene Bounding Box-Größen 40 3.3.3 Experiment 3: Verschiedene Hyperparameter 41 3.3.4 Experiment 4: Verschiedene Modelle 41 3.4 Verwendete Bewertungsmetriken 42 4 Ergebnisse 44 4.1 Experiment 1: Verschiedene Eingangsdaten 44 4.2 Experiment 2: Verschiedene Bounding Box-Größen 48 4.3 Experiment 3: Verschiedene Hyperparameter 52 4.4 Experiment 4: Verschiedene Modelle 56 4.5 Inferenz des besten Modells auf ein unbekanntes Gebiet 61 5 Diskussion 63 5.1 Interpretation der Ergebnisse 63 5.2 Vergleich der Ergebnisse mit anderen Studien 66 5.3 Bewertung der Modelleistung in einem gut und schlecht zu kartierendem Gebiet 68 6 Fazit und Ausblick 71 7 Literaturverzeichnis 73 Anhang 78 / The historical production of charcoal played a significant role in the industrial development. Traditionally, charcoal was produced in so-called kilns, upright ovens. This practice led to extensive deforestation and changed the vegetation composition. In order to better understand historical forest cover and historical land use practices, it is necessary to analyze the spatial distribution of the charcoal kilns. However, manual mapping of the kilns remains using DTM visualization techniques is very time-consuming and labour-intensive. Therefore, this study examines the use of deep learning for the automatic detection of charcoal kiln sites based on LiDAR data products. Pre-trained models from the MMDetection toolbox were trained with DTM images to develop a model specifically adapted to the charcoal kilns. A total of four experiments were conducted to analyze the impact of different DTM visualizations, bounding box sizes, and hyperparameters using the FoveaBox detector as well as the performance of different models (FoveaBox, ATSS, VFNet, RetinaNet). The results show that a 3-band image consisting of hill shading, Sky-View factor, and slope, and a bounding box size of 50 m, is ideal for the detection of kilns. The FoveaBox detector achieved the best performance with the RAdam optimizer and a learning rate of 0.0001, while the ATSS model performed the most consistent results with an accuracy of 93 % and missing only 7 % of the kilns. The ATSS model shows up to 10 % better performance compared to other studies. Key factors for these improvements were the used dataset of the 3-band images, the size of the bounding boxes, and the more extensive data augmentation, particularly the complementary use of radiometric techniques. Through the experimental approach, detection accuracy was improved by 13 %. Compared to manual mapping, the model could identify many additional kilns, although it sometimes led to confusion with accumulated soil on slopes and false detections in uneven terrain with small height differences. The suitability of the algorithm for improved detection of charcoal kiln sites instead of manual mapping is considered efficient but not necessarily more accurate.:Selbständigkeitserklärung II Weitergabe der Arbeit II Kurzfassung IV Abstract V Abbildungsverzeichnis VIII Tabellenverzeichnis X Abkürzungsverzeichnis XI 1 Einleitung 1 1.1 Problemstellung und Zielsetzung 1 1.2 Aufbau der Arbeit 2 2 Grundlagen 3 2.1 Historischer und archäologischer Kontext von Meilerplätzen 3 2.1.1 Holzkohleproduktion und ihre Auswirkungen auf die Umwelt 3 2.1.2 Wichtigkeit der Erforschung von Meilerplätzen 4 2.1.3 Aussehen der Meilerüberreste 5 2.2 Einsatz von LiDAR-Daten für die Detektion von Meilerplätzen 6 2.2.1 Einführung in LiDAR 6 2.2.2 LiDAR in der archäologischen Praxis 8 2.2.3 Visualisierungstechniken von Höhenmodellen 10 2.2.4 Automatisierte Detektion von Meilerplätzen 15 2.3 Objekterkennung mit Deep Learning 16 2.3.1 Einführung in Deep Learning 16 2.3.2 Bildbasierte Objekterkennung von kleinen Objekten 17 2.3.3 Training eines Deep Learning-Modells 18 2.3.4 Datenaugmentierung 19 2.3.5 Hyperparameter 21 2.3.6 Bewertungsmetriken 21 2.3.7 Kategorisierung von Deep Learning-Modellen 23 2.3.8 Verwendete Modelle 25 3 Daten und Methoden 31 3.1 Datengrundlage und Computer-Hardware 31 3.2 Aufbereitung der Daten 32 3.2.1 Bearbeitung der Meilerdaten 32 3.2.2 Vorverarbeitung der DGM-Bilder 33 3.2.3 Aufteilung in Trainings-, Test- und Validierungsdatensatz 34 3.2.4 Datenaugmentierung des Trainingsdatensatzes 35 3.2.5 Verwendete DGM-Visualisierungstechniken 37 3.2.6 COCO-Format und Normalisierung 38 3.3 Experimentelles Vorgehen 39 3.3.1 Experiment 1: Verschiedene Eingangsdaten 39 3.3.2 Experiment 2: Verschiedene Bounding Box-Größen 40 3.3.3 Experiment 3: Verschiedene Hyperparameter 41 3.3.4 Experiment 4: Verschiedene Modelle 41 3.4 Verwendete Bewertungsmetriken 42 4 Ergebnisse 44 4.1 Experiment 1: Verschiedene Eingangsdaten 44 4.2 Experiment 2: Verschiedene Bounding Box-Größen 48 4.3 Experiment 3: Verschiedene Hyperparameter 52 4.4 Experiment 4: Verschiedene Modelle 56 4.5 Inferenz des besten Modells auf ein unbekanntes Gebiet 61 5 Diskussion 63 5.1 Interpretation der Ergebnisse 63 5.2 Vergleich der Ergebnisse mit anderen Studien 66 5.3 Bewertung der Modelleistung in einem gut und schlecht zu kartierendem Gebiet 68 6 Fazit und Ausblick 71 7 Literaturverzeichnis 73 Anhang 78

Künstliche Intelligenz (KI) wird immer leistungsfähiger und KI-basierte Systeme werden zunehmend zur Automatisierung einer steigenden Anzahl von Arbeitstätigkeiten eingesetzt. Kurz- bis mittelfristig führt dies zu Veränderungen von Jobs. Langfristig könnte dies zu strukturellen Arbeitsmarktveränderungen führen, die gesellschaftliche Anpassungen einschließlich der Transformation des bestehenden Wohlfahrtssystems erfordern würden. Beides birgt Potenzial für tiefgreifende psychologische Implikationen für Beschäftigte. Psychologische Forschung, die sich mit den Auswirkungen KI-bedingter Automation befasst, ist jedoch rar. Ziel der Dissertation war es daher, zu einer psychologischen Perspektive auf das Thema beizutragen und die psychologischen Implikationen KI-bedingter Automation für Beschäftigte zu untersuchen. Die Dissertation umfasst drei Projekte. Der Fokus in Projekt 1 und 2 lag auf den kurz- bis mittelfristigen Auswirkungen KI-bedingter Automation. In Projekt 1 wurden fünf Interviewstudien mit Expert*innen und Beschäftigten aus dem Gesundheits- und Finanzdienstleistungssektor (N=91) durchgeführt, um Anwendungsfelder von KI-bedingter Automation, damit verbundene Chancen und Risiken sowie Auswirkungen auf die Tätigkeiten sowie das Erleben und Verhalten von Beschäftigten zu untersuchen. In Projekt 2 wurde ein Fragebogen entwickelt, um die Relevanz ausgewählter psychologischer Konstrukte im Kontext KI-bedingte Automation zu untersuchen. Die Fragebogenentwicklung war in drei Phasen unterteilt und beinhaltete zwei Vorstudien (N=1293). Der Fokus von Projekt 3 lag auf den langfristigen Auswirkungen KI-bedingter Automation und untersuchte das bedingungslose Grundeinkommen (BGE), eine mögliche und grundlegende Veränderung in der Organisation unseres Wohlfahrtssystems. Auf Basis einer repräsentativen Stichprobe der deutschen Erwerbsbevölkerung (N=1986) wurden sozio-demografische und psychologische Prädiktoren für die Akzeptanz eines BGE untersucht. / The capabilities of artificial intelligence (AI) are expanding rapidly, and AI-based systems are increasingly used to automate a growing number of job tasks. In the short- to medium-term, jobs are changing as a result. In the long term, this development may also lead to structural changes in the labor market, requiring societal adaptation including the transformation of the existing welfare system. Both carry great potential for far-reaching psychological implications for employees. However, psychological research dedicated to the impact of AI-driven automation is scarce. Therefore, the aim of this dissertation was to contribute to a psychological perspective on the topic and investigate the psychological implications of AI-driven automation for employees. The dissertation comprises three projects. Projects 1 and 2 focused on the short- to medium-term impact of AI-driven automation, exploring how jobs are changing and the related psychological implications for employees. In Project 1, five interview studies with experts and employees from the healthcare and financial services sectors (N=91) were conducted to explore application fields of AI-driven automation, associated opportunities and threats, its impact on employees’ job tasks, and employees’ experience and behavior in response to these changes. In Project 2, a questionnaire was developed to investigate the relevance of selected psychological concepts in the context of AI-driven automation. The questionnaire development was divided into three phases and included two preliminary studies (N=1,293). Project 3 focused on the long-term impact of AI-driven automation, investigating a universal basic income (UBI), one possible and fundamental shift in the organization of our welfare system. Using a representative sample of the German working population (N=1,986), socio-demographic and psychological predictors of UBI acceptance were investigated.

Introdução: O Câncer de Próstata (CaP) é o tumor mais comum do homem e a segunda causa de óbito por câncer no Brasil. MicroRNA (miRNA) é uma classe de pequenos RNA regulatórios não codificantes de proteínas que tem papel fundamental no controle da expressão dos genes. São responsáveis pelo controle de processos fundamentais na célula e estão envolvidos na tumorigênese em humanos. Previamente demonstramos alterações no perfil de expressão dos miRNA 100, let7c e 218 comparando carcinomas localizados e metastáticos. A caracterização de perfis de expressão de suas proteínas alvo no CaP é crucial para a compreensão dos processos envolvidos na carcinogênese, dando-nos a oportunidade do descobrimento de novos marcadores diagnósticos, prognósticos e mais importante identificação de alvos para o desenvolvimento de terapias inovadoras. Objetivo: Analisar a expressão das proteínas controladas pelo miR-let7c (Ras, c-Myc e Bub1), miR-100 (Smarca5 e Retinoblastoma) e miR-218 (Laminina 5 3) e a atividade proliferativa (Ki-67) no câncer de próstata com a técnica de imuno-histoquímica utilizando microarranjos teciduais representativos de CaP localizado e suas metástases linfonodais e ósseas. Correlacionar os níveis de expressão dos miRNA com suas proteínas alvo. Analisar a expressão dos miRNA, proteínas e atividade proliferativa com os fatores prognósticos do câncer de próstata e com a evolução da doença. Material e Métodos: A imunoexpressão de Smarca5, Retinoblastoma, Laminina, Ras, c- Myc, Bub1 e Ki-67 foi avaliada através de IH pela técnica de microarranjo tecidual caracterizando três estágios do CaP, sendo 112 casos de CaP localizado, 19 metástases linfonodais e 28 metástases ósseas. As imagens obtidas foram submetidas a um software de análise de imagem digital MacBiophotonics ImageJ do National Institutes of Health, EUA, onde a intensidade de luminescência foi quantificada densitometricamente. O perfil de expressão dos miR-let7c, 100 e 218 foi analisado utilizando o bloco de parafina de 61 pacientes dos 112 pacientes com carcinoma localizado, que foram submetidos a analise protéica por IH. O processamento dos miRNA envolveu três etapas: extração do miRNA com kit específico, geração do DNA complementar e amplificação do miRNA por PCR quantitativo em tempo real (qRT-PCR) cujo controle endógeno foi RNU-43 (Applied Biosystems). Os resultados foram analisados usando o método 2-CT. Como controle, utilizamos amostras de tecido com hiperplasia prostática benigna (HPB). Avaliamos a relação entre a expressão dos miRNA e suas proteínas alvo, com o escore de Gleason, estadiamento patológico e evolução da doença considerando recidiva bioquímica, níveis de PSA>0,4 ng/mL, em uma média de seguimento de 77,5 meses. A análise estatística foi realizada através do software SPSS 19.0, utilizamos o test T de Student, Mann-Whitney, Kruskal-Wallis e qui-quadrado. O valor de p foi considerado estatisticamente significante quando inferior na 0,05 em todos os cálculos. Resultados: Observamos uma diminuição de expressão de Ras (p=0,017) e Laminina (p<0,0001) conforme a progressão tumoral do CaP localizado a metástase linfonodal e óssea. Houve um aumento de expressão de Rb (p=0,0361) e aumento da atividade proliferativa avaliada pelo Ki- 67 (p<0,0001). Encontramos ainda uma tendência a relação entre a positividade de expressão de c-Myc com estadiamento patológico pT3 (p=0,070). Todos os miRNA se mostraram superexpressos no CaP localizado. Laminina apresentou uma média de intensidade de expressão maior quanto maior a expressão de miR-218 (p=0,038). Porém os demais miRNA não apresentaram relação de expressão com suas proteínas alvo. Também não houve relação entre a expressão de miRNA e expressão das proteínas por IH com a recidiva bioquímica. Conclusões: Apesar de confirmarmos os nossos achados de superexpressão dos miRNA 100, let7c e 218 no CaP localizado, não houve correlação entre esses e a imunoexpressão de suas proteínas alvo. Demonstramos que houve alteração de imunoexpressão de Ras, Laminina 5 3, Retinoblastoma e Ki-67 de acordo com a progressão tumoral no CaP. E uma maior expressão de c-Myc por IH mostrou uma significância tendência a relacionar-se com tumores não confinados estadiados pT3 / Introduction: Prostate cancer (PCa) is the most common tumor in men and the second leading cause of cancer death in men in Brazil. MicroRNA (miRNA) is a class of small non-coding RNA that plays a key role in the control of gene expression. They are responsible for the control of key processes in the cell and are involved in tumorigenesis in humans. Previously, we demonstrated alterations in the expression profile of miRNA 100, 218 and let7c comparing localized and metastatic carcinomas. The characterization of expression profiles of their target proteins in PCa is crucial to understanding the processes involved in carcinogenesis, giving us the opportunity to discover new diagnostic or prognostic markers, and most importantly to find new targets for the development of innovative therapies. Objective: To analyze the expression of proteins controlled by miR-let7c (Ras, c- Myc and Bub1), miR-100 (Smarca5 and Retinoblastoma) and miR- 218 (Laminin 5 3) and proliferative activity (Ki-67) in prostate cancer with immunohistochemistry using tissue microarrays representing localized PCa, lymph node and bone metastases. To correlate the expression levels of miRNAs with their target proteins. To analyze the expression of miRNAs, proteins and proliferative activity with prognostic factors of prostate cancer and disease progression. Methods: The immunoexpression of Smarca5, Retinoblastoma, Laminin, Ras, c-Myc, Bub1 and Ki-67 was evaluated by IHC by tissue microarray technique featuring three stages of PCa, with 112 cases of localized PCa, 19 lymph node metastases and 28 bone metastases. The images obtained from IHC were submitted to analysis using the digital image software MacBiophotonics ImageJ from the National Institutes of Health, USA, where the intensity of luminescence was quantified densitometrically. We studied the expression profile of the miRNAs in the paraffin blocks of 61 patients out of the 112 patients with localized carcinoma, who underwent protein analysis by IHC. The processing of miRNA involved three steps: extraction of miRNA, generation of complementary DNA and amplification of the miRNA by quantitative real time PCR (qRT-PCR). To analyze the data we used a control endogenous RNU-43. The results were analyzed using the 2-CT formula. As control, we used the tissue from five patients with benign prostate hyperplasia (BPH) submitted to surgery. The relationship between the expression of miRNAs and their target proteins were analyzed as well as their expression with Gleason score, pathological stage and disease progression considered as PSA>0.4 ng/mL in a mean follow-up of 77.5 months. The statistical analysis was performed using SPSS 19.0 software, we used the Student t test, Mann-Whitney test, Kruskal- Wallis and chi-square. The value was considered statistically significant when p0.05. Results: There was a decrease in the expression of Ras (p=0.017) and Laminin (p<0.0001) according to PCa progression from localized to lymph node and bone metastases. There was an increase in the expression of Retinoblastoma (p=0.0361) and an increase in proliferative activity assessed by Ki-67 (p<0.0001). We also found a relationship between the positivity of c-Myc expression with pT3 staged tumors (p=0.070). All miRNAs showed overexpression in PCa samples. Laminin showed a higher expression together with higher expression of miR-218 (p=0.038). The other miRNAs did not show a relationship with protein expression by IHC. There was no correlation between the expression of miRNAs and protein expression by IHC with biochemical recurrence. Conclusions: Although our findings confirm the overexpression of miR-100, 218 and let7c in localized PCa, there was no correlation between their expression and the protein of their target using immunohistochemistry. We demonstrated that there was a change in immunostatining of Ras, Laminin 5 3, Retinoblastoma and Ki- 67 according to tumor progression. The increased expression of c- Myc per IHC showed a significant tendency to relate to tumor unconfined staged pT3

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Roman&quot;,&quot;serif&quot;;mso-fareast-font-family:Calibri;mso-fareast-theme-font:minor-latin;color:black;mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language:AR-SA">Karcinom dojke je heterogena bolest koju karakteri&scaron;u različita morfologija, imunohisto-hemijski profil, klinički tok i terapijski odgovor. Ki-67 proliferativni indeks je jedan od markera sa prognostičkim i prediktivnim značajem, čije metodolo&scaron;ko određivanje i analiza jo&scaron; uvek nisu standardizovani. <b>Cilj: </b>Utvrditi graničnu (&ldquo;cut-off&rdquo;) prognostičku vrednost Ki-67 indeksa, kao i povezanost vrednosti Ki-67 u ranom luminalnom karcinomu dojke sa prognostičkim i prediktivnim parametrima karcinoma dojke, kao &scaron;to su životna dob bolesnica, veličina tumora, histolo&scaron;ki gradus (HG) i nivo tumorske ekspresije receptora estrogena (ER) i progesterona (PR). Takođe, cilj istraživanja je i utvrđivanje značajnosti razlike u vrednosti Ki-67 proliferativnog indeksa u odnosu na pojavu lokalnog recidiva, udaljenih metastaza i dužinu preživljavanja u toku petogodi&scaron;njeg perioda praćenja pacijentkinja. <b>Metode: </b>Retrospektivno je analizirano 120 patohistolo&scaron;kih izve&scaron;taja bolesnica kojima je u periodu od 01.01.2009. godine do 31.12.2011. godine na Institutu za onkologiju Vojvodine imunohistohemijskom analizom dokazan luminalni karcinom dojke (pozitivan ER i PR, negativan HER2), bez metastaza u aksilarnim limfnim čvorovima. <b>Rezultati: </b>Metodama deskriptivne statistike prosečna starost pacijentkinja je iznosila 57,42&plusmn;10,17 godina; prosečna veličina tumora 17,98&plusmn;6,97mm; recidiv je registrovan kod 8 (6,7%) pacijentkinja uz prosečan vremenski period do pojave recidiva od 49&plusmn;20,23 meseci. Vrednost &ldquo;cut off&rdquo; indeksa Ki-67 od prognostičkog značaja za vremenski period bez recidiva je iznosio 20,75%. Nije dokazana signifikantna veza između vrednosti Ki-67 i godina starosti pacijentkinja (p=0,401, odnosno p=0,293), kao i jačine ekspresije ER (p=1,00, p=0,957) i PR (p=0,273, p=0,189). Ustanovljena je signifikantna povezanost Ki-67 postoji sa veličinom (p=0,035, p=0,20) i HG tumora (p=0,041, p=0,20). Prosečan period praćenja bolesnica iznosio je 72,92&plusmn;8,38 meseci; nije registrovana pojava udaljenih metastaza, kao ni smrtni ishod. U odnosu na pojavu lokalnog recidiva, Kaplan-Majerovom analizom i Koksovom regresionom analizom proliferativni indeks Ki-67 se pokazao kao signifikantan prediktor za procenu ponovnog javljanja bolesti, lokalnog recidiva (Log rank (df = 1) = 2,73; p=0,045). Takođe je ustanovljeno da je statistički značajan prediktor za procenu recidiva bolesti i starosna dob bolesnica (Log rank (df = 1) = 6,885; p=0,009). Intenzitet pozitivnosti ER i PR, veličina tumora i histolo&scaron;ki gradus se nisu pokazali kao prediktori za pojavu recidiva luminalnih karcinoma dojke (p &gt; 0,05). <b>Zaključak: </b>Zbog heterogene prirode oboljenja, kori&scaron;ćenjem standardnih histopatolo&scaron;kih faktora i biomarkera te&scaron;ko je predvideti tok i ishod karcinoma dojke. Ki-67 je proliferativni marker, čija visoka vrednost korelira sa faktorima lo&scaron;e prognoze.</span></p> / <p><!--[if gte mso 9]><xml> <o:DocumentProperties> <o:Author>Tanja Lakic</o:Author> <o:Version>12.00</o:Version> </o:DocumentProperties></xml><![endif]--><!--[if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:TrackMoves/> <w:TrackFormatting/> <w:PunctuationKerning/> <w:ValidateAgainstSchemas/> <w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid> <w:IgnoreMixedContent>false</w:IgnoreMixedContent> <w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText> <w:DoNotPromoteQF/> <w:LidThemeOther>EN-US</w:LidThemeOther> <w:LidThemeAsian>X-NONE</w:LidThemeAsian> <w:LidThemeComplexScript>X-NONE</w:LidThemeComplexScript> <w:Compatibility> <w:BreakWrappedTables/> <w:SnapToGridInCell/> <w:WrapTextWithPunct/> <w:UseAsianBreakRules/> <w:DontGrowAutofit/> <w:SplitPgBreakAndParaMark/> <w:DontVertAlignCellWithSp/> <w:DontBreakConstrainedForcedTables/> 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Normal";mso-tstyle-rowband-size:0;mso-tstyle-colband-size:0;mso-style-noshow:yes;mso-style-priority:99;mso-style-qformat:yes;mso-style-parent:"";mso-padding-alt:0cm 5.4pt 0cm 5.4pt;mso-para-margin-top:0cm;mso-para-margin-right:0cm;mso-para-margin-bottom:10.0pt;mso-para-margin-left:0cm;line-height:115%;mso-pagination:widow-orphan;font-size:11.0pt;font-family:"Calibri","sans-serif";mso-ascii-font-family:Calibri;mso-ascii-theme-font:minor-latin;mso-hansi-font-family:Calibri;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi;}</style><![endif]--></p><p class="Default"><b><span style="font-size:11.0pt">Introduction: </span></b><span style="font-size:11.0pt">Breast cancer is a heterogeneous disease characterized by different morphology, immunohistochemical profile, clinical course and response to applied therapy. Ki-67 proliferative index is one of the prognostic and predictive factors, whose methodological determination and analysis are still unstandardized. <b>Objective: </b>Determination of cut-off value for Ki-67 index, its corelation in luminal breast carcinoma with patient&#39;s age, tumor size, histological grade (HG) and expression of estrogen (ER) and progesterone (PR). Also, the aim of the study was to determine the significance of the difference in the value of the Ki-67 proliferative index in relation to the occurrence of local relapse, distant metastases and survival rates during the five-year follow-up period of the patient. <b>Methods: </b>Retrospectively, we analysed 120 pathohistological reports of patients who were treated in the period from 01.01.2009 until 31.12.2011 at the Oncology Institute of Vojvodina, and to whom immunohistochemically was proven luminal breast cancer (positive ER and PR, negative HER2), without axillary lymph node metastases. </span><b><span style="font-size:11.0pt">Results: </span></b><span style="font-size:11.0pt">The average patient&rsquo;s age was 57.42&plusmn;10.17 years; average tumor size 17.98&plusmn;6.97mm; recurrence was registered in 8 (6.7%) patients with average recurrence time of 49&plusmn;20.23 months. &quot;Cut off&quot; Ki-67 value of prognostic significance for period without recurrence was 20.75%. Test didn&rsquo;t show significant relationship between Ki-67 and patient&rsquo;s age (p=0.401 and p=0.293), as well as the strength of expression ER (p=1.00, p=0.957) and PR (p=0.273, p=0.189). Significant correlation was present for Ki-67 with size (p=0.035, p=0.20) and tumor&rsquo;s HG (p=0.041, p=0.20). The average follow-up period for patients was 72.92&plusmn;8.38 months; there was no registered occurrence of distant metastases or fatal outcome. In relation to the occurrence of local relapse, Kaplan-Meier analysis and Cox regression analysis, the proliferative index Ki-67 proved to be a significant predictor for the assessment of recurrence of the disease, local relapse (Log rank (df = 1) = 2.73; p = 0.045). Also, it was founded that a statistically significant predictor for assessing the recurrence of the disease is the age of the patients (Log rank (df = 1) = 6.885; p = 0.009). The intensity of ER and PR expression, tumor size and histological grade have not been shown to be predictors of the recurrence of luminal breast carcinoma (p&gt; 0.05). </span><b><span style="font-size:11.0pt">Conclusion: </span></b><span style="font-size:11.0pt">Breast carcinoma is heterogeneous disease, so it is difficult to predict its course and outcome using standard histopathological factors and biomarkers. Ki-67 is proliferative marker whose high value correlates with factors of bad prognosis. </span></p>

O câncer de colo do útero é o terceiro tipo de câncer mais frequente em mulheres no mundo, e a infecção persistente pelo papilomavirus humano (HPV) oncogênico é condição necessária, mas não suficiente para seu desenvolvimento. As oncoproteínas virais E6 e E7 interferem direta ou indiretamente na ação de várias proteínas celulares. Entretanto, as variantes proteicas, resultantes de polimorfismos genéticos, podem apresentar comportamento distinto mediante a infecção pelo HPV. O objetivo deste estudo foi avaliar possíveis associações entre polimorfismos nos genes TP53 (p53 PIN3, p53 72C>G) e p21 (p21 31C>A) e o desenvolvimento de neoplasias cervicais, considerando os níveis de expressão das proteínas p53, p21, p16 e ciclina D1, e fatores de risco clássicos para o câncer cervical. Foram selecionadas 466 mulheres residentes no Rio de Janeiro, 281 com diagnóstico histopatológico de neoplasia cervical de baixo (LSIL) e alto grau (HSIL) e câncer (grupo de casos) e 185 sem história atual ou pregressa de alteração citológica do colo uterino (grupo controle). A técnica de PCR-RFLP (reação em cadeia da polimerase - polimorfismo de comprimento de fragmento de restrição), foi empregada na análise dos polimorfismos p53 72C>G e p21 31C>A, usando as enzimas de restrição BstUI e BsmaI, respectivamente. A avaliação do polimorfismo p53 PIN3 (duplicação de 16 pb) foi feita por meio da análise eletroforética direta dos produtos de PCR. A expressão das proteínas p53, p21, p16, ciclina D1 e Ki-67 e a pesquisa de anticorpos anti-HPV 16 e HPV pool foram avaliadas por imunohistoquímica (Tissue Microarray - TMA) em 196 biópsias do grupo de casos. O grupo controle se mostrou em equilíbrio de Hardy-Weinberg em relação aos três polimorfismos avaliados. As distribuições genotípicas e alélicas relativas a p53 PIN3 e p53 72C>G nos grupos controles e de casos não apresentaram diferenças significativas, embora o genótipo p53 72CC tenha aumentado o risco atribuído ao uso de contraceptivos das pacientes apresentarem lesões mais severas (OR=4,33; IC 95%=1,19-15,83). O genótipo p21 31CA(Ser/Arg) conferiu proteção ao desenvolvimento de HSIL ou câncer (OR=0,61, IC 95%=0,39-0,97), e modificou o efeito de fatores de risco associados à severidade das lesões. A interação multiplicativa de alelos mostrou que a combinação p53 PIN3A1, p53 72C(Pro) e p21 31C(Ser), representou risco (OR=1,67, IC95%=1,03-2,72) e a combinação p53 PIN3A1, p53 72C(Pro) e p21 31A(Arg) conferiu efeito protetor (OR=0,26, IC95%=0,08-0,78) para o desenvolvimento de HSIL e câncer cervical. Observou-se correlação positiva da expressão de p16 e p21 e negativa da ciclina D1 com o grau da lesão. A distribuição epitelial de p16, Ki-67, p21 e p53 se mostrou associada à severidade da lesão. Os polimorfismos analisados não apresentaram associação com a expressão dos biomarcadores ou positividade para HPV. Nossos resultados sugerem a importância do polimorfismo p21 31C>A para o desenvolvimento das neoplasias cervicais e ausência de correlação dos polimorfismos p53 PIN3 e p53 72C>G com a carcinogênese cervical, embora alguns genótipos tenham se comportado como modificadores de risco. Nossos resultados de TMA corroboram o potencial de uso de biomarcadores do ciclo celular para diferenciar as lesões precursoras do câncer cervical. / Cervical cancer is the third most common female cancer worldwide, and persistent infection by the Human Papillomavirus (HPV) is a necessary but not sufficient condition to cause it. The viral oncoproteins E6 and E7 interfere directly or indirectly with the action of various cellular proteins. However, the protein variants, resulting from genetic polymorphisms, may act differently when encountering HPV infection. The aim of this study was to evaluate possible associations between polymorphisms in the TP53 (p53 PIN3, p53 72C>G) and p21 (p21 31C>A) genes, and the development of cervical neoplasia, considering the expression levels of p53, p21, p16 and cyclin D1 proteins, together with classic risk factors for cervical cancer. A total of 466 women resident in Rio de Janeiro were selected, being 281 with histopathological diagnosis of low (LSIL) or high grade (HSIL) cervical neoplasia or cancer (test group), and 185 with no current or previous history of alteration of cervical cytology (control group). The PCR-RFLP technique (polymerase chain reaction restriction fragment length polymorphism) was used to analyze the p53 72C>G and p21 31C>A polymorphisms, using BstUI and BsmaI restriction enzymes, respectively. Genotyping of the p53 PIN3 (duplication of 16 pb) polymorphism was performed by direct electrophoretic analysis of the PCR products. The expression of p53, p21, p16, cyclin D1 and Ki-67 proteins and the study of anti-HPV 16 and anti-HPV pool positivities were evaluated by immunohistochemisty (Tissue Microarray - TMA) in 196 biopsies of cases. The control group obeyed the Hardy-Weinberg principle in relation to the three polymorphisms analysed. The genotypic and allelic frequencies regarding p53 PIN3 and p53 72C>G in the control and test groups were not significantly different, although the p53 72CC genotype has increased the risk of more severe lesions attributed to the use of contraceptives (OR=4.33; IC 95%=1.19-15.83). The p21 31CA(Ser/Arg) genotype showed to protect against the development of HSIL or cancer (OR=0,61, IC 95%=0,39-0,97), and modified the effect of risk factors associated to the lesion severity. The multiplicative interaction of alleles showed that the combination p53 PIN3A1, p53 72C(Pro) and p21 31C(Ser) represented risk (OR=1,67, IC95%=1,03-2,72) and the combination p53 PIN3A1, p53 72C(Pro) and p21 31A(Arg) conferred protection (OR=0,26, IC95%=0,08-0,78) against the development of HSIL and cervical cancer. It was observed positive and negative correlations of, respectively, p16 and p21, and cyclin D1 expression with the cervical lesion grade. The epithelial distribution of p16, Ki-67, p21 and p53 was associated with the lesion severity. The polymorphisms analyzed showed neither association with the expression of the biomarkers nor positivity for HPV. Our results suggest the importance of polymorphism p21 31C>A in the development of cervical neoplasia and the lack of correlation between the polymorphisms p53 PIN3 and p53 72C>G with cervical carcinogenesis, although some genotypes acted as risk modifiers. Our TMA results corroborated the potential use of cell cycle biomarkers as an adjunctive tool to differentiate cervical precursor lesions.

O câncer de colo do útero é o terceiro tipo de câncer mais frequente em mulheres no mundo, e a infecção persistente pelo papilomavirus humano (HPV) oncogênico é condição necessária, mas não suficiente para seu desenvolvimento. As oncoproteínas virais E6 e E7 interferem direta ou indiretamente na ação de várias proteínas celulares. Entretanto, as variantes proteicas, resultantes de polimorfismos genéticos, podem apresentar comportamento distinto mediante a infecção pelo HPV. O objetivo deste estudo foi avaliar possíveis associações entre polimorfismos nos genes TP53 (p53 PIN3, p53 72C>G) e p21 (p21 31C>A) e o desenvolvimento de neoplasias cervicais, considerando os níveis de expressão das proteínas p53, p21, p16 e ciclina D1, e fatores de risco clássicos para o câncer cervical. Foram selecionadas 466 mulheres residentes no Rio de Janeiro, 281 com diagnóstico histopatológico de neoplasia cervical de baixo (LSIL) e alto grau (HSIL) e câncer (grupo de casos) e 185 sem história atual ou pregressa de alteração citológica do colo uterino (grupo controle). A técnica de PCR-RFLP (reação em cadeia da polimerase - polimorfismo de comprimento de fragmento de restrição), foi empregada na análise dos polimorfismos p53 72C>G e p21 31C>A, usando as enzimas de restrição BstUI e BsmaI, respectivamente. A avaliação do polimorfismo p53 PIN3 (duplicação de 16 pb) foi feita por meio da análise eletroforética direta dos produtos de PCR. A expressão das proteínas p53, p21, p16, ciclina D1 e Ki-67 e a pesquisa de anticorpos anti-HPV 16 e HPV pool foram avaliadas por imunohistoquímica (Tissue Microarray - TMA) em 196 biópsias do grupo de casos. O grupo controle se mostrou em equilíbrio de Hardy-Weinberg em relação aos três polimorfismos avaliados. As distribuições genotípicas e alélicas relativas a p53 PIN3 e p53 72C>G nos grupos controles e de casos não apresentaram diferenças significativas, embora o genótipo p53 72CC tenha aumentado o risco atribuído ao uso de contraceptivos das pacientes apresentarem lesões mais severas (OR=4,33; IC 95%=1,19-15,83). O genótipo p21 31CA(Ser/Arg) conferiu proteção ao desenvolvimento de HSIL ou câncer (OR=0,61, IC 95%=0,39-0,97), e modificou o efeito de fatores de risco associados à severidade das lesões. A interação multiplicativa de alelos mostrou que a combinação p53 PIN3A1, p53 72C(Pro) e p21 31C(Ser), representou risco (OR=1,67, IC95%=1,03-2,72) e a combinação p53 PIN3A1, p53 72C(Pro) e p21 31A(Arg) conferiu efeito protetor (OR=0,26, IC95%=0,08-0,78) para o desenvolvimento de HSIL e câncer cervical. Observou-se correlação positiva da expressão de p16 e p21 e negativa da ciclina D1 com o grau da lesão. A distribuição epitelial de p16, Ki-67, p21 e p53 se mostrou associada à severidade da lesão. Os polimorfismos analisados não apresentaram associação com a expressão dos biomarcadores ou positividade para HPV. Nossos resultados sugerem a importância do polimorfismo p21 31C>A para o desenvolvimento das neoplasias cervicais e ausência de correlação dos polimorfismos p53 PIN3 e p53 72C>G com a carcinogênese cervical, embora alguns genótipos tenham se comportado como modificadores de risco. Nossos resultados de TMA corroboram o potencial de uso de biomarcadores do ciclo celular para diferenciar as lesões precursoras do câncer cervical. / Cervical cancer is the third most common female cancer worldwide, and persistent infection by the Human Papillomavirus (HPV) is a necessary but not sufficient condition to cause it. The viral oncoproteins E6 and E7 interfere directly or indirectly with the action of various cellular proteins. However, the protein variants, resulting from genetic polymorphisms, may act differently when encountering HPV infection. The aim of this study was to evaluate possible associations between polymorphisms in the TP53 (p53 PIN3, p53 72C>G) and p21 (p21 31C>A) genes, and the development of cervical neoplasia, considering the expression levels of p53, p21, p16 and cyclin D1 proteins, together with classic risk factors for cervical cancer. A total of 466 women resident in Rio de Janeiro were selected, being 281 with histopathological diagnosis of low (LSIL) or high grade (HSIL) cervical neoplasia or cancer (test group), and 185 with no current or previous history of alteration of cervical cytology (control group). The PCR-RFLP technique (polymerase chain reaction restriction fragment length polymorphism) was used to analyze the p53 72C>G and p21 31C>A polymorphisms, using BstUI and BsmaI restriction enzymes, respectively. Genotyping of the p53 PIN3 (duplication of 16 pb) polymorphism was performed by direct electrophoretic analysis of the PCR products. The expression of p53, p21, p16, cyclin D1 and Ki-67 proteins and the study of anti-HPV 16 and anti-HPV pool positivities were evaluated by immunohistochemisty (Tissue Microarray - TMA) in 196 biopsies of cases. The control group obeyed the Hardy-Weinberg principle in relation to the three polymorphisms analysed. The genotypic and allelic frequencies regarding p53 PIN3 and p53 72C>G in the control and test groups were not significantly different, although the p53 72CC genotype has increased the risk of more severe lesions attributed to the use of contraceptives (OR=4.33; IC 95%=1.19-15.83). The p21 31CA(Ser/Arg) genotype showed to protect against the development of HSIL or cancer (OR=0,61, IC 95%=0,39-0,97), and modified the effect of risk factors associated to the lesion severity. The multiplicative interaction of alleles showed that the combination p53 PIN3A1, p53 72C(Pro) and p21 31C(Ser) represented risk (OR=1,67, IC95%=1,03-2,72) and the combination p53 PIN3A1, p53 72C(Pro) and p21 31A(Arg) conferred protection (OR=0,26, IC95%=0,08-0,78) against the development of HSIL and cervical cancer. It was observed positive and negative correlations of, respectively, p16 and p21, and cyclin D1 expression with the cervical lesion grade. The epithelial distribution of p16, Ki-67, p21 and p53 was associated with the lesion severity. The polymorphisms analyzed showed neither association with the expression of the biomarkers nor positivity for HPV. Our results suggest the importance of polymorphism p21 31C>A in the development of cervical neoplasia and the lack of correlation between the polymorphisms p53 PIN3 and p53 72C>G with cervical carcinogenesis, although some genotypes acted as risk modifiers. Our TMA results corroborated the potential use of cell cycle biomarkers as an adjunctive tool to differentiate cervical precursor lesions.

Made available in DSpace on 2014-12-17T15:32:18Z (GMT). No. of bitstreams: 1 ThaisGB.pdf: 4320063 bytes, checksum: c97c01facb2aed61dd49fc01568c6ca9 (MD5) Previous issue date: 2010-02-26 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The squamous cell carcinoma (SCC) is the most common malignant neoplasm of epithelial origin in oral cavity and present high capacity to invade adjacent structures. Traditionally, SCC has a predominance of 50 years male patients with long-time use of tobacco and alcohol, and the tongue is the most affected anatomic site. At present, there is an increasing incidence of SCC in patients below 40 years of age, who has been exposed or not to risk factors, mainly for tongue lesions. This study aims to analyze cell proliferation index using Ki-67 antigen in SCC of the tongue for two groups of different age range: until 40 years and older than 50 years. The first group was composed by 16 patients and the second one was composed by 20 patients. Clinicopathological features of the cases were also assessed. There was a male predominance in both groups. Tobacco and alcohol habits were common for patients until 40 years (72,2%), as well as for patients older than 50 years (52,9%). The first group had statistical association with the presence of regional metastases (p = 0,036) and with the most advanced stages of the disease (p = 0,012). Considering the histological malignancy grading, there was higher incidence (56,2%) of high malignancy grade tumors in the group of patients until 40 years old, but no statistical difference has found between groups and histologic malignancy grading. Regarding the immunohistochemical expression of Ki-67, there was no statistically significant difference between the antibody expression of the groups, as well as between other clinical and histopathological parameters. This study identified no significant difference regarding cell proliferation between the analyzed groups / O Carcinoma Epiderm?ide (CE) ? a neoplasia maligna de origem epitelial que mais ocorre na cavidade oral, com alta capacidade de invadir estruturas adjacentes. Classicamente, o CEO ocorre mais em homens com idade acima de 50 anos, que fazem uso de tabaco e ?lcool por longos per?odos de tempo, sendo a l?ngua, o s?tio anat?mico mais frequentemente acometido. Atualmente, vem se percebendo um aumento na incid?ncia dessa les?o em pacientes com idade abaixo dos 40 anos expostos ou n?o a fatores de risco, principalmente as les?es de l?ngua. O objetivo desta pesquisa foi analisar o ?ndice de prolifera??o celular, utilizando o anticorpo Ki-67em CEs de l?ngua em dois grupos de faixas et?rias distintas. Tamb?m, avaliaram-se as caracter?sticas cl?nico-patol?gicas dos casos constantes do estudo. A amostra se constituiu de 16 casos de pacientes com idade at? 40 anos e 20 casos de pacientes com idade acima de 50 anos. Em rela??o ?s caracter?sticas cl?nicopatol?gicas das les?es, o sexo masculino foi o mais acometido para os dois grupos, sendo evidenciado que o h?bito de beber e fumar foi frequente tanto para os pacientes com idade at? 40 anos (72,7%) como para os pacientes com idade acima dos 50 anos (52,9%). Foi poss?vel observar que houve uma associa??o estatisticamente significativa entre o grupo de pacientes com idade at? 40 anos e a presen?a de met?stase regional (p = 0,036), bem como entre o mesmo grupo e os est?gios mais avan?ados da doen?a (p = 0,012). Em rela??o ? grada??o histol?gica de malignidade, houve uma maior frequ?ncia de tumores classificados em alto grau de malignidade no grupo de pacientes com at? 40 anos (56,2%), mas n?o foi evidenciada diferen?a estat?stica entre os grupos e a grada??o histol?gica de malignidade. Quanto ? an?lise da express?o imuno-histoqu?mica pelo Ki-67, n?o houve diferen?a estatisticamente significativa entre a express?o do anticorpo para os grupos et?rios estudados nesta pesquisa, assim como n?o houve associa??o do ?ndice de positividade para o Ki-67 com os par?metros cl?nicos e histomorfol?gicos. Pode-se concluir que a prolifera??o celular n?o foi significativamente diferente entre os grupos que constitu?ram o presente estudo

Introdução: O Câncer de Próstata (CaP) é o tumor mais comum do homem e a segunda causa de óbito por câncer no Brasil. MicroRNA (miRNA) é uma classe de pequenos RNA regulatórios não codificantes de proteínas que tem papel fundamental no controle da expressão dos genes. São responsáveis pelo controle de processos fundamentais na célula e estão envolvidos na tumorigênese em humanos. Previamente demonstramos alterações no perfil de expressão dos miRNA 100, let7c e 218 comparando carcinomas localizados e metastáticos. A caracterização de perfis de expressão de suas proteínas alvo no CaP é crucial para a compreensão dos processos envolvidos na carcinogênese, dando-nos a oportunidade do descobrimento de novos marcadores diagnósticos, prognósticos e mais importante identificação de alvos para o desenvolvimento de terapias inovadoras. Objetivo: Analisar a expressão das proteínas controladas pelo miR-let7c (Ras, c-Myc e Bub1), miR-100 (Smarca5 e Retinoblastoma) e miR-218 (Laminina 5 3) e a atividade proliferativa (Ki-67) no câncer de próstata com a técnica de imuno-histoquímica utilizando microarranjos teciduais representativos de CaP localizado e suas metástases linfonodais e ósseas. Correlacionar os níveis de expressão dos miRNA com suas proteínas alvo. Analisar a expressão dos miRNA, proteínas e atividade proliferativa com os fatores prognósticos do câncer de próstata e com a evolução da doença. Material e Métodos: A imunoexpressão de Smarca5, Retinoblastoma, Laminina, Ras, c- Myc, Bub1 e Ki-67 foi avaliada através de IH pela técnica de microarranjo tecidual caracterizando três estágios do CaP, sendo 112 casos de CaP localizado, 19 metástases linfonodais e 28 metástases ósseas. As imagens obtidas foram submetidas a um software de análise de imagem digital MacBiophotonics ImageJ do National Institutes of Health, EUA, onde a intensidade de luminescência foi quantificada densitometricamente. O perfil de expressão dos miR-let7c, 100 e 218 foi analisado utilizando o bloco de parafina de 61 pacientes dos 112 pacientes com carcinoma localizado, que foram submetidos a analise protéica por IH. O processamento dos miRNA envolveu três etapas: extração do miRNA com kit específico, geração do DNA complementar e amplificação do miRNA por PCR quantitativo em tempo real (qRT-PCR) cujo controle endógeno foi RNU-43 (Applied Biosystems). Os resultados foram analisados usando o método 2-CT. Como controle, utilizamos amostras de tecido com hiperplasia prostática benigna (HPB). Avaliamos a relação entre a expressão dos miRNA e suas proteínas alvo, com o escore de Gleason, estadiamento patológico e evolução da doença considerando recidiva bioquímica, níveis de PSA>0,4 ng/mL, em uma média de seguimento de 77,5 meses. A análise estatística foi realizada através do software SPSS 19.0, utilizamos o test T de Student, Mann-Whitney, Kruskal-Wallis e qui-quadrado. O valor de p foi considerado estatisticamente significante quando inferior na 0,05 em todos os cálculos. Resultados: Observamos uma diminuição de expressão de Ras (p=0,017) e Laminina (p<0,0001) conforme a progressão tumoral do CaP localizado a metástase linfonodal e óssea. Houve um aumento de expressão de Rb (p=0,0361) e aumento da atividade proliferativa avaliada pelo Ki- 67 (p<0,0001). Encontramos ainda uma tendência a relação entre a positividade de expressão de c-Myc com estadiamento patológico pT3 (p=0,070). Todos os miRNA se mostraram superexpressos no CaP localizado. Laminina apresentou uma média de intensidade de expressão maior quanto maior a expressão de miR-218 (p=0,038). Porém os demais miRNA não apresentaram relação de expressão com suas proteínas alvo. Também não houve relação entre a expressão de miRNA e expressão das proteínas por IH com a recidiva bioquímica. Conclusões: Apesar de confirmarmos os nossos achados de superexpressão dos miRNA 100, let7c e 218 no CaP localizado, não houve correlação entre esses e a imunoexpressão de suas proteínas alvo. Demonstramos que houve alteração de imunoexpressão de Ras, Laminina 5 3, Retinoblastoma e Ki-67 de acordo com a progressão tumoral no CaP. E uma maior expressão de c-Myc por IH mostrou uma significância tendência a relacionar-se com tumores não confinados estadiados pT3 / Introduction: Prostate cancer (PCa) is the most common tumor in men and the second leading cause of cancer death in men in Brazil. MicroRNA (miRNA) is a class of small non-coding RNA that plays a key role in the control of gene expression. They are responsible for the control of key processes in the cell and are involved in tumorigenesis in humans. Previously, we demonstrated alterations in the expression profile of miRNA 100, 218 and let7c comparing localized and metastatic carcinomas. The characterization of expression profiles of their target proteins in PCa is crucial to understanding the processes involved in carcinogenesis, giving us the opportunity to discover new diagnostic or prognostic markers, and most importantly to find new targets for the development of innovative therapies. Objective: To analyze the expression of proteins controlled by miR-let7c (Ras, c- Myc and Bub1), miR-100 (Smarca5 and Retinoblastoma) and miR- 218 (Laminin 5 3) and proliferative activity (Ki-67) in prostate cancer with immunohistochemistry using tissue microarrays representing localized PCa, lymph node and bone metastases. To correlate the expression levels of miRNAs with their target proteins. To analyze the expression of miRNAs, proteins and proliferative activity with prognostic factors of prostate cancer and disease progression. Methods: The immunoexpression of Smarca5, Retinoblastoma, Laminin, Ras, c-Myc, Bub1 and Ki-67 was evaluated by IHC by tissue microarray technique featuring three stages of PCa, with 112 cases of localized PCa, 19 lymph node metastases and 28 bone metastases. The images obtained from IHC were submitted to analysis using the digital image software MacBiophotonics ImageJ from the National Institutes of Health, USA, where the intensity of luminescence was quantified densitometrically. We studied the expression profile of the miRNAs in the paraffin blocks of 61 patients out of the 112 patients with localized carcinoma, who underwent protein analysis by IHC. The processing of miRNA involved three steps: extraction of miRNA, generation of complementary DNA and amplification of the miRNA by quantitative real time PCR (qRT-PCR). To analyze the data we used a control endogenous RNU-43. The results were analyzed using the 2-CT formula. As control, we used the tissue from five patients with benign prostate hyperplasia (BPH) submitted to surgery. The relationship between the expression of miRNAs and their target proteins were analyzed as well as their expression with Gleason score, pathological stage and disease progression considered as PSA>0.4 ng/mL in a mean follow-up of 77.5 months. The statistical analysis was performed using SPSS 19.0 software, we used the Student t test, Mann-Whitney test, Kruskal- Wallis and chi-square. The value was considered statistically significant when p0.05. Results: There was a decrease in the expression of Ras (p=0.017) and Laminin (p<0.0001) according to PCa progression from localized to lymph node and bone metastases. There was an increase in the expression of Retinoblastoma (p=0.0361) and an increase in proliferative activity assessed by Ki-67 (p<0.0001). We also found a relationship between the positivity of c-Myc expression with pT3 staged tumors (p=0.070). All miRNAs showed overexpression in PCa samples. Laminin showed a higher expression together with higher expression of miR-218 (p=0.038). The other miRNAs did not show a relationship with protein expression by IHC. There was no correlation between the expression of miRNAs and protein expression by IHC with biochemical recurrence. Conclusions: Although our findings confirm the overexpression of miR-100, 218 and let7c in localized PCa, there was no correlation between their expression and the protein of their target using immunohistochemistry. We demonstrated that there was a change in immunostatining of Ras, Laminin 5 3, Retinoblastoma and Ki- 67 according to tumor progression. The increased expression of c- Myc per IHC showed a significant tendency to relate to tumor unconfined staged pT3