Pharmacokinetic Analysis of the Positron Emission Tomography Radiotracer [18F]ML-10 in Glioblastoma Multiforme Brain Tumors.

Conventional approaches to radiologic response assessment are inadequate for early therapy response assessment (ETRA) of glioblastoma multiforme (GBM), as they rely on slowly changing measures of therapeutic effect, e.g., changes in tumor size or in contrast enhancement on magnetic resonance imaging (MRI). Positron emission tomography (PET) imaging of GBM glucose metabolism using 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) has exhibited some success in diagnostic imaging of GBM; however, false-positive uptake caused by inflammation at the tumor site as well as high background uptake in uninvolved brain tissue limits its use for ETRA. 2-(5-[18F]fluoro-pentyl)-2-methylmalonic acid ([18F]ML-10) has recently been proposed as a PET radiotracer for imaging apoptosis. Molecular imaging of apoptosis is an attractive approach to ETRA of GBM, as it would provide a direct measure of therapeutic effect and a framework within which to interpret and compare efficacies of competing therapies. Previous studies have demonstrated the safety and bioavailability of [18F]ML-10 in healthy humans. However, the pharmacokinetic (PK) properties of [18F]ML-10 have not been evaluated. This dissertation examines the PK properties of [18F]ML-10 in a cohort of human subjects receiving treatment for GBM. [18F]ML-10 time-course in GBM was studied on a whole tumor and voxelwise level. [18F]ML-10 uptake was modeled as the response of a linear system using the radiotracer concentration in blood (measured from the PET image) as the input function (IF). In the whole tumor analysis, candidate models for the IF and GBM impulse response function (IRF) were fit simultaneously using a maximum likelihood approach. The relative merits of the joint IF/IRF models were compared using the Akaike information criterion and model parameter estimability considerations. Spectral analysis was performed to support model selection and provide alternative estimates of standard PK uptake measures. The selected IF/IRF models were further interpreted through tissue compartmental modeling. Standard PK measures of radiotracer uptake derived from models were evaluated for their utility in ETRA for a limited number of subjects. Finally, GBM heterogeneity in response to therapy was evaluated through a voxelwise analysis of [18F]ML-10 uptake. Comparison was made between [18F]ML-10 uptake at an ETRA time-point and later change in tumor cellularity measured using diffusion weighted MRI

Skeletal and dental changes following functional regulator therapy on class II patients

Craniofacial growth in 100 patients treated with the functional regulator of Frankel for about 24 months was compared with craniofacial growth seen in a matched group of untreated persons with Class II malocclusion. Both conventional and geometric cephalometric analyses were used to evaluate the skeletal and dental adaptations. This study shows several clear effects of treatment in either of two age ranges considered. The principal skeletal effect was advancement of the mandible along the direction of the facial axis. This advancement resulted in increases in mandibular length and in vertical facial dimensions. There was little effect of treatment upon maxillary skeletal structures with the exception of point A, which moved slightly posteriorly. Dentoalveolar adaptations due to treatment included a decrease in the normal forward movement of the upper molar and an increase in the normal vertical movement of the lower molar. There was a 2-mm posterior movement of the tip of the upper incisor but minimal anterior tipping of the lower incisor.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25601/1/0000148.pd

Evaluación de la calidad de datos en un sistema de Data Warehousing : un enfoque basado en contextos

Los Sistemas de Data Warehousing son de gran relevancia para el apoyo en la toma de decisiones y el análisis de los datos. Esto ha quedado demostrado a lo largo del tiempo, a través de la generalización de su desarrollo y uso a nivel industrial en todo tipo de organizaciones y mediante la gran cantidad de trabajos científicos que se han centrado en el estudio de este tipo de sistemas. Muchos investigadores han presentado la necesidad de incorporar y mantener la calidad de los datos en los Sistemas de Data Warehousing. Sin embargo, en las investigaciones no se encuentra un consenso acerca de como hacerlo, ni acerca de si es posible definir un único conjunto de dimensiones de calidad en el entorno de un Data Warehouse, dado que dicho conjunto puede depender del propósito con el cual se utilizan los datos. Por otro lado, una vez que los datos están en el Data Warehouse surge otro desafío, como serán utilizados los mismos. Los requerimientos de calidad pueden variar entre los diferentes dominios y entre los diferentes usuarios, no solo por el propósito de la tarea que necesiten realizar, sino también porque la calidad percibida por un usuario puede diferir respecto a la calidad percibida por otro usuario. Dado que, los datos vienen de diversas fuentes con niveles de calidad distintos, los dominios de análisis pueden ser variados y los usuarios pueden percibir la calidad de distintas formas, dependiendo esto de múltiples factores (su perfil, la tarea que va a realizar, etc.). Para la evaluación de la Calidad de Datos en los Sistemas de Data Warehousing, se considera un enfoque basado en el Contexto de los datos. En este trabajo se ejecuta una metodología de búsqueda bibliográfica para obtener una visión general de la investigación existente acerca del uso de contextos en los Sistemas de Data Warehousing y/o en la evaluación de Calidad de Datos. A partir de los resultados obtenidos con la aplicación de dicha metodología, se obtiene una visión general del estado del arte, lo que permite realizar el primer planteo de una propuesta para evaluar la Calidad de Datos en los Sistemas de Data Warehousing, con un enfoque basado en Contextos. Este primer planteo, es el punto de partida de una investigación mas amplia y profunda que permita la gestión de la calidad en este tipo de Sistemas.Data Warehousing Systems are of great relevance for supporting decision making and data analysis. This has been proven over time, through the generalization of its development and use at industrial level in all kind of organizations. Moreover, the large number of scientic studies that have focused on the study of such systems have also proven the importance of them. Many researchers have presented the need to incorporate and maintain data quality in Data Warehousing Systems. However, there is no consensus in the research community on how or whether it is possible to define a single set of quality dimensions for Data Warehouse systems, due to the fact that this set of dimensions may depend on the purpose for which the data are used. On the other hand, once the data are in the Data Warehouse another challenge arises, how they will be used. Quality requirements may vary among different domains and among different users, not only due to the task they need to perform, but also because the quality perceived by a user may differ from the quality perceived by another user. Since data come from different sources with different levels of quality, analysis domains can vary and users can perceive the quality in different ways, depending on many factors (their profile, the task to be performed, etc.), for the evaluation of Data Quality in Data Warehousing Systems it is considered a data-context based approach. In this thesis a systematic literature review is executed to obtain an overview of existing research on the use of contexts in Data Warehousing Systems and/or on the evaluation of Data Quality in this kind of systems. From the results obtained with the application of this methodology, an overview of the state-of-the-art is performed, which allows to do the first proposal to assess data quality in Data Warehousing Systems with an approach based on Contexts. This first proposal is the starting point of a broader and deeper investigation that will allow quality management in Data Warehousing Systems