Molecular Characteristics of Neuroblastoma with Special Reference to Novel Prognostic Factors and Diagnostic Applications

Molecular Characteristics of Neuroblastoma with Special Reference to Novel Prognostic Factors and Diagnostic Applications Department of Medical Biochemistry and Genetics Annales Universitatis Turkuensis, Medica-Odontologica, 2009, Turku, Finland Painosalama Oy, Turku, Finland 2009 Background: Neuroblastoma, which is the most common and extensively studied childhood solid cancer, shows a great clinical and biological heterogeneity. Most of the neuroblastoma patients older than one year have poor prognosis despite intensive therapies. The hallmark of neuroblastoma, biological heterogeneity, has hindered the discovery of prognostic tumour markers. At present, few molecular markers, such as MYCN oncogene status, have been adopted into clinical practice. Aims: The aim of the study was to improve the current prognostic methodology of neuroblastoma, especially by taking cognizance of the biological heterogeneity of neuroblastoma. Furthermore, unravelling novel molecular characteristics which associate with neuroblastoma tumour progression and cell differentiation was an additional objective. Results: A new strictly defined selection of neuroblastoma tumour spots of highest proliferation activity, hotspots, appeared to be representative and reliable in an analysis of MYCN amplification status using a chromogenic in situ hybridization technique (CISH). Based on the hotspot tumour tissue microarray immunohistochemistry and high-resolution oligo-array-based comparative genomic hybridization, which was integrated with gene expression and in silico analysis of existing transcriptomics, a polysialylated neural cell adhesion molecule (NCAM) and poorly characterized amplicon at 12q24.31 were discovered to associate with outcome. In addition, we found that a previously considered new neuroblastoma treatment target, the mutated c-kit receptor, was not mutated in neuroblastoma samples. Conclusions: Our studies indicate polysialylated NCAM and 12q24.31 amplicon to be new molecular markers with important value in prognostic evaluation of neuroblastoma. Moreover, the presented hotspot tumour tissue microarray method together with the CISH technique of the MYCN oncogene copy number is directly applicable to clinical use. Key words: neuroblastoma, polysialic acid, neural cell adhesion molecule, MYCN, c-kit, chromogenic in situ hybridization, hotspot

Understanding the in vitro dissolution rate of glasses with respect to future clinical applications

Glass is a unique material with a long history. Several glass products are used daily in our everyday life, often unnoticed. Glass can be found not only in obvious applications such as tableware, windows, and light bulbs, but also in tennis rackets, windmill turbine blades, optical devices, and medical implants. The glasses used at present as implants are inorganic silica-based melt-derived compositions mainly for hard-tissue repair as bone graft substitute in dentistry and orthopedics. The degree of glass reactivity desired varies according to implantation situation and it is vital that the ion release from any glasses used in medical applications is controlled. Understanding the in vitro dissolution rate of glasses provides a first approximation of their behavior in vivo. Specific studies concerning dissolution properties of bioactive glasses have been relatively scarce and mostly concentrated to static condition studies. The motivation behind this work was to develop a simple and accurate method for quantifying the in vitro dissolution rate of highly different types of glass compositions with interest for future clinical applications. By combining information from various experimental conditions, a better knowledge of glass dissolution and the suitability of different glasses for different medical applications can be obtained. Thus, two traditional and one novel approach were utilized in this thesis to study glass dissolution. The chemical durability of silicate glasses was tested in water and TRIS-buffered solution at static and dynamic conditions. The traditional in vitro testing with a TRISbuffered solution under static conditions works well with bioactive or with readily dissolving glasses, and it is easy to follow the ion dissolution reactions. However, in the buffered solution no marked differences between the more durable glasses were observed. The hydrolytic resistance of the glasses was studied using the standard procedure ISO 719. The relative scale given by the standard failed to provide any relevant information when bioactive glasses were studied. However, the clear differences in the hydrolytic resistance values imply that the method could be used as a rapid test to get an overall idea of the biodegradability of glasses. The standard method combined with the ion concentration and pH measurements gives a better estimate of the hydrolytic resistance because of the high silicon amount released from a glass. A sensitive on-line analysis method utilizing inductively coupled plasma optical emission spectrometer and a flow-through micro-volume pH electrode was developed to study the initial dissolution of biocompatible glasses. This approach was found suitable for compositions within a large range of chemical durability. With this approach, the initial dissolution of all ions could be measured simultaneously and quantitatively, which gave a good overall idea of the initial dissolution rates for the individual ions and the dissolution mechanism. These types of results with glass dissolution were presented for the first time during the course of writing this thesis. Based on the initial dissolution patterns obtained with the novel approach using TRIS, the experimental glasses could be divided into four distinct categories. The initial dissolution patterns of glasses correlated well with the anticipated bioactivity. Moreover, the normalized surface-specific mass loss rates and the different in vivo models and the actual in vivo data correlated well. The results suggest that this type of approach can be used for prescreening the suitability of novel glass compositions for future clinical applications. Furthermore, the results shed light on the possible bioactivity of glasses. An additional goal in this thesis was to gain insight into the phase changes occurring during various heat treatments of glasses with three selected compositions. Engineering-type T-T-T curves for glasses 1-98 and 13-93 were stablished. The information gained is essential in manufacturing amorphous porous implants or for drawing of continuous fibers of the glasses. Although both glasses can be hot worked to amorphous products at carefully controlled conditions, 1-98 showed one magnitude greater nucleation and crystal growth rate than 13-93. Thus, 13-93 is better suited than 1-98 for working processes which require long residence times at high temperatures. It was also shown that amorphous and partially crystalline porous implants can be sintered from bioactive glass S53P4. Surface crystallization of S53P4, forming Na2O∙CaO∙2SiO2, was observed to start at 650°C. The secondary crystals of Na2Ca4(PO4)2SiO4, reported for the first time in this thesis, were detected at higher temperatures, from 850°C to 1000°C. The crystal phases formed affected the dissolution behavior of the implants in simulated body fluid. This study opens up new possibilities for using S53P4 to manufacture various structures, while tailoring their bioactivity by controlling the proportions of the different phases. The results obtained in this thesis give valuable additional information and tools to the state of the art for designing glasses with respect to future clinical applications. With the knowledge gained we can identify different dissolution patters and use this information to improve the tuning of glass compositions. In addition, the novel online analysis approach provides an excellent opportunity to further enhance our knowledge of glass behavior in simulated body conditions.

Practical applications of a systematic approach to the chemical abatement of pollutants in water and air

The present dissertation is devoted to the systematic approach to the development of organic toxic and refractory pollutants abatement by chemical decomposition methods in aqueous and gaseous phases. The systematic approach outlines the basic scenario of chemical decomposition process applications with a step-by-step approximation to the most effective result with a predictable outcome for the full-scale application, confirmed by successful experience. The strategy includes the following steps: chemistry studies, reaction kinetic studies in interaction with the mass transfer processes under conditions of different control parameters, contact equipment design and studies, mathematical description of the process for its modelling and simulation, processes integration into treatment technology and its optimisation, and the treatment plant design. The main idea of the systematic approach for oxidation process introduction consists of a search for the most effective combination between the chemical reaction and the treatment device, in which the reaction is supposed to take place. Under this strategy,a knowledge of the reaction pathways, its products, stoichiometry and kinetics is fundamental and, unfortunately, often unavailable from the preliminary knowledge. Therefore, research made in chemistry on novel treatment methods, comprisesnowadays a substantial part of the efforts. Chemical decomposition methods in the aqueous phase include oxidation by ozonation, ozone-associated methods (O3/H2O2, O3/UV, O3/TiO2), Fenton reagent (H2O2/Fe2+/3+) and photocatalytic oxidation (PCO). In the gaseous phase, PCO and catalytic hydrolysis over zero valent ironsare developed. The experimental studies within the described methodology involve aqueous phase oxidation of natural organic matter (NOM) of potable water, phenolic and aromatic amino compounds, ethylene glycol and its derivatives as de-icing agents, and oxygenated motor fuel additives ¿ methyl tert-butyl ether (MTBE) ¿ in leachates and polluted groundwater. Gas-phase chemical decomposition includes PCO of volatile organic compounds and dechlorination of chlorinated methane derivatives. The results of the research summarised here are presented in fifteenattachments (publications and papers submitted for publication and under preparation).

Applications of graph transformation in tools for domain-specific modeling languages

The use of domain-specific languages (DSLs) has been proposed as an approach to cost-e ectively develop families of software systems in a restricted application domain. Domain-specific languages in combination with the accumulated knowledge and experience of previous implementations, can in turn be used to generate new applications with unique sets of requirements. For this reason, DSLs are considered to be an important approach for software reuse. However, the toolset supporting a particular domain-specific language is also domain-specific and is per definition not reusable. Therefore, creating and maintaining a DSL requires additional resources that could be even larger than the savings associated with using them. As a solution, di erent tool frameworks have been proposed to simplify and reduce the cost of developments of DSLs. Developers of tool support for DSLs need to instantiate, customize or configure the framework for a particular DSL. There are di erent approaches for this. An approach is to use an application programming interface (API) and to extend the basic framework using an imperative programming language. An example of a tools which is based on this approach is Eclipse GEF. Another approach is to configure the framework using declarative languages that are independent of the underlying framework implementation. We believe this second approach can bring important benefits as this brings focus to specifying what should the tool be like instead of writing a program specifying how the tool achieves this functionality. In this thesis we explore this second approach. We use graph transformation as the basic approach to customize a domain-specific modeling (DSM) tool framework. The contributions of this thesis includes a comparison of di erent approaches for defining, representing and interchanging software modeling languages and models and a tool architecture for an open domain-specific modeling framework that e ciently integrates several model transformation components and visual editors. We also present several specific algorithms and tool components for DSM framework. These include an approach for graph query based on region operators and the star operator and an approach for reconciling models and diagrams after executing model transformation programs. We exemplify our approach with two case studies MICAS and EFCO. In these studies we show how our experimental modeling tool framework has been used to define tool environments for domain-specific languages.

Applications of Industrial Statistics

This dissertation examines knowledge and industrial knowledge creation processes. It looks at the way knowledge is created in industrial processes based on data, which is transformed into information and finally into knowledge. In the context of this dissertation the main tool for industrial knowledge creation are different statistical methods. This dissertation strives to define industrial statistics. This is done using an expert opinion survey, which was sent to a number of industrial statisticians. The survey was conducted to create a definition for this field of applied statistics and to demonstrate the wide applicability of statistical methods to industrial problems. In this part of the dissertation, traditional methods of industrial statistics are introduced. As industrial statistics are the main tool for knowledge creation, the basics of statistical decision making and statistical modeling are also included. The widely known Data Information Knowledge Wisdom (DIKW) hierarchy serves as a theoretical background for this dissertation. The way that data is transformed into information, information into knowledge and knowledge finally into wisdom is used as a theoretical frame of reference. Some scholars have, however, criticized the DIKW model. Based on these different perceptions of the knowledge creation process, a new knowledge creation process, based on statistical methods is proposed. In the context of this dissertation, the data is a source of knowledge in industrial processes. Because of this, the mathematical categorization of data into continuous and discrete types is explained. Different methods for gathering data from processes are clarified as well. There are two methods for data gathering in this dissertation: survey methods and measurements. The enclosed publications provide an example of the wide applicability of statistical methods in industry. In these publications data is gathered using surveys and measurements. Enclosed publications have been chosen so that in each publication, different statistical methods are employed in analyzing of data. There are some similarities between the analysis methods used in the publications, but mainly different methods are used. Based on this dissertation the use of statistical methods for industrial knowledge creation is strongly recommended. With statistical methods it is possible to handle large datasets and different types of statistical analysis results can easily be transformed into knowledge.

Modification of Photocatalyst with Enhanced Photocatalytic Activity for Water

Increasing demand and shortage of energy resources and clean water due to the rapid development of industry, population growth and long term droughts have become an issue worldwide. As a result, global warming, long term droughts and pollution-related diseases are becoming more and more serious. The traditional technologies, such as precipitation, neutralization, sedimentation, filtration and waste immobilization, cannot prevent the pollution but restrict the waste chemicals only after the pollution emission. Meanwhile, most of these treatments cannot thoroughly degrade the contaminants and may generate toxic secondary pollutants into ecosystem. Heterogeneous photocatalysis as the innovative wastewater technology attracts many attention, because it is able to generate highly reactive transitory species for total degradation of organic compounds, water pathogens and disinfection by-products. Semiconductor as photocatalysts have demonstrated their efficiency in degrading a wide range of organics into readily biodegradable compounds, and eventually mineralized them to innocuous carbon dioxide and water. But, the efficiency of photocatalysis is limited, and hence, it is crucial issue to modify photocatalyst to enhance photocatalytic activity. In this thesis, first of all, two literature views are conducted. A survey of materials for photocatalysis has been carried out in order to summarize the properties and the applications of photocatalysts that have been developed in this field. Meanwhile, the strategy for the improvement of photocatalytic activity have been explicit discussed. Furthermore, all the raw material and chemicals used in this work have been listed as well as a specific experimental process and characterization method has been described. The synthesize methods of different photocatalysts have been depicted step by step. Among these cases, different modification strategies have been used to enhance the efficiency of photocatalyst on degradation of organic compounds (Methylene Blue or Phenol). For each case, photocatalytic experiments have been done to exhibit their photocatalytic activity.The photocatalytic experiments have been designed and its process have been explained and illustrated in detailed. Moreover, the experimental results have been shown and discussion. All the findings have been demonstrated in detail and discussed case by case. Eventually, the mechanisms on the improvement of photocatalytic activities have been clarified by characterization of samples and analysis of results. As a conclusion, the photocatalytic activities of selected semiconductors have been successfully enhanced via choosing appropriate strategy for the modification of photocatalysts.

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).