Logistic regression with Markov chains as covariates

The tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is an obvious carcinogen for lung cancer. Since CBMN (Cytokinesis-blocked micronucleus) has been found to be extremely sensitive to NNK-induced genetic damage, it is a potential important factor to predict the lung cancer risk. However, the association between lung cancer and NNK-induced genetic damage measured by CBMN assay has not been rigorously examined. ^ This research develops a methodology to model the chromosomal changes under NNK-induced genetic damage in a logistic regression framework in order to predict the occurrence of lung cancer. Since these chromosomal changes were usually not observed very long due to laboratory cost and time, a resampling technique was applied to generate the Markov chain of the normal and the damaged cell for each individual. A joint likelihood between the resampled Markov chains and the logistic regression model including transition probabilities of this chain as covariates was established. The Maximum likelihood estimation was applied to carry on the statistical test for comparison. The ability of this approach to increase discriminating power to predict lung cancer was compared to a baseline "non-genetic" model. ^ Our method offered an option to understand the association between the dynamic cell information and lung cancer. Our study indicated the extent of DNA damage/non-damage using the CBMN assay provides critical information that impacts public health studies of lung cancer risk. This novel statistical method could simultaneously estimate the process of DNA damage/non-damage and its relationship with lung cancer for each individual.^

Post-transcriptional Regulation of Mammalian Gene Expression in Non-coding Region of Target RNA

Tumor Suppressor Candidate 2 (TUSC2) is a novel tumor suppressor gene located in the human chromosome 3p21.3 region. TUSC2 mRNA transcripts could be detected on Northern blots in both normal lung and some lung cancer cell lines, but no endogenous TUSC2 protein could be detected in a majority of lung cancer cell lines. Mechanisms regulating TUSC2 protein expression and its inactivation in primary lung cancer cells are largely unknown. We investigated the role of the 5’- and 3’-untranslated regions (UTRs) of the TUSC2 gene in the regulation of TUSC2 protein expression. We found that two small upstream open-reading frames (uORFs) in the 5’UTR of TUSC2 could markedly inhibit the translational initiation of TUSC2 protein by interfering with the “scanning” of the ribosome initiation complexes. Site-specific stem-loop array reverse transcription-polymerase chain reaction (SLA-RT-PCR) verified several micoRNAs (miRNAs) targeted at 3’UTR and directed TUSC2 cleavage and degradation. In addition, we used the established let-7-targeted high mobility group A2 (Hmga2) mRNA as a model system to study the mechanism of regulation of target mRNA by miRNAs in mammalian cells under physiological conditions. There have been no evidence of direct link between mRNA downregulation and mRNA cleavages mediated by miRNAs. Here we showed that the endonucleolytic cleavages on mRNAs were initiated by mammalian miRNA in seed pairing style. Let-7 directed cleavage activities among the eight predicted potential target sites have varied efficiency, which are influenced by the positional and the structural contexts in the UTR. The 5’ cleaved RNA fragments were mostly oligouridylated at their 3’-termini and accumulated for delayed 5’–3’ degradation. RNA fragment oligouridylation played important roles in marking RNA fragments for delayed bulk degradation and in converting RNA degradation mode from 3’–5’ to 5’–3’ with cooperative efforts from both endonucleolytic and non-catalytic miRNA-induced silencing complex (miRISC). Our findings point to a mammalian miRNA-mediated mechanism for the regulation of mRNA that miRNA can decrease target mRNA through target mRNA cleavage and uridine addition

Cellular Uptake of Neutrohpil Elastase Links Inflammation to Adaptive Immunity

Many tumors arise from sites of inflammation providing evidence that innate immunity is a critical component in the development and progression of cancer. Neutrophils are primary mediators of the innate immune response. Upon activation, an important function of neutrophils is release of an assortment of proteins from their granules including the serine protease neutrophil elastase (NE). The effect of NE on cancer has been attributed primarily to its ability to degrade the extracellular matrix thereby promoting invasion and metastasis. Recently, it was shown that NE could be taken up by lung cancer cells leading to degradation of insulin receptor substrate-1 thereby promoting hyperactivity of the phosphatidylinositol-3 kinase (PI3K) pathway and tumor cell proliferation. To our knowledge, nobody has investigated uptake of NE by other tumor types. In addition, NE has broad substrate specificity suggesting that uptake of NE by tumor cells could impact processes regulating tumorigenensis other than activation of the PI3K pathway. Neutrophil elastase has been identified in breast cancer specimens where high levels of NE have prognostic significance. These studies have assessed NE levels in whole tumor lysates. Because the major source of NE is from activated neutrophils, we hypothesized that breast cancer cells do not have endogenous NE but may take up NE released by tumor associated neutrophils in the tumor microenvironment and that this could provide a link between the innate immune response to tumors and specific adaptive immune responses. In this thesis, we show that breast cancer cells lack endogenous NE expression and that they are able to take up NE resulting in increased generation of low molecular weight cyclin E (CCNE) and enhanced susceptibility to lysis by CCNE-specific cytotoxic T lymphocytes. We also show that after taking up NE and proteinase 3 (PR3), a second primary granule protease with significant homology to NE, breast cancer cells cross-present the NE- and PR3-derived peptide PR1 rendering them susceptible to PR1-targeted therapies. Taken together, our data support a role for NE uptake in modulating adaptive immune responses against breast cancer.

Design and Optimization of Four-dimensional Cone-beam Computed Tomography in Image-guided Radiation Therapy

The influence of respiratory motion on patient anatomy poses a challenge to accurate radiation therapy, especially in lung cancer treatment. Modern radiation therapy planning uses models of tumor respiratory motion to account for target motion in targeting. The tumor motion model can be verified on a per-treatment session basis with four-dimensional cone-beam computed tomography (4D-CBCT), which acquires an image set of the dynamic target throughout the respiratory cycle during the therapy session. 4D-CBCT is undersampled if the scan time is too short. However, short scan time is desirable in clinical practice to reduce patient setup time. This dissertation presents the design and optimization of 4D-CBCT to reduce the impact of undersampling artifacts with short scan times. This work measures the impact of undersampling artifacts on the accuracy of target motion measurement under different sampling conditions and for various object sizes and motions. The results provide a minimum scan time such that the target tracking error is less than a specified tolerance. This work also presents new image reconstruction algorithms for reducing undersampling artifacts in undersampled datasets by taking advantage of the assumption that the relevant motion of interest is contained within a volume-of-interest (VOI). It is shown that the VOI-based reconstruction provides more accurate image intensity than standard reconstruction. The VOI-based reconstruction produced 43% fewer least-squares error inside the VOI and 84% fewer error throughout the image in a study designed to simulate target motion. The VOI-based reconstruction approach can reduce acquisition time and improve image quality in 4D-CBCT.

DEVELOPMENT OF A BEAM-SPECIFIC PLANNING TARGET VOLUME AND A ROBUST PLAN ANALYSIS TOOL FOR PROTON THERAPY

Proton therapy is growing increasingly popular due to its superior dose characteristics compared to conventional photon therapy. Protons travel a finite range in the patient body and stop, thereby delivering no dose beyond their range. However, because the range of a proton beam is heavily dependent on the tissue density along its beam path, uncertainties in patient setup position and inherent range calculation can degrade thedose distribution significantly. Despite these challenges that are unique to proton therapy, current management of the uncertainties during treatment planning of proton therapy has been similar to that of conventional photon therapy. The goal of this dissertation research was to develop a treatment planning method and a planevaluation method that address proton-specific issues regarding setup and range uncertainties. Treatment plan designing method adapted to proton therapy: Currently, for proton therapy using a scanning beam delivery system, setup uncertainties are largely accounted for by geometrically expanding a clinical target volume (CTV) to a planning target volume (PTV). However, a PTV alone cannot adequately account for range uncertainties coupled to misaligned patient anatomy in the beam path since it does not account for the change in tissue density. In order to remedy this problem, we proposed a beam-specific PTV (bsPTV) that accounts for the change in tissue density along the beam path due to the uncertainties. Our proposed method was successfully implemented, and its superiority over the conventional PTV was shown through a controlled experiment.. Furthermore, we have shown that the bsPTV concept can be incorporated into beam angle optimization for better target coverage and normal tissue sparing for a selected lung cancer patient. Treatment plan evaluation method adapted to proton therapy: The dose-volume histogram of the clinical target volume (CTV) or any other volumes of interest at the time of planning does not represent the most probable dosimetric outcome of a given plan as it does not include the uncertainties mentioned earlier. Currently, the PTV is used as a surrogate of the CTV’s worst case scenario for target dose estimation. However, because proton dose distributions are subject to change under these uncertainties, the validity of the PTV analysis method is questionable. In order to remedy this problem, we proposed the use of statistical parameters to quantify uncertainties on both the dose-volume histogram and dose distribution directly. The robust plan analysis tool was successfully implemented to compute both the expectation value and its standard deviation of dosimetric parameters of a treatment plan under the uncertainties. For 15 lung cancer patients, the proposed method was used to quantify the dosimetric difference between the nominal situation and its expected value under the uncertainties.

Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction

Radiomics is the high-throughput extraction and analysis of quantitative image features. For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard of care computed tomography (CT) images to improve tumor diagnosis, staging, and response assessment. The first objective of this work was to show that CT image features extracted from pre-treatment NSCLC tumors could be used to predict tumor shrinkage in response to therapy. This is important since tumor shrinkage is an important cancer treatment endpoint that is correlated with probability of disease progression and overall survival. Accurate prediction of tumor shrinkage could also lead to individually customized treatment plans. To accomplish this objective, 64 stage NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. Quantitative image features were extracted and principal component regression with simulated annealing subset selection was used to predict shrinkage. Cross validation and permutation tests were used to validate the results. The optimal model gave a strong correlation between the observed and predicted shrinkages with . The second objective of this work was to identify sets of NSCLC CT image features that are reproducible, non-redundant, and informative across multiple machines. Feature sets with these qualities are needed for NSCLC radiomics models to be robust to machine variation and spurious correlation. To accomplish this objective, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. For each machine, quantitative image features with concordance correlation coefficient values greater than 0.90 were considered reproducible. Multi-machine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering. The findings showed that image feature reproducibility and redundancy depended on both the CT machine and the CT image type (average cine 4D-CT imaging vs. end-exhale cine 4D-CT imaging vs. helical inspiratory breath-hold 3D CT). For each image type, a set of cross-machine reproducible, non-redundant, and informative image features was identified. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multi-machine reproducibility and are the best candidates for clinical correlation.

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

Development and implementation of a dynamic heterogeneous proton equivalent anthropomorphic thorax phantom for the assessment of scanned proton beam therapy

DEVELOPMENT AND IMPLEMENTATION OF A DYNAMIC HETEROGENEOUS PROTON EQUIVALENT ANTHROPOMORPHIC THORAX PHANTOM FOR THE ASSESSMENT OF SCANNED PROTON BEAM THERAPY by James Leroy Neihart, B.S. APPROVED: ______________________________David Followill, Ph.D. ______________________________Peter Balter, Ph.D. ______________________________Narayan Sahoo, Ph.D. ______________________________Kenneth Hess, Ph.D. ______________________________Paige Summers, M.S. APPROVED: ____________________________ Dean, The University of Texas Graduate School of Biomedical Sciences at Houston DEVELOPMENT AND IMPLEMENTATION OF A DYNAMIC HETEROGENEOUS PROTON EQUIVALENT ANTHROPOMORPHIC THORAX PHANTOM FOR THE ASSESSMENT OF SCANNED PROTON BEAM THERAPY A THESIS Presented to the Faculty of The University of Texas Health Science Center at Houston andThe University of TexasMD Anderson Cancer CenterGraduate School of Biomedical Sciences in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE by James Leroy Neihart, B.S. Houston, Texas Date of Graduation August, 2013 Acknowledgments I would like to acknowledge my advisory committee members, chair David Followill, Ph.D., Peter Balter, Ph.D, Narayan Sahoo, Ph.D., Kenneth Hess, Ph.D., Paige Summers M.S. and, for their time and effort contributed to this project. I would additionally like to thank the faculty and staff at the PTC-H and the RPC who assisted in many aspects of this project. Falk Pӧnisch, Ph.D. for his breath hold proton therapy treatment expertise, Matt Palmer and Jaques Bluett for proton dosimetry assistance, Matt Kerr for verification plan assistance, Carrie Amador, Nadia Hernandez, Trang Nguyen, Andrea Molineu, Lynda McDonald for TLD and film dosimetry assistance. Finally, I would like to thank my wife and family for their support and encouragement during my research and studies. Development and implementation of a dynamic heterogeneous proton equivalent anthropomorphic thorax phantom for the assessment of scanned proton beam therapy By: James Leroy Neihart, B.S. Chair of Advisory Committee: David Followill, Ph.D Proton therapy has been gaining ground recently in radiation oncology. To date, the most successful utilization of proton therapy is in head and neck cases as well as prostate cases. These tumor locations do not suffer from the resulting difficulties of treatment delivery as a result of respiratory motion. Lung tumors require either breath hold or motion tracking, neither of which have been assessed with an end-to-end phantom for proton treatments. Currently, the RPC does not have a dynamic thoracic phantom for proton therapy procedure assessment. Additionally, such a phantom could be an excellent means of assessing quality assurance of the procedures of proton therapy centers wishing to participate in clinical trials. An eventual goal of this phantom is to have a means of evaluating and auditing institutions for the ability to start clinical trials utilizing proton therapy procedures for lung cancers. Therefore, the hypothesis of this study is that a dynamic anthropomorphic thoracic phantom can be created to evaluate end-to-end proton therapy treatment procedures for lung cancer to assure agreement between the measured and calculated dose within 5% / 5 mm with a reproducibility of 2%. Multiple materials were assessed for thoracic heterogeneity equivalency. The phantom was designed from the materials found to be in greatest agreement. The phantom was treated in an end-to-end treatment four times, which included simulation, treatment planning and treatment delivery. Each treatment plan was delivered three times to assess reproducibility. The dose measured within the phantom was compared to that of the treatment plan. The hypothesis was fully supported for three of the treatment plans, but failed the reproducibility requirement for the most aggressive treatment plan.

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

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

Estudio experimental sobre la efectividad y la viabilidad de distintas soluciones constructivas para reducir la concentración de gas radón en edificaciones

El trabajo de investigación desarrollado que ha dado lugar a la realización de esta Tesis, aborda la protección de los edificios frente a la entrada de gas radón y su acumulación en los espacios habitados. Dicho gas (isótopo del radón Rn-222) es un elemento radiactivo que se genera, principalmente, en terrenos con altos contenidos de radio (terrenos graníticos por ejemplo). Su alto grado de movilidad permite que penetre en los edificios a través de los materiales de cerramiento del mismo (porosidad de los materiales, fisuras, grietas y juntas) y se acumule en su interior, donde puede ser inhalado en altas concentraciones. La Organización Mundial de la Salud, califica al radón como agente cancerígeno de grado 1. Según este Organismo, el radón es la segunda causa de contracción de cáncer pulmonar detrás del tabaco. Como respuesta a esta alarma, distintos estados ya han elaborado normativas en las que se proponen soluciones para que los niveles de concentración de radón no superen los valores recomendados por los organismos internacionales responsables de la protección radiológica. En España aún no existe normativa de protección frente a este agente cancerígeno causante de numerosas muertes, y es por tal motivo evidente la necesidad de aportar documentación técnica que ayude a las administraciones nacionales y locales a desarrollar dicha normativa para ajustarse a las recomendaciones europeas e internacionales sobre los niveles que no se deben superar y que, por otro lado, ya han contemplado una gran cantidad de países. Como principal aportación de este trabajo se muestran los resultados de reducción de concentración de gas radón de distintas soluciones constructivas enfocadas a frenar la entrada de gas radón al interior de los edificios haciendo uso de técnicas y materiales habituales en el ámbito de la construcción en España. Para ello, se han estudiado las efectividades de dichas soluciones, en lo referente a su capacidad para frenar la inmisión de radón, en un prototipo de vivienda construido al efecto en un terreno con altas concentraciones de radón. Las soluciones propuestas y ensayadas han sido el resultado de una labor de optimización de los sistemas estudiados en la bibliografía con el fin de adaptar las técnicas a los sistemas constructivos habituales en España y en concreto a la situación real del prototipo de vivienda construido en un lugar con contenidos de radón en terreno muy elevados. El trabajo incluye un capítulo inicial con los conceptos básicos necesarios para entender la problemática que supone habitar en espacios con altos contenidos de radón. ABSTRACT The research developed, which has led to the completion of this thesis, deal with the protection of buildings against entry of radon gas and its accumulation in the ocupated spaces. This gas (radon isotope Rn-222) is a radioactive element generated, mainly, in areas with high levels of radio (granitic terrain for example). Its high mobility allows entering in buildings through the enclosure materials of it (porosity of materials, cracks, crevices and joints) and accumulates inside, where it can be inhaled in high concentrations. The World Health Organization describes radon gas as a carcinogen agent in level 1. According to this Agency, radon is the second leading cause of lung cancer behind tobacco. In response to this alarm, some states have developed regulations that propose solutions to reduce radon concentration levels for not exceeding the values recommended by international agencies responsible in radiation protection. In Spain there is still no legislation to protect against this carcinogen element that cause numerous deaths, and for that reason it is evident the need to provide technical documentation to help the national and local governments to develop legislation for reaching the European and international levels recommendations. As the main contribution of this work are the results of reducing radon concentration using different constructive solutions aimed to stop radon entry in buildings, with techniques and materials common in Spain. To do this, effectiveness of such solutions, have been studied in terms of its ability to stop radon entry in a housing prototype built for this purpose in an area with high radon levels. The solutions proposed and tested have been the result of a process of optimization of systems studied in the literature in order to adapt the techniques to Spanish building material and, specifically, to the actual situation of housing prototype built in a place with high contents of radon in soil. The work includes an initial chapter with the basic concepts needed to understand the problem of living in areas with high levels of radon.

Air quality modeling and mortality impact of fine particles reduction policies in Spain

Background: In recent years, Spain has implemented a number of air quality control measures that are expected to lead to a future reduction in fine particle concentrations and an ensuing positive impact on public health. Objectives: We aimed to assess the impact on mortality attributable to a reduction in fine particle levels in Spain in 2014 in relation to the estimated level for 2007. Methods: To estimate exposure, we constructed fine particle distribution models for Spain for 2007 (reference scenario) and 2014 (projected scenario) with a spatial resolution of 16x16 km2. In a second step, we used the concentration-response functions proposed by cohort studies carried out in Europe (European Study of Cohorts for Air Pollution Effects and Rome longitudinal cohort) and North America (American Cancer Society cohort, Harvard Six Cities study and Canadian national cohort) to calculate the number of attributable annual deaths corresponding to all causes, all non-accidental causes, ischemic heart disease and lung cancer among persons aged over 25 years (2005-2007 mortality rate data). We examined the effect of the Spanish demographic shift in our analysis using 2007 and 2012 population figures. Results: Our model suggested that there would be a mean overall reduction in fine particle levels of 1mg/m3 by 2014. Taking into account 2007 population data, between 8 and 15 all-cause deaths per 100,000 population could be postponed annually by the expected reduction in fine particle levels. For specific subgroups, estimates varied from 10 to 30 deaths for all non-accidental causes, from 1 to 5 for lung cancer, and from 2 to 6 for ischemic heart disease. The expected burden of preventable mortality would be even higher in the future due to the Spanish population growth. Taking into account the population older than 30 years in 2012, the absolute mortality impact estimate would increase approximately by 18%. Conclusions: Effective implementation of air quality measures in Spain, in a scenario with a short-term projection, would amount to an appreciable decline infine particle concentrations, and this, in turn, would lead to notable health-related benefits. Recent European cohort studies strengthen the evidence of an association between long-term exposure to fine particles and health effects, and could enhance the health impact quantification in Europe. Air quality models can contribute to improved assessment of air pollution health impact estimates, particularly in study areas without air pollution monitoring data.

Computerized polymorphic marker identification: Experimental validation and a predicted human polymorphism catalog

A computational system for the prediction of polymorphic loci directly and efficiently from human genomic sequence was developed and verified. A suite of programs, collectively called pompous (polymorphic marker prediction of ubiquitous simple sequences) detects tandem repeats ranging from dinucleotides up to 250 mers, scores them according to predicted level of polymorphism, and designs appropriate flanking primers for PCR amplification. This approach was validated on an approximately 750-kilobase region of human chromosome 3p21.3, involved in lung and breast carcinoma homozygous deletions. Target DNA from 36 paired B lymphoblastoid and lung cancer lines was amplified and allelotyped for 33 loci predicted by pompous to be variable in repeat size. We found that among those 36 predominately Caucasian individuals 22 of the 33 (67%) predicted loci were polymorphic with an average heterozygosity of 0.42. Allele loss in this region was found in 27/36 (75%) of the tumor lines using these markers. pompous provides the genetic researcher with an additional tool for the rapid and efficient identification of polymorphic markers, and through a World Wide Web site, investigators can use pompous to identify polymorphic markers for their research. A catalog of 13,261 potential polymorphic markers and associated primer sets has been created from the analysis of 141,779,504 base pairs of human genomic sequence in GenBank. This data is available on our Web site (pompous.swmed.edu) and will be updated periodically as GenBank is expanded and algorithm accuracy is improved.

Governing step of metastasis visualized in vitro

Metastasis is the ultimate life-threatening stage of cancer. The lack of accurate model systems thwarted studies of the metastatic cell’s basic biology. To follow continuously the succeeding stages of metastatic colony growth, we heritably labeled cells from the human lung adenocarcinoma cell line ANIP 973 with green fluorescent protein (GFP) by transfection with GFP cDNA. Labeled cells were then injected intravenously into nude mice, where, by 7 days, they formed brilliantly fluorescing metastatic colonies on mouse lung [Chishima, T., Miyagi, Y., Wang, X., Yang, M., Tan, Y., Shimada, H., Moossa, A. R. & Hoffman, R. M. (1997) Clin. Exp. Metastasis 15, 547–552]. The seeded lung tissue was then excised and incubated in the three-dimensional sponge-gel-matrix-supported histoculture that maintained the critical features of progressive in vivo tumor colonization while allowing continuous access for measurement and manipulation. Tumor progression was continuously visualized by GFP fluorescence in the same individual cultures over a 52-day period, during which the tumors spread throughout the lung. Histoculture tumor colonization was selective for lung cancer cells to grow on lung tissue, because no growth occurred on histocultured mouse liver tissue, which was also observed in vivo. The ability to support selective organ colonization in histoculture and visualize tumor progression by GFP fluorescence allows the in vitro study of the governing processes of metastasis [Kuo, T.-H., Kubota, T., Watanbe, M., Furukawa, T., Teramoto, T., Ishibiki, K., Kitajima, M., Moossa, A. R., Penman, S. & Hoffman, R. M. (1995) Proc. Natl. Acad. Sci. USA 92, 12085–12089]. The results presented here provide significant, new opportunities to understand and to develop treatments that prevent and possibly reverse metastasis.

Antitumor activity and pharmacokinetics of a mixed-backbone antisense oligonucleotide targeted to the RIα subunit of protein kinase A after oral administration

Overexpression of the RIα subunit of cAMP-dependent protein kinase (PKA) has been demonstrated in various human cancers. PKA has been suggested as a potential target for cancer therapy. The goal of the present study was to evaluate an anti-PKA antisense oligonucleotide (mixed-backbone oligonucleotide) as a therapeutic approach to human cancer treatment. The identified oligonucleotide inhibited the growth of cell lines of human colon cancer (LS174T, DLD-1), leukemia (HL-60), breast cancer (MCF-7, MDA-MB-468), and lung cancer (A549) in a time-, concentration-, and sequence-dependent manner. In a dose-dependent manner, the oligonucleotide displayed in vivo antitumor activity in severe combined immunodeficient and nude mice bearing xenografts of human cancers of the colon (LS174T), breast (MDA-MB-468), and lung (A549). The routes of drug administration were intraperitoneal and oral. Synergistic effects were found when the antisense oligonucleotide was used in combination with the cancer chemotherapeutic agent cisplatin. The pharmacokinetics of the oligonucleotide after oral administration of 35S-labeled oligonucleotide into tumor-bearing mice indicated an accumulation and retention of the oligonucleotide in tumor tissue. This study further provides a basis for clinical studies of the antisense oligonucleotide targeted to the RIα subunit of PKA (GEM 231) as a cancer therapeutic agent used alone or in combination with conventional chemotherapy.

National burden of disease in India from indoor air pollution

In the last decade, a number of quantitative epidemiological studies of specific diseases have been done in developing countries that for the first time allow estimation of the total burden of disease (mortality and morbidity) attributable to use of solid fuels in adult women and young children, who jointly receive the highest exposures because of their household roles. Few such studies are available as yet for adult men or children over 5 years. This paper evaluates the existing epidemiological studies and applies the resulting risks to the more than three-quarters of all Indian households dependent on such fuels. Allowance is made for the existence of improved stoves with chimneys and other factors that may lower exposures. Attributable risks are calculated in reference to the demographic conditions and patterns of each disease in India. Sufficient evidence is available to estimate risks most confidently for acute respiratory infections (ARI), chronic obstructive pulmonary disease (COPD), and lung cancer. Estimates for tuberculosis (TB), asthma, and blindness are of intermediate confidence. Estimates for heart disease have the lowest confidence. Insufficient quantitative evidence is currently available to estimate the impact of adverse pregnancy outcomes (e.g., low birthweight and stillbirth). The resulting conservative estimates indicate that some 400–550 thousand premature deaths can be attributed annually to use of biomass fuels in these population groups. Using a disability-adjusted lost life-year approach, the total is 4–6% of the Indian national burden of disease, placing indoor air pollution as a major risk factor in the country.