1,721,153 research outputs found
Factor analysis of predictive markers of response and resistance to primary chemo-endocrine treatment in elderly breast cancer (BC) patients
No full textBackscatter and dose perturbations for low- to medium-energy electron point sources at the interface between materials with different atomic numbers
No full text: Electron backscatter at interfaces between dissimilar media can affect dosimetry and should be taken into consideration in radiotherapy and in radiobiology experiments. Backscatter dose perturbations depend upon factors such as electron energy, medium atomic number (Z), and distance from the interface. This study quantifies the backscatter dose factor (BSDF) for electron point sources of energy between 0.1 to 3 MeV in water at the interface with scattering materials ranging in Z from (13)Al to (79)Au. A Monte Carlo code that performs dose calculations for monoenergetic and continuous-spectrum electron sources was developed using EGSnrc transport routines. The BSDF was quantified in a parallel layers geometry (BSDF(1D)) and three-dimensional voxel geometry (BSDF(3D)). The BSDF(1D) near the interface increased up to 52% with decreasing energy from 3 to 0.1 MeV and increasing Z from 13 to 79. The analysis of the BSDF(3D) showed a significant dependence of the scattered electron angular distribution on Z and energy, with a decrease in isotropy going from high to low Z. This effect proves the importance of considering the correct geometry when quantifying the BSDF for electron sources, especially when the dimensions of the relevant dose-collecting volume are comparable with the CSDA range of the source
FUNDAMENTAL RADIOBIOLOGY AND ITS APPLICATION TO RADIATION ONCOLOGY
No full textA brief overview of fundamental concepts in radiobiology is provided. The concept of cell survival as ability to retain reproductive integrity is introduced, and critical variables influencing the cell survival curve as a function of absorbed radiation dose are discussed. Application of these concepts to radiation oncology and radiotherapy is then outlined. Examples are provided of clinical studies that can be performed with current high-throughput molecular biology and imaging technology. The type of information derived from these studies and its potential are discussed in the context of radiotherapy trial design, radiotherapy schedule and modality tailoring, and planning of treatment dose. © 2009 Springer Science + Business Media B.V
Machine learning using Gene-Sets to infer miRNA function
No full textmiRNA are regulators of cell phenotype, and there is clear evidence that these small posttranscriptional modifiers of gene expression are involved in defining a cellular response across states of development and disease. Classical methods for elucidating the repressive effect of a miRNA on its targets involve controlling for the many factors influencing miRNA action, and this can be achieved in cell lines, but misses tissue and organism level context which are key to a miRNA function. Also, current technology to carry out this validation is limited in both generalizability and throughput. Methodologies with greater scalability and rapidity are required to better understand the function of these important species of RNA. To this end, there is an increasing store of RNA expression level data incorporating both miRNA and mRNA, and in this chapter, we describe how to use machine learning and gene-sets to translate the knowledge of phenotype defined by mRNA to putative roles for miRNA. We outline our approach to this process and highlight how it was done for our miRNA annotation of the hallmarks of cancer using the Cancer Genome Atlas (TCGA) dataset. The concepts we present are applicable across datasets and phenotypes, and we highlight potential pitfalls and challenges that may be faced as they are used
Guest editorial data science in smart healthcare: Challenges and opportunities
Full text linkThe fifteen articles in this special section focus on data science used in smart healthcare applications. A shift toward a data-driven socio-economic health model is occurring. This is the result of the increased volume, velocity and variety of data collected from the public and private sector in healthcare, and biology in general. In the past five-years, there has been an impressive development of computational intelligence and informatics methods for application to health and biomedical science. However, the effective use of data to address the scale and scope of human health problems has yet to realize its full potential. The barriers limiting the impact of practical application of standard data mining and machine learning methods have been inherent to the characteristics of health data. Besides the volume of the data (‘big data’), these are challenging due to their heterogeneity, complexity, variability and dynamic nature. Finally, data management and interpretability of the results have been limited by practical challenges in implementing new and also existing standards across the different health providers and research institutions. The scope of this Special issue is to discuss some of these challenges and opportunities in health and biological data science, with particular focus on the infrastructure, software, methods and algorithms needed to analyze large datasets in biological and clinical research
A modified EACOP implementation for real-parameter single objective optimization problems
No full textNew perspectives in liquid biopsy for glioma patients
No full textReview.
Gliomas are the most common primary tumors of the central nervous system. They are characterized by a disappointing prognosis and ineffective therapy that has shown no substantial improvements in the past 20 years. The lack of progress in treating gliomas is linked with the inadequacy of suitable tumor samples to plan translational studies and support laboratory developments. To overcome the use of tumor tissue, this commentary review aims to highlight the potential for the clinical application of liquid biopsy (intended as the study of circulating biomarkers in the blood), focusing on circulating tumor cells, circulating DNA and circulating noncoding RNA.
Recent findings
Thanks to the increasing sensitivity of sequencing techniques, it is now possible to analyze circulating nucleic acids and tumor cells (liquid biopsy).
Summary
Although studies on the use of liquid biopsy are still at an early stage, the potential clinical applications of liquid biopsy in the study of primary brain cancer are many and have the potential to revolutionize the approach to neuro-oncology, and importantly, they offer the possibility of gathering information on the disease at any time during its history
Informed Consent, Biobank Research, and Locality: Perceptions of Breast Cancer Patients in Three European Countries
No full textComparative studies are missing that explore how socio-cultural and institutional circumstances influence patient comprehension and expectations regarding informed consent for current and future research on their tissue and data. This study compares how breast cancer patients in three European countries (the United Kingdom, Belgium, and Germany) who have consented to participate in tumor banking assess the given consent and the accompanying local contextual factors influencing it. Our survey demonstrates that only 59% of the patients in the British survey, but about 90% in the German and Belgian surveys, correctly recalled tissue and data donation for study purposes. Of those who remembered the study participation status correctly, about 90% had altruistic motives. At the same time, approximately half of the survey participants, or even 70% of the Belgians, expected personal benefit from research participation and information on cancer risk within the family. About half of the interviewees, but only 27% of the British participants, definitively wanted to be asked for re-consent for future research. Of the local contextual factors under study, participants’ appraisals of medical science and data protection were particularly pertinent. More culturally and contextually sensitive comparative research is needed to better understand patient attitudes toward research participation and tissue donation in the context of biobank research
Modelling genotypes in their microenvironment to predict single- and multi-cellular behaviour
Full text linkA cell's phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behaviour. Deciphering genotype-phenotype relationships has been crucial to understand normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, it does typically not consider the physical microenvironment, which is a key determinant of phenotype.
In this study, we present a novel modelling framework that enables to study the link between genotype, signalling networks and cell behaviour in a 3D microenvironment. To achieve this we bring together Agent Based Modelling, a powerful computational modelling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modelling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrients availability, and their interactions.
Using cancer as a model system, we illustrate the how this framework delivers a unique opportunity to identify determinants of single-cell behaviour, while uncovering emerging properties of multi-cellular growth
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