302 research outputs found
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
Backscatter 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
New 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
Moment equations for the mixed formulation of the Hodge Laplacian with stochastic data
No full textWe study the mixed formulation of the stochastic Hodge-Laplace problem defined on a n-dimensional domain D (n ≥ 1), with random forcing term. In particular, we focus on the magnetostatic problem and on the Darcy problem in the three dimensional case. We derive and analyze the moment equations, that is the deterministic equations solved by the m-th moment (m ≥ 1) of the unique stochastic solution of the stochastic problem. We find stable tensor product finite element discretizations, both full and sparse, and provide optimal order of convergence estimates. In particular,
we prove the inf-sup condition for sparse tensor product finite element spaces
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
A modified EACOP implementation for real-parameter single objective optimization problems
No full textRobustness and reproducibility for AI learning in biomedical sciences: RENOIR
No full textAbstract Artificial intelligence (AI) techniques are increasingly applied across various domains, favoured by the growing acquisition and public availability of large, complex datasets. Despite this trend, AI publications often suffer from lack of reproducibility and poor generalisation of findings, undermining scientific value and contributing to global research waste. To address these issues and focusing on the learning aspect of the AI field, we present RENOIR (REpeated random sampliNg fOr machIne leaRning), a modular open-source platform for robust and reproducible machine learning (ML) analysis. RENOIR adopts standardised pipelines for model training and testing, introducing elements of novelty, such as the dependence of the performance of the algorithm on the sample size. Additionally, RENOIR offers automated generation of transparent and usable reports, aiming to enhance the quality and reproducibility of AI studies. To demonstrate the versatility of our tool, we applied it to benchmark datasets from health, computer science, and STEM (Science, Technology, Engineering, and Mathematics) domains. Furthermore, we showcase RENOIR’s successful application in recently published studies, where it identified classifiers for SET2D and TP53 mutation status in cancer. Finally, we present a use case where RENOIR was employed to address a significant pharmacological challenge—predicting drug efficacy. RENOIR is freely available at https://github.com/alebarberis/renoir
Modelling genotypes in their microenvironment to predict single- and multi-cellular behaviour
No full textA 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
The role of hypoxia regulated microRNAs in cancer.
No full textThe molecular response of cancer cells to hypoxia is the focus of intense research. In the last decade, research into microRNAs (miRNAs), small RNAs which have a role in regulation of mRNA and translation, has grown exponentially. miR-210 has emerged as the predominant miRNA regulated by hypoxia. Elucidation of its targets points to a variety of roles for this, and other hypoxia-regulated miRNAs (HRMs), in tumour growth and survival. miR-210 expression correlates with poor survival in cancer patients, and shows promise for future use as a tumour marker or therapeutic agent. The role of miR-210 and other HRMs in cancer biology is the subject of this review
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