1,721,070 research outputs found
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
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
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
Single cell RNA-sequencing: a powerful yet still challenging technology to study cellular heterogeneity
No full textAlmost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic 'average' cannot outright be used as representative of the 'average cell'. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single-cell RNA sequencing (scRNA-Seq) enables the comparison of the transcriptomes of individual cells. This provides high-resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context-specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA-Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA-Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research
GeneFEAST: the pivotal, gene-centric step in functional enrichment analysis interpretation
Full text linkSummary: GeneFEAST, implemented in Python, is a gene-centric functional enrichment analysis summarization and visualization tool that can be applied to large functional enrichment analysis (FEA) results arising from upstream FEA pipelines. It produces a systematic, navigable HTML report, making it easy to identify sets of genes putatively driving multiple enrichments and to explore gene-level quantitative data first used to identify input genes. Further, GeneFEAST can juxtapose FEA results from multiple studies, making it possible to highlight patterns of gene expression amongst genes that are differentially expressed in at least one of multiple conditions, and which give rise to shared enrichments under those conditions. Thus, GeneFEAST offers a novel, effective way to address the complexities of linking up many overlapping FEA results to their underlying genes and data, advancing gene-centric hypotheses, and providing pivotal information for downstream validation experiments. Availability and implementation: GeneFEAST GitHub repository: https://github.com/avigailtaylor/GeneFEAST; Zenodo record: 10.5281/zenodo.14753734; Python Package Index: https://pypi.org/project/genefeast; Docker container: ghcr.io/avigailtaylor/genefeast
A Fast Feature Selection for Interpretable Modeling Based on Fuzzy Inference Systems
No full textLarge datasets are often beneficial for the generation of predictive models using machine learning approaches. However, it is often the case that not all variables in the dataset contain useful information. In fact, some variables might be useless, redundant, misleading, or even harmful to performance, both in terms of accuracy and computational effort. Because of that, Feature Selection (FS) is one of the most delicate and important steps in machine learning. This is even more relevant in the case of interpretable models based on Fuzzy Inference Systems (FIS). The reasons are two-fold: on the one hand, FIS are generally built on top of a data partitioning based on clustering, which can suffer from high dimensionality; on the other hand, the knowledge base of the FIS, to be concretely understandable, should not contain rules involving too many variables. FS can be performed using multiple approaches, most notably filter and wrapper methods. The latter are often based on evolutionary algorithms, where a population of candidate solutions (each representing a possible set of selected variables) evolves towards the optimal selection. Although wrapper methods can be effective, they are, in general, computationally expensive. In this work, we propose a completely different – and more computationally effective – algorithm based on Random Forest (RF) models. Specifically, we exploit RFs to rank variables according to their importance. Then, we use that information to perform a statistical analysis and determine the minimal set of features necessary to build an accurate FIS. We show the effectiveness of our approach by using two (semi)synthetic datasets built on real-world datasets, and we validate our approach by applying the FS method to a medical dataset
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