Secreted reporter proteins, a valuable complementary tool for non-invasive preclinical monitoring of brain tumour growth

2016 ◽  
Vol 5 (S7) ◽  
pp. S1486-S1488 ◽  
Author(s):  
Ludwig J. Dubois ◽  
Frank Verhaegen ◽  
Marc A. Vooijs
Keyword(s):  
Brain Tumour ◽  
Tumour Growth ◽  
Non Invasive ◽  
Reporter Proteins

scholarly journals Segmentation of Brain Images by Optimizing Clustering of Convolution Based Features

2021 ◽  
Vol 229 ◽  
pp. 01034
Author(s):  
Vikas Kumar
Keyword(s):  
Brain Tumour ◽  
Brain Tumours ◽  
Normal Brain ◽  
Resonance Imaging ◽  
Comprehensive Overview ◽  
Brain Images ◽  
Non Invasive ◽  
Tumour Segmentation ◽  
Tissue Contrast ◽  
Brain Tumour Segmentation

Brain tumour segmentation aims to separate the various types of tumour tissues like active cells, necrotic core, and edema from normal brain tissues of substantia alba (WM), grey matter (GM), and spinal fluid (CSF). Magnetic Resonance Imaging based brain tumour segmentation studies are attracting more and more attention in recent years thanks to non-invasive imaging and good soft tissue contrast of resonance Imaging (MRI) images. With the event of just about two decades, the ingenious approaches applying computer-aided techniques for segmenting brain tumour are getting more and more mature and coming closer to routine clinical applications. the aim of this paper is to supply a comprehensive overview for MRIbased brain tumour segmentation methods. Firstly, a quick introduction to brain tumours and imaging modalities of brain tumours is given in this proposed research, convolution based optimization. These stepwise step refine the segmentation and improve the classification parameter with the assistance of particle swarmoptimization.

scholarly journals O-093 Energy Expenditure In White Adipose Tissue Is Activated In Response To Brain Tumour Growth

2014 ◽  
Vol 99 (Suppl 2) ◽  
pp. A59.3-A60
Author(s):  
C Lam ◽  
L Robinson ◽  
ME Symonds ◽  
B Coyle
Keyword(s):  
Adipose Tissue ◽  
Energy Expenditure ◽  
Brain Tumour ◽  
White Adipose Tissue ◽  
Tumour Growth

The candidate tumour suppressor protein ING4 regulates brain tumour growth and angiogenesis

Nature ◽  
2004 ◽  
Vol 428 (6980) ◽  
pp. 328-332 ◽  
Author(s):  
Igor Garkavtsev ◽  
Sergey V. Kozin ◽  
Olga Chernova ◽  
Lei Xu ◽  
Frank Winkler ◽  
...  
Keyword(s):  
Brain Tumour ◽  
Tumour Growth ◽  
Tumour Suppressor ◽  
Tumour Suppressor Protein ◽  
Candidate Tumour Suppressor

scholarly journals Classification of Alzheimer's disease from MRI Images using CNN based Pre-trained VGG-19 Model

2020 ◽  
Vol 1 (2) ◽  
pp. 34-41
Author(s):  
Manimurugan S ◽  
Keyword(s):  
Brain Tumor ◽  
Brain Tumour ◽  
Objective Assessment ◽  
Survival Rates ◽  
Automated Detection ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Non Invasive ◽  
Magnetic Resonance Imaging Mri

Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vectormachine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.

scholarly journals A Bright Future for Fluorescence Imaging of Fungi in Living Hosts

2019 ◽  
Vol 5 (2) ◽  
pp. 29 ◽  
Author(s):  
Ambre Chapuis ◽  
Elizabeth Ballou ◽  
Donna MacCallum
Keyword(s):  
Mouse Models ◽  
Fluorescent Proteins ◽  
Imaging Techniques ◽  
Post Mortem ◽  
New Techniques ◽  
Ethical Concerns ◽  
Non Invasive ◽  
Reporter Proteins ◽  
Invasive Techniques

Traditional in vivo investigation of fungal infection and new antifungal therapies in mouse models is usually carried out using post mortem methodologies. However, biomedical imaging techniques focusing on non-invasive techniques using bioluminescent and fluorescent proteins have become valuable tools. These new techniques address ethical concerns as they allow reduction in the number of animals required to evaluate new antifungal therapies. They also allow better understanding of the growth and spread of the pathogen during infection. In this review, we concentrate on imaging technologies using different fungal reporter proteins. We discuss the advantages and limitations of these different reporters and compare the efficacy of bioluminescent and fluorescent proteins for fungal research.

scholarly journals Mathematical model of brain tumour growth with drug resistance

2021 ◽  
pp. 106013
Author(s):  
José Trobia ◽  
Kun Tian ◽  
Antonio M Batista ◽  
Celso Grebogi ◽  
Hai-Peng Ren ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Drug Resistance ◽  
Brain Tumour ◽  
Tumour Growth

Programmable base editing of mutated TERT promoter inhibits brain tumour growth

2020 ◽  
Vol 22 (3) ◽  
pp. 282-288 ◽  
Author(s):  
Xinjian Li ◽  
Xu Qian ◽  
Bin Wang ◽  
Yan Xia ◽  
Yanhua Zheng ◽  
...  
Keyword(s):  
Brain Tumour ◽  
Tumour Growth ◽  
Base Editing ◽  
Tert Promoter
Sign in / Sign up

Export Citation Format

Share Document