scholarly journals Towards the Design of a Patient-Specific Virtual Tumour

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Flavien Caraguel ◽  
Anne-Cécile Lesart ◽  
François Estève ◽  
Boudewijn van der Sanden ◽  
Angélique Stéphanou
Keyword(s):  
Image Analysis ◽  
Personalized Medicine ◽  
Tumour Growth ◽  
Therapeutic Strategies ◽  
Patient Specific ◽  
Multiscale Approach ◽  
Proof Of Concept ◽  
Spatiotemporal Evolution ◽  
Physiological Modelling ◽  
Key Events

The design of a patient-specific virtual tumour is an important step towards Personalized Medicine. However this requires to capture the description of many key events of tumour development, including angiogenesis, matrix remodelling, hypoxia, and cell state heterogeneity that will all influence the tumour growth kinetics and degree of tumour invasiveness. To that end, an integrated hybrid and multiscale approach has been developed based on data acquired on a preclinical mouse model as a proof of concept. Fluorescence imaging is exploited to build case-specific virtual tumours. Numerical simulations show that the virtual tumour matches the characteristics and spatiotemporal evolution of its real counterpart. We achieved this by combining image analysis and physiological modelling to accurately described the evolution of different tumour cases over a month. The development of such models is essential since a dedicated virtual tumour would be the perfect tool to identify the optimum therapeutic strategies that would make Personalized Medicine truly reachable and achievable.

A Proof of Concept Study of the Mechanical Behavior of Lattice Structures Used to Design a Shoulder Hemi-Prosthesis

2021 ◽  
Author(s):  
Marinela Peto ◽  
Oscar Aguilar-Rosas ◽  
Erick Erick Ramirez-Cedillo ◽  
Moises Jimenez ◽  
Adriana Hernandez ◽  
...  
Keyword(s):  
Mechanical Behavior ◽  
Mechanical Response ◽  
Mechanical Performance ◽  
Cell Attachment ◽  
Lattice Structure ◽  
Material Model ◽  
Patient Specific ◽  
Hexagonal Prism ◽  
Lattice Structures ◽  
Proof Of Concept

Abstract Lattice structures offer great benefits when employed in medical implants for cell attachment and growth (osseointegration), minimization of stress shielding phenomena, and weight reduction. This study is focused on a proof of concept for developing a generic shoulder hemi-prosthesis, from a patient-specific case of a 46 years old male with a tumor on the upper part of his humerus. A personalized biomodel was designed and a lattice structure was integrated in its middle portion, to lighten weight without affecting humerus’ mechanical response. To select the most appropriate lattice structure, three different configurations were initially tested: Tetrahedral Vertex Centroid (TVC), Hexagonal Prism Vertex Centroid (HPVC), and Cubic Diamond (CD). They were fabricated in resin by digital light processing and its mechanical behavior was studied via compression testing and finite element modeling (FEM). The selected structure according to the results was the HPVC, which was integrated in a digital twin of the biomodel to validate its mechanical performance through FEM but substituting the bone material model with a biocompatible titanium alloy (Ti6Al4V) suitable for prostheses fabrication. Results of the simulation showed acceptable levels of Von Mises stresses (325 MPa max.), below the elastic limit of the titanium alloys, and a better response (52 MPa max.) in a model with equivalent elastic properties, with stress performance in the same order of magnitude than the showed in bone’s material model.

scholarly journals PET-Specific Parameters and Radiotracers in Theoretical Tumour Modelling

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Matthew Jennings ◽  
Loredana G. Marcu ◽  
Eva Bezak
Keyword(s):  
Gene Expression ◽  
In Silico ◽  
Tumour Growth ◽  
Patient Specific ◽  
Computational Techniques ◽  
Accurate Information ◽  
Pet Ct ◽  
Ct Data ◽  
Specific Management

The innovation of computational techniques serves as an important step toward optimized, patient-specific management of cancer. In particular,in silicosimulation of tumour growth and treatment response may eventually yield accurate information on disease progression, enhance the quality of cancer treatment, and explain why certain therapies are effective where others are not.In silicomodelling is demonstrated to considerably benefit from information obtainable with PET and PET/CT. In particular, models have successfully integrated tumour glucose metabolism, cell proliferation, and cell oxygenation from multiple tracers in order to simulate tumour behaviour. With the development of novel radiotracers to image additional tumour phenomena, such as pH and gene expression, the value of PET and PET/CT data for use in tumour models will continue to grow. In this work, the use of PET and PET/CT information inin silicotumour models is reviewed. The various parameters that can be obtained using PET and PET/CT are detailed, as well as the radiotracers that may be used for this purpose, their utility, and limitations. The biophysical measures used to quantify PET and PET/CT data are also described. Finally, a list ofin silicomodels that incorporate PET and/or PET/CT data is provided and reviewed.

scholarly journals Erratum

2018 ◽  
Vol 46 (6) ◽  
pp. 722-722
Keyword(s):  
Bone Marrow ◽  
Image Analysis ◽  
Automated Image Analysis ◽  
Proof Of Concept ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Bone Marrow Cellularity ◽  
Marrow Cellularity ◽  
Rat Bone

Kozlowski, C., Brumm, J., and Cain, G. (2018). An Automated Image Analysis Method to Quantify Veterinary Bone Marrow Cellularity on H&E Sections. Tox Path46, 324–335. (Original DOI: 10.1177/0192623318766457). Kozlowski, C., Fullerton, A., Cain, G., Katavolos, P., Bravo, J., and Tarrant, J. M. (2018). Proof of Concept for an Automated Image Analysis Method to Quantify Rat Bone Marrow Hematopoietic Lineages on H&E Sections. Tox Path46, 336–347. (Oringinal DOI: 10.1177/0192623318766458). In the print issue and initial version of the online issue, the figures for Kozlowski, Brumm, and Cain were mistakenly placed into Kozlowski, Fullerton, et al., and vice versa. The online versions of both articles have been updated to display the appropriate figures.

scholarly journals Adipose-Derived Stem Cells Reduce Lipopolysaccharide-Induced Myelin Degradation and Neuroinflammatory Responses of Glial Cells in Mice

2020 ◽  
Vol 10 (3) ◽  
pp. 66
Author(s):  
Kateryna Yatsenko ◽  
Iryna Lushnikova ◽  
Alina Ustymenko ◽  
Maryna Patseva ◽  
Iryna Govbakh ◽  
...  
Keyword(s):  
Stem Cells ◽  
Personalized Medicine ◽  
Neurodegenerative Diseases ◽  
Brain Slices ◽  
Brain Inflammation ◽  
Patient Specific ◽  
Adipose Derived Stem Cells ◽  
Humoral Factors

Brain inflammation is a key event triggering the pathological process associated with many neurodegenerative diseases. Current personalized medicine and translational research in neurodegenerative diseases focus on adipose-derived stem cells (ASCs), because they are patient-specific, thereby reducing the risk of immune rejection. ASCs have been shown to exert a therapeutic effect following transplantation in animal models of neuroinflammation. However, the mechanisms by which transplanted ASCs promote cell survival and/or functional recovery are not fully understood. We investigated the effects of ASCs in in vivo and in vitro lipopolysaccharide (LPS)-induced neuroinflammatory models. Brain damage was evaluated immunohistochemically using specific antibody markers of microglia, astroglia and oligodendrocytes. ASCs were used for intracerebral transplantation, as well as for non-contact co-culture with brain slices. In both in vivo and in vitro models, we found that LPS caused micro- and astroglial activation and oligodendrocyte degradation, whereas the presence of ASCs significantly reduced the damaging effects. It should be noted that the observed ASCs protection in a non-contact co-culture suggested that this effect was due to humoral factors via ASC-released biomodulatory molecules. However, further clinical studies are required to establish the therapeutic mechanisms of ASCs, and optimize their use as a part of a personalized medicine strategy.

scholarly journals Nanomedicine-Based Neuroprotective Strategies in Patient Specific-iPSC and Personalized Medicine

2014 ◽  
Vol 15 (3) ◽  
pp. 3904-3925 ◽  
Author(s):  
Shih-Fan Jang ◽  
Wei-Hsiu Liu ◽  
Wen-Shin Song ◽  
Kuan-Lin Chiang ◽  
Hsin-I Ma ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Patient Specific ◽  
Neuroprotective Strategies ◽  
And Personalized Medicine

scholarly journals One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 141
Author(s):  
Jianguang Li ◽  
Wen Li ◽  
Cong Jin ◽  
Lijuan Yang ◽  
Hui He
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Environmental Benefits ◽  
Sustainable Urban Development ◽  
Aerial Image ◽  
Proof Of Concept ◽  
Data Set ◽  
Concept Study ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

The segmentation of buildings in remote-sensing (RS) images plays an important role in monitoring landscape changes. Quantification of these changes can be used to balance economic and environmental benefits and most importantly, to support the sustainable urban development. Deep learning has been upgrading the techniques for RS image analysis. However, it requires a large-scale data set for hyper-parameter optimization. To address this issue, the concept of “one view per city” is proposed and it explores the use of one RS image for parameter settings with the purpose of handling the rest images of the same city by the trained model. The proposal of this concept comes from the observation that buildings of a same city in single-source RS images demonstrate similar intensity distributions. To verify the feasibility, a proof-of-concept study is conducted and five fully convolutional networks are evaluated on five cities in the Inria Aerial Image Labeling database. Experimental results suggest that the concept can be explored to decrease the number of images for model training and it enables us to achieve competitive performance in buildings segmentation with decreased time consumption. Based on model optimization and universal image representation, it is full of potential to improve the segmentation performance, to enhance the generalization capacity, and to extend the application of the concept in RS image analysis.

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