scholarly journals Computational modelling of anti-angiogenic therapies based on multiparametric molecular imaging data

2012 ◽  
Vol 57 (19) ◽  
pp. 6079-6101 ◽  
Author(s):  
Benjamin Titz ◽  
Kevin R Kozak ◽  
Robert Jeraj
Keyword(s):  
Molecular Imaging ◽  
Computational Modelling ◽  
Imaging Data

scholarly journals Near-infrared fluorescence molecular imaging of amyloid beta species and monitoring therapy in animal models of Alzheimer’s disease

2015 ◽  
Vol 112 (31) ◽  
pp. 9734-9739 ◽  
Author(s):  
Xueli Zhang ◽  
Yanli Tian ◽  
Can Zhang ◽  
Xiaoyu Tian ◽  
Alana W. Ross ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Imaging ◽  
Amyloid Beta ◽  
Near Infrared ◽  
Imaging Data ◽  
Curcumin Analog ◽  
Near Infrared Fluorescence ◽  
Brain Extraction ◽  
Monitoring Therapy

Near-infrared fluorescence (NIRF) molecular imaging has been widely applied to monitoring therapy of cancer and other diseases in preclinical studies; however, this technology has not been applied successfully to monitoring therapy for Alzheimer’s disease (AD). Although several NIRF probes for detecting amyloid beta (Aβ) species of AD have been reported, none of these probes has been used to monitor changes of Aβs during therapy. In this article, we demonstrated that CRANAD-3, a curcumin analog, is capable of detecting both soluble and insoluble Aβ species. In vivo imaging showed that the NIRF signal of CRANAD-3 from 4-mo-old transgenic AD (APP/PS1) mice was 2.29-fold higher than that from age-matched wild-type mice, indicating that CRANAD-3 is capable of detecting early molecular pathology. To verify the feasibility of CRANAD-3 for monitoring therapy, we first used the fast Aβ-lowering drug LY2811376, a well-characterized beta-amyloid cleaving enzyme-1 inhibitor, to treat APP/PS1 mice. Imaging data suggested that CRANAD-3 could monitor the decrease in Aβs after drug treatment. To validate the imaging capacity of CRANAD-3 further, we used it to monitor the therapeutic effect of CRANAD-17, a curcumin analog for inhibition of Aβ cross-linking. The imaging data indicated that the fluorescence signal in the CRANAD-17–treated group was significantly lower than that in the control group, and the result correlated with ELISA analysis of brain extraction and Aβ plaque counting. It was the first time, to our knowledge, that NIRF was used to monitor AD therapy, and we believe that our imaging technology has the potential to have a high impact on AD drug development.

Biomechanical functional and sensory modelling of the gastrointestinal tract

2008 ◽  
Vol 366 (1879) ◽  
pp. 3281-3299 ◽  
Author(s):  
Donghua Liao ◽  
Dina Lelic ◽  
Feng Gao ◽  
Asbjørn Mohr Drewes ◽  
Hans Gregersen
Keyword(s):  
Information And Communication Technologies ◽  
Computational Modelling ◽  
Medical Knowledge ◽  
Holistic Approach ◽  
Communication Technologies ◽  
Medical Procedures ◽  
Imaging Data ◽  
Gi Tract ◽  
Information And Communication ◽  
Biomechanical Reconstruction

The aim of this review is to describe the biomechanical, functional and sensory modelling work that can be used to integrate the physiological, anatomical and medical knowledge of the gastrointestinal (GI) system. The computational modelling in the GI tract was designed, implemented and evaluated using a series of information and communication technologies-based tools. These tools modelled the morphometry, biomechanics, functions and sensory aspects of the human GI tract. The research presented in this review is based on the virtual physiological human concept that pursues a holistic approach to representation of the human body. Such computational modelling combines imaging data, GI physiology, the gut–brain axis, geometrical and biomechanical reconstruction, and computer graphics for mechanical, electronic and pain analysis. The developed modelling will aid research and ensure that medical professionals benefit through the provision of relevant and precise information about a patient's condition. It will also improve the accuracy and efficiency of the medical procedures that could result in reduced cost for diagnosis and treatment.

scholarly journals A Patient-based Model to Investigate the Effect of Micro-structure Scars on the Optogenetic Defibrillation Success: A Computational Modelling Approach

2020 ◽  
Author(s):  
Elham Zakeri Zafarghandi ◽  
Fariba Bahrami
Keyword(s):  
Single Cell ◽  
Cardiac Tissue ◽  
Cell Model ◽  
Success Probability ◽  
Computational Cost ◽  
Computational Modelling ◽  
Relative Area ◽  
Imaging Data ◽  
Data Set ◽  
Interface Length

AbstractThis study investigates the role of the microstructure of real scars in the success of optogenetic defibrillation. To reduce the computational cost of high-order models (like Ten Tusscher Model, TTM) for a single cell as well as to take advantage of their ability to generate a more realistic output, we developed a low-order model of optogenetic cardiac tissue based on the modified Alieve-Panfilov single-cell model and estimated its parameters using a TTM. Two-dimensional electrophysiological cardiac tissue models were produced including different scar shapes that were extracted from Late Gadolinium-Enhanced (LGE) magnetic resonance imaging data set of 10 patients with non-ischemic dilated cardiomyopathy. The scar shapes were classified based on four criteria: transmurality, relative area, scar entropy, and interface length. Scar with the highest 25% of the relative area showed 25% of successful cases, this ratio is 27%, and 25% for a scar with the most top 25% of entropy, and transmurality, respectively. In comparison, the proportions are 61.54%, 44.44%, and 61.76%, for the lowest 25% of the area, entropy, and transmurality. We also investigated the efficacy of various methods for light-sensitive cells’ distribution within the cardiac tissue with scar. Four types of distributions were defined. Defibrillation within tissues with 0.1 light-sensitive out of all cells was 15 to 25% more successful than their counterparts with 0.05 light-sensitive cells. Lastly, we examined the effect of an earlier stimulation on the success probability of defibrillation. Our results indicated that inducing 0.5 msec earlier resulted in a roughly 15% rise in successful cases.

PET Brain Imaging Methodologies

2017 ◽  
Author(s):  
Ansel T. Hillmer ◽  
Kelly P. Cosgrove ◽  
Richard E. Carson
Keyword(s):  
Molecular Imaging ◽  
Brain Imaging ◽  
Volume Of Distribution ◽  
Group Differences ◽  
Binding Potential ◽  
Imaging Data ◽  
Imaging Studies ◽  
Standard Data ◽  
Positron Emission ◽  
Methodological Considerations

While quantitative and pharmacologically specific aspects distinguish molecular imaging, they also impose the need for considerable expertise to design, conduct, and analyze molecular imaging studies. Positron emission tomography (PET) brain imaging provides a powerful noninvasive tool for quantitative and pharmacologically specific clinical research. This chapter describes basic methodological considerations for PET brain imaging studies. First the physiological interpretation of the most common outcome measures of binding potential (BPND) and volume of distribution (VT) are described. Next, aspects of acquisition of PET imaging data and blood measurements for analysis are discussed, followed by a summary of standard data analysis techniques. Finally, various applications for the study of mental illness, including group differences, measurements of drug occupancy, and assay of acute neurotransmitter release are discussed.

scholarly journals Harnessing multimodality to enhance quantification and reproducibility of in vivo molecular imaging data

2008 ◽  
Vol 5 (11) ◽  
pp. iii-iv ◽  
Author(s):  
Gilbert D Feke ◽  
W Matthew Leevy ◽  
Sean Orton ◽  
Benjamin Geldhof ◽  
M Catherine Muenker ◽  
...  
Keyword(s):  
Molecular Imaging ◽  
Imaging Data

scholarly journals Multiphysics and multiscale modelling, data–model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics

2016 ◽  
Vol 6 (2) ◽  
pp. 20150083 ◽  
Author(s):  
Radomir Chabiniok ◽  
Vicky Y. Wang ◽  
Myrianthi Hadjicharalambous ◽  
Liya Asner ◽  
Jack Lee ◽  
...  
Keyword(s):  
Computational Models ◽  
Clinical Care ◽  
Computational Modelling ◽  
Basic Research ◽  
Cardiac Mechanics ◽  
Imaging Data ◽  
Cardiac Modelling ◽  
Cardiovascular Flow ◽  
And Function ◽  
Modelling Data

With heart and cardiovascular diseases continually challenging healthcare systems worldwide, translating basic research on cardiac (patho)physiology into clinical care is essential. Exacerbating this already extensive challenge is the complexity of the heart, relying on its hierarchical structure and function to maintain cardiovascular flow. Computational modelling has been proposed and actively pursued as a tool for accelerating research and translation. Allowing exploration of the relationships between physics, multiscale mechanisms and function, computational modelling provides a platform for improving our understanding of the heart. Further integration of experimental and clinical data through data assimilation and parameter estimation techniques is bringing computational models closer to use in routine clinical practice. This article reviews developments in computational cardiac modelling and how their integration with medical imaging data is providing new pathways for translational cardiac modelling.

Sign in / Sign up

Export Citation Format

Share Document