| 1. |
- Heslop, James A., et al.
(author)
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Concise Review : Workshop Review: Understanding and Assessing the Risks of Stem Cell-Based Therapies
- 2015
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In: Stem Cells Translational Medicine. - : Oxford University Press (OUP). - 2157-6564 .- 2157-6580. ; 4:4, s. 389-400
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Research review (peer-reviewed)abstract
- The field of stem cell therapeutics is moving ever closer to widespread application in the clinic. However, despite the undoubted potential held by these therapies, the balance between risk and benefit remains difficult to predict. As in any new field, a lack of previous application in man and gaps in the underlying science mean that regulators and investigators continue to look for a balance between minimizing potential risk and ensuring therapies are not needlessly kept from patients. Here, we attempt to identify the important safety issues, assessing the current advances in scientific knowledge and how they may translate to clinical therapeutic strategies in the identification and management of these risks. We also investigate the tools and techniques currently available to researchers during preclinical and clinical development of stem cell products, their utility and limitations, and how these tools may be strategically used in the development of these therapies. We conclude that ensuring safety through cutting-edge science and robust assays, coupled with regular and open discussions between regulators and academic/industrial investigators, is likely to prove the most fruitful route to ensuring the safest possible development of new products.
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| 2. |
- Reymbaut, Alexis, et al.
(author)
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Magic DIAMOND : Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding
- 2021
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In: Medical Image Analysis. - : Elsevier BV. - 1361-8415. ; 70
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Journal article (peer-reviewed)abstract
- Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition schemes. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this “Magic DIAMOND” model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We demonstrate using in silico evaluations that tensor-valued diffusion encoding significantly improves Magic DIAMOND's accuracy. Most importantly, we show in vivo that our estimated metrics can be robustly mapped along tracks across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts.
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