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Walsh CL, Berg M, West H, Holroyd NA, Walker-Samuel S, Shipley RJ. Reconstructing microvascular network skeletons from 3D images: What is the ground truth? Comput Biol Med 2024; 171:108140. [PMID: 38422956 DOI: 10.1016/j.compbiomed.2024.108140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/29/2024] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
Structural changes to microvascular networks are increasingly highlighted as markers of pathogenesis in a wide range of disease, e.g. Alzheimer's disease, vascular dementia and tumour growth. This has motivated the development of dedicated 3D imaging techniques, alongside the creation of computational modelling frameworks capable of using 3D reconstructed networks to simulate functional behaviours such as blood flow or transport processes. Extraction of 3D networks from imaging data broadly consists of two image processing steps: segmentation followed by skeletonisation. Much research effort has been devoted to segmentation field, and there are standard and widely-applied methodologies for creating and assessing gold standards or ground truths produced by manual annotation or automated algorithms. The Skeletonisation field, however, lacks widely applied, simple to compute metrics for the validation or optimisation of the numerous algorithms that exist to extract skeletons from binary images. This is particularly problematic as 3D imaging datasets increase in size and visual inspection becomes an insufficient validation approach. In this work, we first demonstrate the extent of the problem by applying 4 widely-used skeletonisation algorithms to 3 different imaging datasets. In doing so we show significant variability between reconstructed skeletons of the same segmented imaging dataset. Moreover, we show that such a structural variability propagates to simulated metrics such as blood flow. To mitigate this variability we introduce a new, fast and easy to compute super metric that compares the volume, connectivity, medialness, bifurcation point identification and homology of the reconstructed skeletons to the original segmented data. We then show that such a metric can be used to select the best performing skeletonisation algorithm for a given dataset, as well as to optimise its parameters. Finally, we demonstrate that the super metric can also be used to quickly identify how a particular skeletonisation algorithm could be improved, becoming a powerful tool in understanding the complex implication of small structural changes in a network.
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Affiliation(s)
- Claire L Walsh
- Department of Mechanical Engineering, University College London, United Kingdom
| | - Maxime Berg
- Department of Mechanical Engineering, University College London, United Kingdom.
| | - Hannah West
- Department of Mechanical Engineering, University College London, United Kingdom
| | - Natalie A Holroyd
- Centre for Computational Medicine, Division of Medicine, University College London, United Kingdom
| | - Simon Walker-Samuel
- Centre for Computational Medicine, Division of Medicine, University College London, United Kingdom
| | - Rebecca J Shipley
- Department of Mechanical Engineering, University College London, United Kingdom; Centre for Computational Medicine, Division of Medicine, University College London, United Kingdom
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Zhu J, Huang J, Sun Y, Xu W, Qian H. Emerging role of extracellular vesicles in diabetic retinopathy. Theranostics 2024; 14:1631-1646. [PMID: 38389842 PMCID: PMC10879872 DOI: 10.7150/thno.92463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/26/2024] [Indexed: 02/24/2024] Open
Abstract
Diabetic retinopathy (DR), a complex complication of diabetes mellitus (DM), is a leading cause of adult blindness. Hyperglycemia triggers DR, resulting in microvascular damage, glial apoptosis, and neuronal degeneration. Inflammation and oxidative stress play crucial roles during this process. Current clinical treatments for DR primarily target the advanced retinal disorder but offer limited benefits with inevitable side effects. Extracellular vesicles (EVs) exhibit unique morphological features, contents, and biological properties and can be found in cell culture supernatants, various body fluids, and tissues. In DR, EVs with specific cargo composition would induce the reaction of receptor cell once internalized, mediating cellular communication and disease progression. Increasing evidence indicates that monitoring changes in EV quantity and content in DR can aid in disease diagnosis and prognosis. Furthermore, extensive research is investigating the potential of these nanoparticles as effective therapeutic agents in preclinical models of DR. This review explores the current understanding of the pathological effects of EVs in DR development, discusses their potential as biomarkers and therapeutic strategies, and paves the way for further research and therapeutic advancements.
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Affiliation(s)
- Junyan Zhu
- Department of Gynecology and obstetrics, The Affiliated Yixing Hospital of Jiangsu University, 214200, China
- Jiangsu Province Key Laboratory of Medical Science and Laboratory Medicine, Department of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Jin Huang
- Department of Gynecology and obstetrics, The Affiliated Yixing Hospital of Jiangsu University, 214200, China
| | - Yaoxiang Sun
- Department of clinical laboratory, The Affiliated Yixing Hospital of Jiangsu University, Yixing, 214200, China
| | - Wenrong Xu
- Department of Gynecology and obstetrics, The Affiliated Yixing Hospital of Jiangsu University, 214200, China
- Jiangsu Province Key Laboratory of Medical Science and Laboratory Medicine, Department of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Hui Qian
- Jiangsu Province Key Laboratory of Medical Science and Laboratory Medicine, Department of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu 212013, China
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Delgado-Rodriguez P, Brooks CJ, Vaquero JJ, Muñoz-Barrutia A. Innovations in ex vivo Light Sheet Fluorescence Microscopy. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 168:37-51. [PMID: 34293338 DOI: 10.1016/j.pbiomolbio.2021.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 07/12/2021] [Indexed: 06/13/2023]
Abstract
Light Sheet Fluorescence Microscopy (LSFM) has revolutionized how optical imaging of biological specimens can be performed as this technique allows to produce 3D fluorescence images of entire samples with a high spatiotemporal resolution. In this manuscript, we aim to provide readers with an overview of the field of LSFM on ex vivo samples. Recent advances in LSFM architectures have made the technique widely accessible and have improved its acquisition speed and resolution, among other features. These developments are strongly supported by quantitative analysis of the huge image volumes produced thanks to the boost in computational capacities, the advent of Deep Learning techniques, and by the combination of LSFM with other imaging modalities. Namely, LSFM allows for the characterization of biological structures, disease manifestations and drug effectivity studies. This information can ultimately serve to develop novel diagnostic procedures, treatments and even to model the organs physiology in healthy and pathological conditions.
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Affiliation(s)
- Pablo Delgado-Rodriguez
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
| | - Claire Jordan Brooks
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
| | - Juan José Vaquero
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Arrate Muñoz-Barrutia
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.
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Chang CC, Huang ZY, Shih SF, Luo Y, Ko A, Cui Q, Sumner J, Cavallero S, Das S, Gao W, Sinsheimer J, Bui A, Jacobs JP, Pajukanta P, Wu H, Tai YC, Li Z, Hsiai TK. Electrical impedance tomography for non-invasive identification of fatty liver infiltrate in overweight individuals. Sci Rep 2021; 11:19859. [PMID: 34615918 PMCID: PMC8494919 DOI: 10.1038/s41598-021-99132-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 09/16/2021] [Indexed: 01/23/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of cardiometabolic diseases in overweight individuals. While liver biopsy is the current gold standard to diagnose NAFLD and magnetic resonance imaging (MRI) is a non-invasive alternative still under clinical trials, the former is invasive and the latter costly. We demonstrate electrical impedance tomography (EIT) as a portable method for detecting fatty infiltrate. We enrolled 19 overweight subjects to undergo liver MRI scans, followed by EIT measurements. The MRI images provided the a priori knowledge of the liver boundary conditions for EIT reconstruction, and the multi-echo MRI data quantified liver proton-density fat fraction (PDFF%) to validate fat infiltrate. Using the EIT electrode belts, we circumferentially injected pairwise current to the upper abdomen, followed by acquiring the resulting surface-voltage to reconstruct the liver conductivity. Pearson's correlation analyses compared EIT conductivity or MRI PDFF with body mass index, age, waist circumference, height, and weight variables. We reveal that the correlation between liver EIT conductivity or MRI PDFF with demographics is statistically insignificant, whereas liver EIT conductivity is inversely correlated with MRI PDFF (R = -0.69, p = 0.003, n = 16). As a pilot study, EIT conductivity provides a portable method for operator-independent and cost-effective detection of hepatic steatosis.
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Affiliation(s)
- Chih-Chiang Chang
- Department of Bioengineering, UCLA, Los Angeles, CA, USA.,Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Zi-Yu Huang
- Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Shu-Fu Shih
- Department of Bioengineering, UCLA, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Yuan Luo
- Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Arthur Ko
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Qingyu Cui
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Jennifer Sumner
- Department of Psychology, College of Life Sciences, UCLA, Los Angeles, CA, USA
| | - Susana Cavallero
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Swarna Das
- Department of Bioengineering, UCLA, Los Angeles, CA, USA
| | - Wei Gao
- Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Janet Sinsheimer
- Department of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, CA, USA.,Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Alex Bui
- Department of Bioengineering, UCLA, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Jonathan P Jacobs
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Division of Digestive Diseases, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Greater Los Angeles VA Healthcare System, Los Angeles, CA, USA
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Holden Wu
- Department of Bioengineering, UCLA, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Yu-Chong Tai
- Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Zhaoping Li
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Greater Los Angeles VA Healthcare System, Los Angeles, CA, USA.,Center for Human Nutrition, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Tzung K Hsiai
- Department of Bioengineering, UCLA, Los Angeles, CA, USA. .,Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA. .,Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. .,Greater Los Angeles VA Healthcare System, Los Angeles, CA, USA.
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