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Kimondo JJ, Said RR, Wu J, Tian C, Wu Z. Mechanical rheological model on the assessment of elasticity and viscosity in tissue inflammation: A systematic review. PLoS One 2024; 19:e0307113. [PMID: 39008477 PMCID: PMC11249233 DOI: 10.1371/journal.pone.0307113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 06/30/2024] [Indexed: 07/17/2024] Open
Abstract
Understanding the extent of inflammation is crucial for early disease detection, monitoring disease progression, and evaluating treatment responses. Over the past decade, researchers have demonstrated the need to understand the extent of inflammation through qualitative or quantitative characterization of tissue viscoelasticity using different techniques. In this scientific review, an examination of research on the association between elasticity and Viscosity in diseases, particularly as tissue inflammation progresses, is conducted. A review of utilizing mechanical rheological models to characterize quantitative viscoelastic parameters of normal and inflamed tissues is also undertaken. Based on inclusion and exclusion criteria, we identified 14 full-text studies suitable for review out of 290 articles published from January 2000 to January 2024. We used PRISMA guidelines for the systematic review. In the review, three studies demonstrated the criterion used by the researchers in identifying the best rheological model. Eleven studies showed the clinical application of the rheological model in quantifying the viscoelastic properties of normal and pathological tissue. The review quantified viscoelastic parameters for normal and pathological tissue across various soft tissues. It evaluated the effectiveness of each viscoelastic property in distinguishing between normal and pathological tissue stiffness. Furthermore, the review outlined additional viscoelastic-related parameters for researchers to consider in future stiffness classification studies.
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Affiliation(s)
- Jotham Josephat Kimondo
- School of life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ramadhan Rashid Said
- School of life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jun Wu
- School of Medical Imaging, North Sichuan Medical College, Nanchong, Sichuan, China
| | - Chao Tian
- Department of Women’s Health, Sichuan Cancer Hospital, Chengdu, China
| | - Zhe Wu
- School of life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Tianfu Jincheng Laboratory, City of Future Medicine, Chengdu, China
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Rinaldi L, Giorgione C, Mormone A, Esposito F, Rinaldi M, Berretta M, Marfella R, Romano C. Non-Invasive Measurement of Hepatic Fibrosis by Transient Elastography: A Narrative Review. Viruses 2023; 15:1730. [PMID: 37632072 PMCID: PMC10459581 DOI: 10.3390/v15081730] [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: 07/10/2023] [Revised: 08/04/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
Transient elastography by FibroScan® (Echosens, Paris, France) is a non-invasive method that can provide a reliable measurement of liver fibrosis through the evaluation of liver stiffness. Despite its limitations and risks, liver biopsy has thus far been the only procedure able to provide data to quantify fibrosis. Scientific evidence and clinical practice have made it possible to use FibroScan® in the diagnostic work-up of several liver diseases to monitor patients' long-term treatment response and for complication prevention. For these reasons, this procedure is widely used in clinical practice and is still being investigated for further applications. The aim of this narrative review is to provide a comprehensive overview of the main applications of transient elastography in the current clinical practice.
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Affiliation(s)
- Luca Rinaldi
- Department of Advanced Medical and Surgical Sciences, “Luigi Vanvitelli” University of Campania, 80131 Naples, Italy; (L.R.); (R.M.)
| | - Chiara Giorgione
- Department of Advanced Medical and Surgical Sciences, “Luigi Vanvitelli” University of Campania, 80131 Naples, Italy; (L.R.); (R.M.)
| | - Andrea Mormone
- Department of Advanced Medical and Surgical Sciences, “Luigi Vanvitelli” University of Campania, 80131 Naples, Italy; (L.R.); (R.M.)
| | - Francesca Esposito
- Department of Advanced Medical and Surgical Sciences, “Luigi Vanvitelli” University of Campania, 80131 Naples, Italy; (L.R.); (R.M.)
| | - Michele Rinaldi
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, “Federico II” University of Naples, 80131 Naples, Italy;
| | - Massimiliano Berretta
- Department of Clinical and Experimental Medicine, University of Messina, 98121 Messina, Italy;
| | - Raffaele Marfella
- Department of Advanced Medical and Surgical Sciences, “Luigi Vanvitelli” University of Campania, 80131 Naples, Italy; (L.R.); (R.M.)
| | - Ciro Romano
- Department of Advanced Medical and Surgical Sciences, “Luigi Vanvitelli” University of Campania, 80131 Naples, Italy; (L.R.); (R.M.)
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Scalco R, Hamsafar Y, White CL, Schneider JA, Reichard RR, Prokop S, Perrin RJ, Nelson PT, Mooney S, Lieberman AP, Kukull WA, Kofler J, Keene CD, Kapasi A, Irwin DJ, Gutman DA, Flanagan ME, Crary JF, Chan KC, Murray ME, Dugger BN. The status of digital pathology and associated infrastructure within Alzheimer's Disease Centers. J Neuropathol Exp Neurol 2023; 82:202-211. [PMID: 36692179 PMCID: PMC9941826 DOI: 10.1093/jnen/nlac127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Digital pathology (DP) has transformative potential, especially for Alzheimer disease and related disorders. However, infrastructure barriers may limit adoption. To provide benchmarks and insights into implementation barriers, a survey was conducted in 2019 within National Institutes of Health's Alzheimer's Disease Centers (ADCs). Questions covered infrastructure, funding sources, and data management related to digital pathology. Of the 35 ADCs to which the survey was sent, 33 responded. Most respondents (81%) stated that their ADC had digital slide scanner access, with the most frequent brand being Aperio/Leica (62.9%). Approximately a third of respondents stated there were fees to utilize the scanner. For DP and machine learning (ML) resources, 41% of respondents stated none was supported by their ADC. For scanner purchasing and operations, 50% of respondents stated they received institutional support. Some were unsure of the file size of scanned digital images (37%) and total amount of storage space files occupied (50%). Most (76%) were aware of other departments at their institution working with ML; a similar (76%) percentage were unaware of multiuniversity or industry partnerships. These results demonstrate many ADCs have access to a digital slide scanner; additional investigations are needed to further understand hurdles to implement DP and ML workflows.
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Affiliation(s)
- Rebeca Scalco
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
| | - Yamah Hamsafar
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
| | - Charles L White
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | | | | | - Stefan Prokop
- Department of Pathology, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, USA
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, USA
- Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, Saint Louis, Missouri, USA
| | | | - Sean Mooney
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Andrew P Lieberman
- Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Walter A Kukull
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Julia Kofler
- Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Christopher Dirk Keene
- Department Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
| | | | - David J Irwin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Gutman
- Departments of Neurology, Psychiatry, and Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Margaret E Flanagan
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - John F Crary
- Department of Pathology, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Neuroscience, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Artificial Intelligence & Human Health, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kwun C Chan
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Brittany N Dugger
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
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Theofilis P, Vordoni A, Kalaitzidis RG. Metabolic Dysfunction-Associated Fatty Liver Disease in the National Health and Nutrition Examination Survey 2017-2020: Epidemiology, Clinical Correlates, and the Role of Diagnostic Scores. Metabolites 2022; 12:1070. [PMID: 36355156 PMCID: PMC9697527 DOI: 10.3390/metabo12111070] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/10/2023] Open
Abstract
The recent establishment of metabolic dysfunction-associated fatty liver disease (MAFLD) has led to a reevaluation of its epidemiology, diagnosis, and clinical implications. In this study, we aimed to evaluate MAFLD's epidemiology and its association with other pathologic states and biomarkers, as well as to assess the prevalence of the different fibrosis stages in the MAFLD population, together with the importance of diagnostic scores in the preliminary determination of significant fibrosis. After analyzing the National Health and Nutrition Examination Survey (NHANES) 2017-2020, we found a high prevalence of MAFLD, at 58.6% of the studied population. MAFLD was accompanied by numerous comorbidities, which were increasingly common in individuals with higher grades of liver fibrosis. Fatty liver index emerged as a reliable indicator of MAFLD, as well as significant fibrosis. The estimation of fatty liver index could be a reasonable addition to the evaluation of patients with metabolic risk factors and could lead a diagnosis in the absence of liver elastography or biopsy. Further studies are needed to enhance our knowledge regarding its prognosis, as well as the role of novel therapies in its prevention or regression.
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Wagner T, Katou S, Wahl P, Vogt F, Kneifel F, Morgul H, Vogel T, Houben P, Becker F, Struecker B, Pascher A, Radunz S. Hyperspectral imaging for quantitative assessment of hepatic steatosis in human liver allografts. Clin Transplant 2022; 36:e14736. [PMID: 35622345 DOI: 10.1111/ctr.14736] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/25/2022] [Accepted: 05/01/2022] [Indexed: 11/24/2022]
Abstract
INTRODUCTION In liver transplantation (LT), steatosis is commonly judged to be a risk factor for graft dysfunction, and quantitative assessment of hepatic steatosis remains crucial. Liver biopsy as the gold standard for evaluation of hepatic steatosis has certain drawbacks, i.e. invasiveness, and intra- and inter-observer variability. A non-invasive, quantitative modality could replace liver biopsy and eliminate these disadvantages, but has not yet been evaluated in human LT. METHODS We performed a pilot study to evaluate the feasibility and accuracy of hyperspectral imaging (HSI) in the assessment of hepatic steatosis of human liver allografts for transplantation. Thirteen deceased donor liver allografts were included in the study. The degree of steatosis was assessed by means of conventional liver biopsy as well as HSI, performed at the end of backtable preparation, during normothermic machine perfusion (NMP), and after reperfusion in the recipient. RESULTS Organ donors were 51 [30-83] years old, and 61.5% were male. Donor body mass index was 24.2 [16.5-38.0] kg/m2. The tissue lipid index (TLI) generated by HSI at the end of back-table preparation correlated significantly with the histopathologically assessed degree of overall hepatic steatosis (R2 = 0.9085, p<0.0001); this was based on a correlation of TLI and microvesicular steatosis (R2 = 0.8120; p<0.0001). There is also a linear relationship between the histopathologically assessed degree of overall steatosis and TLI during NMP (R2 = 0.5646; p = 0.0031) as well as TLI after reperfusion (R2 = 0.6562; p = 0.0008). CONCLUSION HSI may safely be applied for accurate assessment of hepatic steatosis in human liver grafts. Certainly, TLI needs further assessment and validation in larger sample sizes. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Tristan Wagner
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Shadi Katou
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Philip Wahl
- Diaspective Vision GmbH, Am Salzhaff, Germany
| | - Franziska Vogt
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Felicia Kneifel
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Haluk Morgul
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Thomas Vogel
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Philipp Houben
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Felix Becker
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Benjamin Struecker
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Andreas Pascher
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Sonia Radunz
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
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