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Footner E, Firipis K, Liu E, Baker C, Foley P, Kapsa RMI, Pirogova E, O'Connell C, Quigley A. Layer-by-Layer Analysis of In Vitro Skin Models. ACS Biomater Sci Eng 2023; 9:5933-5952. [PMID: 37791888 DOI: 10.1021/acsbiomaterials.3c00283] [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] [Indexed: 10/05/2023]
Abstract
In vitro human skin models are evolving into versatile platforms for the study of skin biology and disorders. These models have many potential applications in the fields of drug testing and safety assessment, as well as cosmetic and new treatment development. The development of in vitro skin models that accurately mimic native human skin can reduce reliance on animal models and also allow for more precise, clinically relevant testing. Recent advances in biofabrication techniques and biomaterials have led to the creation of increasingly complex, multilayered skin models that incorporate important functional components of skin, such as the skin barrier, mechanical properties, pigmentation, vasculature, hair follicles, glands, and subcutaneous layer. This improved ability to recapitulate the functional aspects of native skin enhances the ability to model the behavior and response of native human skin, as the complex interplay of cell-to-cell and cell-to-material interactions are incorporated. In this review, we summarize the recent developments in in vitro skin models, with a focus on their applications, limitations, and future directions.
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Affiliation(s)
- Elizabeth Footner
- Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
- Aikenhead Centre for Medical Discovery, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Kate Firipis
- Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
- Aikenhead Centre for Medical Discovery, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Emily Liu
- Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
- Aikenhead Centre for Medical Discovery, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Chris Baker
- Department of Dermatology, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
- Skin Health Institute, Carlton, VIC 3053, Australia
- Department of Medicine, University of Melbourne, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Peter Foley
- Department of Dermatology, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
- Skin Health Institute, Carlton, VIC 3053, Australia
- Department of Medicine, University of Melbourne, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Robert M I Kapsa
- Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
- Aikenhead Centre for Medical Discovery, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
- Department of Medicine, University of Melbourne, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
- Centre for Clinical Neurosciences and Neurological Research, St. Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Elena Pirogova
- Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
- Aikenhead Centre for Medical Discovery, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Cathal O'Connell
- Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
- Aikenhead Centre for Medical Discovery, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Anita Quigley
- Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
- Aikenhead Centre for Medical Discovery, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
- Department of Medicine, University of Melbourne, St Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
- Centre for Clinical Neurosciences and Neurological Research, St. Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
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Saknite I, Kwun S, Zhang K, Hood A, Chen F, Kangas L, Kortteisto P, Kukkonen A, Spigulis J, Tkaczyk ER. Hyperspectral imaging to accurately segment skin erythema and hyperpigmentation in cutaneous chronic graft-versus-host disease. JOURNAL OF BIOPHOTONICS 2023; 16:e202300009. [PMID: 36942511 DOI: 10.1002/jbio.202300009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/15/2023] [Accepted: 03/17/2023] [Indexed: 06/18/2023]
Abstract
In 51 lesions from 15 patients with the inflammatory skin condition chronic graft-versus-host-disease, hyperspectral imaging accurately delineated active erythema and post-inflammatory hyperpigmentation. The method was validated by dermatologist-approved confident delineations of only definitely affected and definitely unaffected areas in photographs. A prototype hyperspectral imaging system acquired a 2.5 × 3.5 cm2 area of skin at 120 wavelengths in the 450-850 nm range. Unsupervised extraction of unknown absorbers by endmember analysis achieved a comparable accuracy to that of supervised extraction of known absorbers (melanin, hemoglobin) by chromophore mapping: 0.78 (IQR: 0.39-0.85) vs. 0.83 (0.53-0.91) to delineate erythema and 0.74 (0.57-0.87) vs. 0.73 (0.52-0.84) to delineate hyperpigmentation. Both algorithms achieved higher specificity than sensitivity. Whereas a trained human confidently marked a median of 7% of image pixels, unsupervised and supervised algorithms delineated a median of 14% and 27% pixels. Hyperspectral imaging could overcome a fundamental practice gap of distinguishing active from inactive manifestations of inflammatory skin disease.
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Affiliation(s)
- Inga Saknite
- Vanderbilt Dermatology Translational Research Clinic, Department of Dermatology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Biophotonics Laboratory, Institute of Atomic Physics and Spectroscopy, University of Latvia, Riga, Latvia
| | - Shinwho Kwun
- Vanderbilt Dermatology Translational Research Clinic, Department of Dermatology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kathy Zhang
- Vanderbilt Dermatology Translational Research Clinic, Department of Dermatology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Alexis Hood
- Vanderbilt Dermatology Translational Research Clinic, Department of Dermatology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Fuyao Chen
- Vanderbilt Dermatology Translational Research Clinic, Department of Dermatology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | | | | | - Janis Spigulis
- Biophotonics Laboratory, Institute of Atomic Physics and Spectroscopy, University of Latvia, Riga, Latvia
| | - Eric R Tkaczyk
- Vanderbilt Dermatology Translational Research Clinic, Department of Dermatology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt-Ingram Cancer Center, Nashville, Tennessee, USA
- Dermatology Service and Research Service, Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, Tennessee, USA
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Kallepalli A, Halls J, James DB, Richardson MA. An ultrasonography-based approach for tissue modelling to inform photo-therapy treatment strategies. JOURNAL OF BIOPHOTONICS 2022; 15:e202100275. [PMID: 35044094 DOI: 10.1002/jbio.202100275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 06/14/2023]
Abstract
Currently, diagnostic medicine uses a multitude of tools ranging from ionising radiation to histology analysis. With advances in piezoelectric crystal technology, high-frequency ultrasound imaging has developed to achieve comparatively high resolution without the drawbacks of ionising radiation. This research proposes a low-cost, non-invasive and real-time protocol for informing photo-therapy procedures using ultrasound imaging. We combine currently available ultrasound procedures with Monte Carlo methods for assessing light transport and photo-energy deposition in the tissue. The measurements from high-resolution ultrasound scans are used as input for optical simulations. Consequently, this provides a pipeline that will inform medical practitioners for better therapy strategy planning. While validating known inferences of light transport through biological tissue, our results highlight the range of information such as temporal monitoring and energy deposition at varying depths. This process also retains the flexibility of testing various wavelengths for individual-specific geometries and anatomy.
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Affiliation(s)
- Akhil Kallepalli
- School of Physics and Astronomy, University of Glasgow, Glasgow, UK
| | - James Halls
- Department of Radiology, The Great Western Hospital, Swindon, UK
| | - David B James
- Centre for Electronic Warfare, Information and Cyber, Cranfield University, Defence Academy of the UK, Shrivenham, UK
| | - Mark A Richardson
- Centre for Electronic Warfare, Information and Cyber, Cranfield University, Defence Academy of the UK, Shrivenham, UK
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Long H, Chen B, Li W, Xian Y, Peng Z. Blood glucose detection based on Teager-Kaiser main energy of photoacoustic signal. Comput Biol Med 2021; 134:104552. [PMID: 34144363 DOI: 10.1016/j.compbiomed.2021.104552] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 06/02/2021] [Accepted: 06/02/2021] [Indexed: 11/27/2022]
Abstract
Real-time blood glucose detection is an essential tool for diabetes monitoring. Non-invasive blood glucose detection technology is one of the current research hotspots in this field. Previous research mainly focused on improving the system's detection capability to obtain signals with low signal-to-noise ratio and high quality, and simple methods are often used in signal processing. Moreover, photoacoustic signal simulation also simplifies the influence of the transmission medium on the signal. In the present study, we built a new simulation model which considers human skin, blood, and the detector's limitations, to obtain a more practical photoacoustic signal. We then proposed a blood glucose detection algorithm based on Teager-Kaiser main energy (TKME) to overcome noise and medium interference and achieve a high detection accuracy at low SNR. Finally, the simulation and actual data were utilised during the experiment, and the detection error was 15 mg/dL (SNR = 10 dB).
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Affiliation(s)
- Hongfeng Long
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China; Laboratory of Imaging Detection and Intelligent Perception University of Electronic Science and Technology of China, 610054, Chengdu, China
| | - Bingzhang Chen
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China; Laboratory of Imaging Detection and Intelligent Perception University of Electronic Science and Technology of China, 610054, Chengdu, China.
| | - Wei Li
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China; Laboratory of Imaging Detection and Intelligent Perception University of Electronic Science and Technology of China, 610054, Chengdu, China
| | - Yongli Xian
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Electronic Engineering and Electronic Information, Xihua University, Chengdu, 610039, China
| | - Zhenming Peng
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China; Laboratory of Imaging Detection and Intelligent Perception University of Electronic Science and Technology of China, 610054, Chengdu, China.
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