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Campbell JM, Gosnell M, Agha A, Handley S, Knab A, Anwer AG, Bhargava A, Goldys EM. Label-Free Assessment of Key Biological Autofluorophores: Material Characteristics and Opportunities for Clinical Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403761. [PMID: 38775184 DOI: 10.1002/adma.202403761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/04/2024] [Indexed: 06/13/2024]
Abstract
Autofluorophores are endogenous fluorescent compounds that naturally occur in the intra and extracellular spaces of all tissues and organs. Most have vital biological functions - like the metabolic cofactors NAD(P)H and FAD+, as well as the structural protein collagen. Others are considered to be waste products - like lipofuscin and advanced glycation end products - which accumulate with age and are associated with cellular dysfunction. Due to their natural fluorescence, these materials have great utility for enabling non-invasive, label-free assays with direct ties to biological function. Numerous technologies, with different advantages and drawbacks, are applied to their assessment, including fluorescence lifetime imaging microscopy, hyperspectral microscopy, and flow cytometry. Here, the applications of label-free autofluorophore assessment are reviewed for clinical and health-research applications, with specific attention to biomaterials, disease detection, surgical guidance, treatment monitoring, and tissue assessment - fields that greatly benefit from non-invasive methodologies capable of continuous, in vivo characterization.
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Affiliation(s)
- Jared M Campbell
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, 2033, Australia
| | | | - Adnan Agha
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, 2033, Australia
| | - Shannon Handley
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, 2033, Australia
| | - Aline Knab
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, 2033, Australia
| | - Ayad G Anwer
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, 2033, Australia
| | - Akanksha Bhargava
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, 2033, Australia
| | - Ewa M Goldys
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, 2033, Australia
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Xu S, Dawuti W, Maimaitiaili M, Dou J, Aizezi M, Aimulajiang K, Lü X, Lü G. Rapid and non-invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms. JOURNAL OF BIOPHOTONICS 2024; 17:e202300357. [PMID: 38263544 DOI: 10.1002/jbio.202300357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/15/2023] [Accepted: 12/14/2023] [Indexed: 01/25/2024]
Abstract
Cystic echinococcosis (CE) is a grievous zoonotic parasitic disease. Currently, the traditional technology of screening CE is laborious and expensive, developing an innovative technology is urgent. In this study, we combined serum fluorescence spectroscopy with machine learning algorithms to develop an innovative screening technique to diagnose CE in sheep. Serum fluorescence spectra of Echinococcus granulosus sensu stricto-infected group (n = 63) and uninfected E. granulosus s.s. group (n = 60) under excitation at 405 nm were recorded. The linear support vector machine (Linear SVM), Quadratic SVM, medium radial basis function (RBF) SVM, K-nearest neighbor (KNN), and principal component analysis-linear discriminant analysis (PCA-LDA) were used to analyze the spectra data. The results showed that Quadratic SVM had the great classification capacity, its sensitivity, specificity, and accuracy were 85.0%, 93.8%, and 88.9%, respectively. In short, serum fluorescence spectroscopy combined with Quadratic SVM algorithm has great potential in the innovative diagnosis of CE in sheep.
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Affiliation(s)
- Shengke Xu
- College of Life Sciences and Technology, Xinjiang University, Urumqi, Xinjiang, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
- Xinjiang Key Laboratory of Echinococcosis, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Wubulitalifu Dawuti
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Maierhaba Maimaitiaili
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
- Xinjiang Key Laboratory of Echinococcosis, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Jingrui Dou
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Malike Aizezi
- Animal Health Supervision Institute of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, PR China
| | - Kalibixiati Aimulajiang
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
- Xinjiang Key Laboratory of Echinococcosis, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xiaoyi Lü
- College of Software, Xinjiang University, Urumqi, Xinjiang, China
| | - Guodong Lü
- College of Life Sciences and Technology, Xinjiang University, Urumqi, Xinjiang, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
- Xinjiang Key Laboratory of Echinococcosis, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
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