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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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Kaniyala Melanthota S, Kistenev YV, Borisova E, Ivanov D, Zakharova O, Boyko A, Vrazhnov D, Gopal D, Chakrabarti S, K SP, Mazumder N. Types of spectroscopy and microscopy techniques for cancer diagnosis: a review. Lasers Med Sci 2022; 37:3067-3084. [PMID: 35834141 PMCID: PMC9525344 DOI: 10.1007/s10103-022-03610-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 07/05/2022] [Indexed: 11/25/2022]
Abstract
Cancer is a life-threatening disease that has claimed the lives of many people worldwide. With the current diagnostic methods, it is hard to determine cancer at an early stage, due to its versatile nature and lack of genomic biomarkers. The rapid development of biophotonics has emerged as a potential tool in cancer detection and diagnosis. Using the fluorescence, scattering, and absorption characteristics of cells and tissues, it is possible to detect cancer at an early stage. The diagnostic techniques addressed in this review are highly sensitive to the chemical and morphological changes in the cell and tissue during disease progression. These changes alter the fluorescence signal of the cell/tissue and are detected using spectroscopy and microscopy techniques including confocal and two-photon fluorescence (TPF). Further, second harmonic generation (SHG) microscopy reveals the morphological changes that occurred in non-centrosymmetric structures in the tissue, such as collagen. Again, Raman spectroscopy is a non-destructive method that provides a fingerprinting technique to differentiate benign and malignant tissue based on Raman signal. Photoacoustic microscopy and spectroscopy of tissue allow molecule-specific detection with high spatial resolution and penetration depth. In addition, terahertz spectroscopic studies reveal the variation of tissue water content during disease progression. In this review, we address the applications of spectroscopic and microscopic techniques for cancer detection based on the optical properties of the tissue. The discussed state-of-the-art techniques successfully determines malignancy to its rapid diagnosis.
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Affiliation(s)
- Sindhoora Kaniyala Melanthota
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, 576104, Manipal, India
| | - Yury V Kistenev
- Laboratory of Biophotonics, Tomsk State University, Tomsk, 634050, Russia
- Central Research Laboratory, Siberian State Medical University, Tomsk, 634050, Russia
| | - Ekaterina Borisova
- Laboratory of Biophotonics, Institute of Electronics, Bulgarian Academy of Sciences, Tsarigradsko Chaussee Blvd, 72, 1784, Sofia, Bulgaria.
- Biology Faculty, Saratov State University, 83, Astrakhanskaya Str, 410012, Saratov, Russia.
| | - Deyan Ivanov
- Laboratory of Biophotonics, Institute of Electronics, Bulgarian Academy of Sciences, Tsarigradsko Chaussee Blvd, 72, 1784, Sofia, Bulgaria
| | - Olga Zakharova
- Laboratory of Biophotonics, Tomsk State University, Tomsk, 634050, Russia
| | - Andrey Boyko
- Laboratory of Biophotonics, Tomsk State University, Tomsk, 634050, Russia
| | - Denis Vrazhnov
- Laboratory of Biophotonics, Tomsk State University, Tomsk, 634050, Russia
| | - Dharshini Gopal
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, 576104, Manipal, India
| | - Shweta Chakrabarti
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, 576104, Manipal, India
| | - Shama Prasada K
- Department of Cell and Molecular Biology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, 576104, Manipal, India
| | - Nirmal Mazumder
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, 576104, Manipal, India.
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Applying particle swarm optimization-based decision tree classifier for wart treatment selection. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-021-00348-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractWart is a disease caused by human papillomavirus with common and plantar warts as general forms. Commonly used methods to treat warts are immunotherapy and cryotherapy. The selection of proper treatment is vital to cure warts. This paper establishes a classification and regression tree (CART) model based on particle swarm optimisation to help patients choose between immunotherapy and cryotherapy. The proposed model can accurately predict the response of patients to the two methods. Using an improved particle swarm algorithm (PSO) to optimise the parameters of the model instead of the traditional pruning algorithm, a more concise and more accurate model is obtained. Two experiments are conducted to verify the feasibility of the proposed model. On the hand, five benchmarks are used to verify the performance of the improved PSO algorithm. On the other hand, the experiment on two wart datasets is conducted. Results show that the proposed model is effective. The proposed method classifies better than k-nearest neighbour, C4.5 and logistic regression. It also performs better than the conventional optimisation method for the CART algorithm. Moreover, the decision tree model established in this study is interpretable and understandable. Therefore, the proposed model can help patients and doctors reduce the medical cost and improve the quality of healing operation.
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Sdobnov AY, Lademann J, Darvin ME, Tuchin VV. Methods for Optical Skin Clearing in Molecular Optical Imaging in Dermatology. BIOCHEMISTRY (MOSCOW) 2019; 84:S144-S158. [PMID: 31213200 DOI: 10.1134/s0006297919140098] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
This short review describes recent progress in using optical clearing (OC) technique in skin studies. Optical clearing is an efficient tool for enhancing the probing depth and data quality in multiphoton microscopy and Raman spectroscopy. Here, we discuss the main mechanisms of OC, its safety, advantages, and limitations. The data on the OC effect on the skin water content are presented. It was demonstrated that 70% glycerol and 100% OmnipaqueTM 300 reduce the water content in the skin. Both OC agents (OCAs) significantly affect the strongly bound and weakly bound water. However, OmnipaqueTM 300 causes considerably less skin dehydration than glycerol. In addition, the results of examination of the OC effect on autofluorescence in two-photon excitation and background fluorescence in Raman scattering at different skin depths are presented. It is shown that OmnipaqueTM 300 is a promising OCA due to its ability to reduce background fluorescence in the upper skin layers. The possibility of multimodal imaging combining optical methods and OC technique is discussed.
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Affiliation(s)
- A Yu Sdobnov
- Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, 90570, Finland. .,Research-Educational Institute of Optics and Biophotonics, Saratov State University, Saratov, 410012, Russia
| | - J Lademann
- Center of Experimental and Applied Cutaneous Physiology, Department of Dermatology, Venerology and Allergology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, 10117, Germany
| | - M E Darvin
- Center of Experimental and Applied Cutaneous Physiology, Department of Dermatology, Venerology and Allergology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, 10117, Germany
| | - V V Tuchin
- Research-Educational Institute of Optics and Biophotonics, Saratov State University, Saratov, 410012, Russia.,Laboratory of Laser Diagnostics of Technical and Living Systems, Institute of Precision Mechanics and Control, Russian Academy of Sciences, Saratov, 410028, Russia.,Interdisciplinary Laboratory of Biophotonics, Tomsk State University, Tomsk, 634050, Russia.,Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Moscow, 119071, Russia
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Magalhaes C, Mendes J, Vardasca R. The role of AI classifiers in skin cancer images. Skin Res Technol 2019; 25:750-757. [DOI: 10.1111/srt.12713] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 04/28/2019] [Indexed: 01/29/2023]
Affiliation(s)
- Carolina Magalhaes
- INEGI‐LAETA Faculdade de Engenharia Universidade do Porto Porto Portugal
| | - Joaquim Mendes
- INEGI‐LAETA Faculdade de Engenharia Universidade do Porto Porto Portugal
| | - Ricardo Vardasca
- INEGI‐LAETA Faculdade de Engenharia Universidade do Porto Porto Portugal
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Fluorescence spectroscopy in the visible range for the assessment of UVB radiation effects in hairless mice skin. Photodiagnosis Photodyn Ther 2017; 20:21-27. [DOI: 10.1016/j.pdpdt.2017.08.016] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 08/02/2017] [Accepted: 08/27/2017] [Indexed: 02/06/2023]
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