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Jeong SW, Lyu JI, Jeong H, Baek J, Moon JK, Lee C, Choi MG, Kim KH, Park YI. SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits. PLANT CELL REPORTS 2024; 43:164. [PMID: 38852113 DOI: 10.1007/s00299-024-03249-0] [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: 04/03/2024] [Accepted: 05/06/2024] [Indexed: 06/10/2024]
Abstract
KEY MESSAGE Hyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes. Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis.
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Affiliation(s)
- Seok Won Jeong
- Biological Sciences, Chungnam National University, 99 Daehagro, Youseong, Daejon, 34134, Korea
| | - Jae Il Lyu
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - HwangWeon Jeong
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - Jeongho Baek
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - Jung-Kyung Moon
- Crop Foundation Research Division, National Institute of Crop Sciences, 181 Hyeoksinro, Wanju, Jeollabuk-do, 55365, Korea
| | - Chaewon Lee
- Crop Cultivation and Environment Research Division, National Institute of Crop Sciences, 54 Seohoro, Suwon, Kyounggi-do, 16613, Korea
| | - Myoung-Goo Choi
- Wheat Research Team, National Institute of Crop Sciences, RDA, 181 Hyeoksinro, Wanju, Jeollabuk-do, 55365, Korea
| | - Kyoung-Hwan Kim
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - Youn-Il Park
- Biological Sciences, Chungnam National University, 99 Daehagro, Youseong, Daejon, 34134, Korea.
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Zhu J, Xia H, Xu X, Zheng R, Liu C, Hong J, Huang Q. FTIR spectroscopy for assessment of hair from lung cancer patients and its application in monitoring the chemotherapy treatment effect. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 314:124185. [PMID: 38565049 DOI: 10.1016/j.saa.2024.124185] [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: 12/28/2023] [Revised: 03/08/2024] [Accepted: 03/22/2024] [Indexed: 04/04/2024]
Abstract
Lung cancer is the most common cancer and the leading cause of death in China. The current gold standard for clinical lung cancer diagnosis is based on histopathological examination of tumors, but it has the limitation for easy operation and convenient applications. Therefore, researchers are still striving to develop other tools and methods for non-invasive and rapid assessment of the health conditions of lung cancer patients. Hair, as a reflection of the metabolism of the body, is closely related to human health conditions. In principle, Fourier-transform infrared (FTIR) spectroscopy can probe the major chemical compositions in the hair. However, as indicated by previous studies, there is still the challenge to make good use of FTIR spectroscopy for achieving reliable analysis of hair from cancer patients. In this study, hair samples from 82 lung cancer patients were collected and subjected to FTIR measurements and analysis, which showed the protein content in the hair is closely related to the protein content in the blood serum of patients, and the contents of protein and lipid are statistically lower in the lung cancer patients. Furthermore, we demonstrated that FTIR spectroscopy could be employed to monitor the hair of lung cancer patients undergoing chemotherapy, and confirmed that the FTIR spectra of the hair may reflect the resultant effect of the chemotherapy. As such, this work validates the way of using FTIR spectroscopy in hair analysis for the assistance of medical diagnosis of lung cancer as well as monitoring the conditions of the patients under the medical treatment.
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Affiliation(s)
- Jianxia Zhu
- School of Nursing, Anhui Medical University, Hefei, Anhui 230032, China; CAS Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China
| | - Haiqian Xia
- School of Nursing, Anhui Medical University, Hefei, Anhui 230032, China; CAS Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China
| | - Xiuzhi Xu
- School of Nursing, Anhui Medical University, Hefei, Anhui 230032, China
| | - Rong Zheng
- School of Nursing, Anhui Medical University, Hefei, Anhui 230032, China
| | - Chao Liu
- CAS Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China; Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, Anhui 230032, China
| | - Jingfang Hong
- School of Nursing, Anhui Medical University, Hefei, Anhui 230032, China.
| | - Qing Huang
- School of Nursing, Anhui Medical University, Hefei, Anhui 230032, China; CAS Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China; Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, Anhui 230032, China.
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Plazas D, Ferranti F, Liu Q, Lotfi Choobbari M, Ottevaere H. A Study of High-Frequency Noise for Microplastics Classification Using Raman Spectroscopy and Machine Learning. APPLIED SPECTROSCOPY 2024; 78:567-578. [PMID: 38465603 DOI: 10.1177/00037028241233304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Given the growing urge for plastic management and regulation in the world, recent studies have investigated the problem of plastic material identification for correct classification and disposal. Recent works have shown the potential of machine learning techniques for successful microplastics classification using Raman signals. Classification techniques from the machine learning area allow the identification of the type of microplastic from optical signals based on Raman spectroscopy. In this paper, we investigate the impact of high-frequency noise on the performance of related classification tasks. It is well-known that classification based on Raman is highly dependent on peak visibility, but it is also known that signal smoothing is a common step in the pre-processing of the measured signals. This raises a potential trade-off between high-frequency noise and peak preservation that depends on user-defined parameters. The results obtained in this work suggest that a linear discriminant analysis model cannot generalize properly in the presence of noisy signals, whereas an error-correcting output codes model is better suited to account for inherent noise. Moreover, principal components analysis (PCA) can become a must-do step for robust classification models, given its simplicity and natural smoothing capabilities. Our study on the high-frequency noise, the possible trade-off between pre-processing the high-frequency noise and the peak visibility, and the use of PCA as a noise reduction technique in addition to its dimensionality reduction functionality are the fundamental aspects of this work.
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Affiliation(s)
- David Plazas
- School of Applied Sciences and Engineering, Universidad EAFIT, Medellín, Colombia
- Brussels Photonics, Department of Applied Physics and Photonics, Vrije Universiteit Brussel, Brussels, Belgium
| | - Francesco Ferranti
- Brussels Photonics, Department of Applied Physics and Photonics, Vrije Universiteit Brussel and Flanders Make, Brussels, Belgium
| | - Qing Liu
- Brussels Photonics, Department of Applied Physics and Photonics, Vrije Universiteit Brussel and Flanders Make, Brussels, Belgium
| | - Mehrdad Lotfi Choobbari
- Brussels Photonics, Department of Applied Physics and Photonics, Vrije Universiteit Brussel, Brussels, Belgium
| | - Heidi Ottevaere
- Brussels Photonics, Department of Applied Physics and Photonics, Vrije Universiteit Brussel and Flanders Make, Brussels, Belgium
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Shuai W, Wu X, Chen C, Zuo E, Chen X, Li Z, Lv X, Wu L, Chen C. Rapid diagnosis of rheumatoid arthritis and ankylosing spondylitis based on Fourier transform infrared spectroscopy and deep learning. Photodiagnosis Photodyn Ther 2024; 45:103885. [PMID: 37931694 DOI: 10.1016/j.pdpdt.2023.103885] [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: 09/13/2023] [Revised: 09/26/2023] [Accepted: 11/03/2023] [Indexed: 11/08/2023]
Abstract
OBJECTIVE Rheumatoid arthritis and Ankylosing spondylitis are two common autoimmune inflammatory rheumatic diseases that negatively affect activities of daily living and can lead to structural and functional disability, reduced quality of life. Here, this study utilized Fourier transform infrared (FTIR) spectroscopy on dried serum samples and achieved early diagnosis of rheumatoid arthritis and ankylosing spondylitis based on deep learning models. METHOD A total of 243 dried serum samples were collected in this study, including 81 samples each from ankylosing spondylitis, rheumatoid arthritis, and healthy controls. Three multi-scale convolutional modules with different specifications were designed based on the multi-scale convolutional neural network (MSCNN) to effectively fuse the local features to enhance the generalization ability of the model. The FTIR was then combined with the MSCNN model to achieve a non-invasive, fast, and accurate diagnosis of ankylosing spondylitis, rheumatoid arthritis, and healthy controls. RESULTS Spectral analysis shows that the curves and waveforms of the three spectral graphs are similar. The main differences are distributed in the spectral regions of 3300-3250 cm-1, 3000-2800 cm-1, 1750-1500 cm-1, and 1500-1300 cm-1, which represent: Amides, fatty acids, cholesterol, proteins with a carboxyl group, amide II, free amino acids, and polysaccharides. Four classification models, namely artificial neural network (ANN), convolutional neural network (CNN), improved AlexNet model, and multi-scale convolutional neural network (MSCNN) were established. Through comparison, it was found that the diagnostic AUC value of the MSCNN model was 0.99, and the accuracy rate was as high as 0.93, which was much higher than the other three models. CONCLUSION The study demonstrated the superiority of MSCNN in distinguishing ankylosing spondylitis from rheumatoid arthritis and healthy controls. FTIR may become a rapid, sensitive, and non-invasive means of diagnosing rheumatism.
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Affiliation(s)
- Wei Shuai
- College of Software, Xinjiang University, Urumqi, China
| | - Xue Wu
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China; Xinjiang Clinical Research Center for Rheumatoid arthritis, Urumqi, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Enguang Zuo
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Xiaomei Chen
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China; Xinjiang Clinical Research Center for Rheumatoid arthritis, Urumqi, China
| | - Zhengfang Li
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China; Xinjiang Clinical Research Center for Rheumatoid arthritis, Urumqi, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, China; Key Laboratory of signal detection and processing, Xinjiang University, Urumqi, China
| | - Lijun Wu
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China; Xinjiang Clinical Research Center for Rheumatoid arthritis, Urumqi, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, China.
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Zelger P, Brunner A, Zelger B, Willenbacher E, Unterberger SH, Stalder R, Huck CW, Willenbacher W, Pallua JD. Deep learning analysis of mid-infrared microscopic imaging data for the diagnosis and classification of human lymphomas. JOURNAL OF BIOPHOTONICS 2023; 16:e202300015. [PMID: 37578837 DOI: 10.1002/jbio.202300015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 07/19/2023] [Accepted: 08/09/2023] [Indexed: 08/15/2023]
Abstract
The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 to 850 cm-1 . The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data.
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Affiliation(s)
- P Zelger
- University Hospital of Hearing, Voice and Speech Disorders, Medical University of Innsbruck, Innsbruck, Austria
| | - A Brunner
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Austria
| | - B Zelger
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Austria
| | - E Willenbacher
- University Hospital of Internal Medicine V, Hematology & Oncology, Medical University of Innsbruck, Innsbruck, Austria
| | - S H Unterberger
- Institute of Material-Technology, Leopold-Franzens University Innsbruck, Innsbruck, Austria
| | - R Stalder
- Institute of Mineralogy and Petrography, Leopold-Franzens University Innsbruck, Innsbruck, Austria
| | - C W Huck
- Institute of Analytical Chemistry and Radiochemistry, Innsbruck, Austria
| | - W Willenbacher
- University Hospital of Internal Medicine V, Hematology & Oncology, Medical University of Innsbruck, Innsbruck, Austria
- Oncotyrol, Centre for Personalized Cancer Medicine, Innsbruck, Austria
| | - J D Pallua
- University Hospital for Orthopedics and Traumatology, Medical University of Innsbruck, Innsbruck, Austria
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Müller D, Schuhmacher D, Schörner S, Großerueschkamp F, Tischoff I, Tannapfel A, Reinacher-Schick A, Gerwert K, Mosig A. Dimensionality reduction for deep learning in infrared microscopy: a comparative computational survey. Analyst 2023; 148:5022-5032. [PMID: 37702617 DOI: 10.1039/d3an00166k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
While infrared microscopy provides molecular information at spatial resolution in a label-free manner, exploiting both spatial and molecular information for classifying the disease status of tissue samples constitutes a major challenge. One strategy to mitigate this problem is to embed high-dimensional pixel spectra in lower dimensions, aiming to preserve molecular information in a more compact manner, which reduces the amount of data and promises to make subsequent disease classification more accessible for machine learning procedures. In this study, we compare several dimensionality reduction approaches and their effect on identifying cancer in the context of a colon carcinoma study. We observe surprisingly small differences between convolutional neural networks trained on dimensionality reduced spectra compared to utilizing full spectra, indicating a clear tendency of the convolutional networks to focus on spatial rather than spectral information for classifying disease status.
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Affiliation(s)
- Dajana Müller
- Ruhr University Bochum, Center for Protein Diagnostics, Bochum, 44801, Germany.
- Ruhr University Bochum, Faculty of Biology and Biotechnology, Bioinformatics Group, 44801, Germany
| | - David Schuhmacher
- Ruhr University Bochum, Center for Protein Diagnostics, Bochum, 44801, Germany.
- Ruhr University Bochum, Faculty of Biology and Biotechnology, Bioinformatics Group, 44801, Germany
| | - Stephanie Schörner
- Ruhr University Bochum, Center for Protein Diagnostics, Bochum, 44801, Germany.
- Ruhr University Bochum, Faculty of Biology and Biotechnology, Department of Biophysics, 44801, Germany
| | - Frederik Großerueschkamp
- Ruhr University Bochum, Center for Protein Diagnostics, Bochum, 44801, Germany.
- Ruhr University Bochum, Faculty of Biology and Biotechnology, Department of Biophysics, 44801, Germany
| | - Iris Tischoff
- Institute of Pathology, Ruhr-University Bochum, 44789 Bochum, Germany
| | - Andrea Tannapfel
- Ruhr University Bochum, Center for Protein Diagnostics, Bochum, 44801, Germany.
- Institute of Pathology, Ruhr-University Bochum, 44789 Bochum, Germany
| | - Anke Reinacher-Schick
- Ruhr University Bochum, Center for Protein Diagnostics, Bochum, 44801, Germany.
- Department of Hematology, Oncology and Palliative Care, Ruhr-University Bochum, Bochum, Germany
| | - Klaus Gerwert
- Ruhr University Bochum, Center for Protein Diagnostics, Bochum, 44801, Germany.
- Ruhr University Bochum, Faculty of Biology and Biotechnology, Department of Biophysics, 44801, Germany
| | - Axel Mosig
- Ruhr University Bochum, Center for Protein Diagnostics, Bochum, 44801, Germany.
- Ruhr University Bochum, Faculty of Biology and Biotechnology, Bioinformatics Group, 44801, Germany
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Dou J, Dawuti W, Li J, Zhao H, Zhou R, Zhou J, Lin R, Lü G. Rapid detection of serological biomarkers in gallbladder carcinoma using fourier transform infrared spectroscopy combined with machine learning. Talanta 2023; 259:124457. [PMID: 36989965 DOI: 10.1016/j.talanta.2023.124457] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/10/2023] [Accepted: 03/12/2023] [Indexed: 03/29/2023]
Abstract
Gallbladder cancer (GBC) is the most common malignant tumour of the biliary tract. GBC is difficult to diagnose and treat at an early stage because of the lack of effective serum markers and typical symptoms, resulting in low survival rates. This study aimed to investigate the applicability of dried serum Fourier-transform infrared (FTIR) spectroscopy combined with machine learning algorithms to correctly differentiate patients with GBC from patients with gallbladder disease (GBD), cholangiocarcinoma (CCA), hepatocellular carcinoma (HCC) and healthy individuals. The differentiation between healthy individuals and GBC serum was better using principal component analysis (PCA) and linear discriminant analysis (LDA) for six spectral regions, especially in the protein (1710-1475 cm-1) and combined (1710-1475 + 1354-980 cm-1) region. However, the PCA-LDA model poorly differentiated GBC from GBD, CCA, and HCC in serum spectra. We evaluated the PCA- LDA, PCA-support vector machine (SVM), and radial basis kernel function support vector machine (RBF-SVM) models for GBC diagnosis and found that the RBF-SVM model performed the best, with 88.24-95% accuracy, 95.83% sensitivity, and 78.38-94.44% specificity in the 1710-1475 + 1354-980 cm-1 region. This study demonstrated that serum FTIR spectroscopy combined with the RBF-SVM algorithm has great clinical potential for GBC screening.
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Zhang S, Qi Y, Tan SPH, Bi R, Olivo M. Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review. BIOSENSORS 2023; 13:bios13050557. [PMID: 37232918 DOI: 10.3390/bios13050557] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/12/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.
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Affiliation(s)
- Shuyan Zhang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
| | - Yi Qi
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
| | - Sonia Peng Hwee Tan
- Department of Biomedical Engineering, National University of Singapore (NUS), 4 Engineering Drive 3 Block 4, #04-08, Singapore 117583, Singapore
| | - Renzhe Bi
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
| | - Malini Olivo
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
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Dou J, Dawuti W, Zheng X, Zhu Y, Lin R, Lü G, Zhang Y. Rapid discrimination of Brucellosis in sheep using serum Fourier transform infrared spectroscopy combined with PCA-LDA algorithm. Photodiagnosis Photodyn Ther 2023; 42:103567. [PMID: 37084931 DOI: 10.1016/j.pdpdt.2023.103567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/09/2023] [Accepted: 04/11/2023] [Indexed: 04/23/2023]
Abstract
Brucellosis in sheep is an infectious disease caused by Brucella melitensis in sheep. The current conventional serological methods for screening Brucella-infected sheep have the disadvantage of time consuming and low accuracy, so a simple, rapid and highly accurate screening method is needed. The aim of this study was to evaluate the feasibility of diagnosing Brucella-infected sheep by serum samples based on the Fourier transform infrared (FTIR) spectroscopy. In this study, FTIR spectroscopy of serum from Brucella-infected sheep (n=102) and healthy sheep (n=125) revealed abnormal protein and lipid metabolism in serum from Brucella-infected sheep compared to healthy sheep. Principal component analysis-Linear discriminant analysis (PCA-LDA) method was used to differentiate the FTIR spectra of serum from Brucella-infected sheep and healthy sheep in the protein band (3700-3090 cm-1) and lipid band (3000-2800 cm-1), and its overall diagnostic accuracy was 100% (sensitivity 100%, specificity 100%). In conclusion, our results suggest that serum FTIR spectroscopy combined with PCA-LDA algorithm has great potential for brucellosis in sheep screening.
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Affiliation(s)
- Jingrui Dou
- School of Public Health, Xinjiang Medical University, Urumqi 830054, 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 830054, China
| | - Wubulitalifu Dawuti
- School of Public Health, Xinjiang Medical University, Urumqi 830054, 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 830054, China
| | - Xiangxiang Zheng
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Yousen Zhu
- Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi Xinjiang 830054, China
| | - Renyong Lin
- 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 830054, China
| | - Guodong Lü
- School of Public Health, Xinjiang Medical University, Urumqi 830054, 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 830054, China.
| | - Yujiang Zhang
- School of Public Health, Xinjiang Medical University, Urumqi 830054, China; The Center for Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi 830002, China.
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Khan RS, Malik H. Diagnostic Biomarkers for Gestational Diabetes Mellitus Using Spectroscopy Techniques: A Systematic Review. Diseases 2023; 11:diseases11010016. [PMID: 36810530 PMCID: PMC9944100 DOI: 10.3390/diseases11010016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/28/2022] [Accepted: 01/20/2023] [Indexed: 01/27/2023] Open
Abstract
Gestational diabetes mellitus (GDM) is associated with adverse maternal and foetal consequences, along with the subsequent risk of type 2 diabetes mellitus (T2DM) and several other diseases. Due to early risk stratification in the prevention of progression of GDM, improvements in biomarker determination for GDM diagnosis will enhance the optimization of both maternal and foetal health. Spectroscopy techniques are being used in an increasing number of applications in medicine for investigating biochemical pathways and the identification of key biomarkers associated with the pathogenesis of GDM. The significance of spectroscopy promises the molecular information without the need for special stains and dyes; therefore, it speeds up and simplifies the necessary ex vivo and in vivo analysis for interventions in healthcare. All the selected studies showed that spectroscopy techniques were effective in the identification of biomarkers through specific biofluids. Existing GDM prediction and diagnosis through spectroscopy techniques presented invariable findings. Further studies are required in larger, ethnically diverse populations. This systematic review provides the up-to-date state of research on biomarkers in GDM, which were identified via various spectroscopy techniques, and a discussion of the clinical significance of these biomarkers in the prediction, diagnosis, and management of GDM.
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Affiliation(s)
- Rabia Sannam Khan
- Department of Bioengineering, Lancaster University, Lancaster LA1 4YW, UK
- Correspondence:
| | - Haroon Malik
- Queens Medical Centre, Jumeirah, Dubai P.O. Box 2652, United Arab Emirates
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Tkachenko K, Esteban-Díez I, González-Sáiz JM, Pérez-Matute P, Pizarro C. Dual Classification Approach for the Rapid Discrimination of Metabolic Syndrome by FTIR. BIOSENSORS 2022; 13:15. [PMID: 36671850 PMCID: PMC9855898 DOI: 10.3390/bios13010015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/12/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
Metabolic syndrome is a complex of interrelated risk factors for cardiovascular disease and diabetes. Thus, new point-of-care diagnostic tools are essential for unambiguously distinguishing MetS patients, providing results in rapid time. Herein, we evaluated the potential of Fourier transform infrared spectroscopy combined with chemometric tools to detect spectra markers indicative of metabolic syndrome. Around 105 plasma samples were collected and divided into two groups according to the presence of at least three of the five clinical parameters used for MetS diagnosis. A dual classification approach was studied based on selecting the most important spectral variable and classification methods, linear discriminant analysis (LDA) and SIMCA class modelling, respectively. The same classification methods were applied to measured clinical parameters at our disposal. Thus, the classification's performance on reduced spectra fingerprints and measured clinical parameters were compared. Both approaches achieved excellent discrimination results among groups, providing almost 100% accuracy. Nevertheless, SIMCA class modelling showed higher classification performance between MetS and no MetS for IR-reduced variables compared to clinical variables. We finally discuss the potential of this method to be used as a supportive diagnostic or screening tool in clinical routines.
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Affiliation(s)
| | | | | | - Patricia Pérez-Matute
- Infectious Diseases, Microbiota and Metabolism Unit, Infectious Diseases Department, Center for Biomedical Research of La Rioja (CIBIR), 26006 Logroño, Spain
| | - Consuelo Pizarro
- Department of Chemistry, University of La Rioja, 26006 Logroño, Spain
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12
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A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5905230. [PMID: 36569180 PMCID: PMC9788902 DOI: 10.1155/2022/5905230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.
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13
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Zhang R, Han X, Lei Z, Jiang C, Gul I, Hu Q, Zhai S, Liu H, Lian L, Liu Y, Zhang Y, Dong Y, Zhang CY, Lam TK, Han Y, Yu D, Zhou J, Qin P. RCMNet: A deep learning model assists CAR-T therapy for leukemia. Comput Biol Med 2022; 150:106084. [PMID: 36155267 DOI: 10.1016/j.compbiomed.2022.106084] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/16/2022] [Accepted: 09/03/2022] [Indexed: 11/30/2022]
Abstract
Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treating and curing acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with Convolutional Block Attention Module and Multi-Head Self-Attention) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cell dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy is achieved, which is higher than that of other state-of-the-art models. This study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications.
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Affiliation(s)
- Ruitao Zhang
- Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China; Precision Medicine and Public Health, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Xueying Han
- The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China
| | - Zhengyang Lei
- Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China; Precision Medicine and Public Health, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Chenyao Jiang
- Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China; Precision Medicine and Public Health, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Ijaz Gul
- Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China; Precision Medicine and Public Health, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Qiuyue Hu
- Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China; Precision Medicine and Public Health, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Shiyao Zhai
- Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China; Precision Medicine and Public Health, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Hong Liu
- Animal and Plant Inspection and Quarantine Technical Centre, Shenzhen Customs District, Shenzhen, Guangdong 518045, China
| | - Lijin Lian
- Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China; Precision Medicine and Public Health, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Ying Liu
- Animal and Plant Inspection and Quarantine Technical Centre, Shenzhen Customs District, Shenzhen, Guangdong 518045, China
| | - Yongbing Zhang
- Department of Computer Science, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
| | - Yuhan Dong
- Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China; Precision Medicine and Public Health, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Can Yang Zhang
- Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China; Precision Medicine and Public Health, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Tsz Kwan Lam
- Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China; Precision Medicine and Public Health, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Yuxing Han
- Precision Medicine and Public Health, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Dongmei Yu
- School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, Shandong 264209, China
| | - Jin Zhou
- The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China
| | - Peiwu Qin
- Institute of Biopharmaceutical and Health Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China; Precision Medicine and Public Health, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, China.
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14
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Peng W, Yin J, Ma J, Zhou X, Chang C. Identification of hepatocellular carcinoma and paracancerous tissue based on the peak area in FTIR microspectroscopy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:3115-3124. [PMID: 35920728 DOI: 10.1039/d2ay00640e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Hepatocellular carcinoma (HCC) is one of the most common primary hepatic malignancies across the world. The annual incidence and death rates have increased at the highest rate of all cancers in recent years. Surgical resection is a potentially curative option for solitary HCC or unilobar disease without evidence of metastases or vascular invasion. This study focuses on the molecular differences between the HCC foci and paracancerous tissues and provides some valuable biomarkers based on the vibrational spectrum. Fourier transform infrared (FTIR) spectroscopy is a non-invasive and qualitative and semi-quantitative analysis technique that has been widely applied for the identification of macromolecular changes in biological tissues. In this study, the FTIR spectra of the HCC foci and the paracancerous tissues were recorded separately, and ten areas under the absorption peaks of all the specimens were calculated. The result demonstrates that the areas of protein-related absorption peaks at 1398 cm-1, 1548 cm-1, 1654 cm-1 and 3070 cm-1 may be the key indicators of the two different regions. After coupling with the classification algorithms of k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM), it was found that SVM with an RBF kernel performed best with the AUC (area under the ROC curve) reaching 0.997, and the performance was better than the feature based on the full spectrum. This reveals that the peak area-based FTIR spectra combined with the SVM algorithm may be a promising tool in identifying the HCC foci and the paracancerous tissues.
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Affiliation(s)
- Wenyu Peng
- Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing 100071, China.
| | - Junkai Yin
- Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing 100071, China.
| | - Jing Ma
- Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing 100071, China.
| | - Xiaojie Zhou
- National Facility for Protein Science in Shanghai, Shanghai Advanced Research Institute, Chinese Academy of Science, Shanghai 201210, China
| | - Chao Chang
- Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing 100071, China.
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15
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Lugtu EJ, Ramos DB, Agpalza AJ, Cabral EA, Carandang RP, Dee JE, Martinez A, Jose JE, Santillan A, Bangaoil R, Albano PM, Tomas RC. Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy. PLoS One 2022; 17:e0268329. [PMID: 35551276 PMCID: PMC9098097 DOI: 10.1371/journal.pone.0268329] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 04/27/2022] [Indexed: 12/19/2022] Open
Abstract
Given the increasing prevalence of lung cancer worldwide, an auxiliary diagnostic method is needed alongside the microscopic examination of biopsy samples, which is dependent on the skills and experience of pathologists. Thus, this study aimed to advance lung cancer diagnosis by developing five (5) artificial neural network (NN) models that can discriminate malignant from benign samples based on infrared spectral data of lung tumors (n = 122; 56 malignant, 66 benign). NNs were benchmarked with classical machine learning (CML) models. Stratified 10-fold cross-validation was performed to evaluate the NN models, and the performance metrics—area under the curve (AUC), accuracy (ACC) positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)—were averaged for comparison. All NNs were able to outperform the CML models, however, support vector machine is relatively comparable to NNs. Among the NNs, CNN performed best with an AUC of 92.28% ± 7.36%, ACC of 98.45% ± 1.72%, PPV of 96.62% ± 2.30%, NPV of 90.50% ± 11.92%, SR of 96.01% ± 3.09%, and RR of 89.21% ± 12.93%. In conclusion, NNs can be potentially used as a computational tool in lung cancer diagnosis based on infrared spectroscopy of lung tissues.
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Affiliation(s)
- Eiron John Lugtu
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
- * E-mail:
| | - Denise Bernadette Ramos
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Alliah Jen Agpalza
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Erika Antoinette Cabral
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Rian Paolo Carandang
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Jennica Elia Dee
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Angelica Martinez
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Julius Eleazar Jose
- Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila, Philippines
| | - Abegail Santillan
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- The Graduate School, University of Santo Tomas, Manila, Philippines
| | - Ruth Bangaoil
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- The Graduate School, University of Santo Tomas, Manila, Philippines
- University of Santo Tomas Hospital, Manila, Philippines
| | - Pia Marie Albano
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- The Graduate School, University of Santo Tomas, Manila, Philippines
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Rock Christian Tomas
- Department of Electrical Engineering, University of the Philippines Los Baños, Laguna, Philippines
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16
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Skobeeva S, Banyard A, Rooney B, Thatti R, Thatti B, Fletcher J. Near-infrared spectroscopy combined with chemometrics to classify cosmetic foundations from a crime scene. Sci Justice 2022; 62:327-335. [PMID: 35598925 DOI: 10.1016/j.scijus.2022.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 02/23/2022] [Accepted: 03/06/2022] [Indexed: 11/26/2022]
Abstract
Cosmetic smears are a form of trace evidence that can link the crime scene, suspects, and victims. Foundation and lipstick are the most common sources of cosmetics that can easily smear, with most current research focused on the evidential analysis of lipsticks. This research aims to create a database of cosmetic foundations on different materials and to access the robustness of using Near-infrared with chemometrics as a non-destructive technique to identify unknown samples collected from a crime scene. Small amounts of six shades of three brands of foundations were smeared on clothing materials, which were then analysed with a combination of Near-infrared with chemometric analysis. Principle component analysis (PCA) was used to reduce data dimensionality and explore potential patterns in sample separation and Linear Discriminant Analysis (LDA) was utilised to assign unknown samples to one of the established classes. The selected techniques proved to be promising for database construction and as a preliminary method of analysis, with 93% of the spectra being correctly classified. Notably, darker foundation shades were less likely to be correctly classified (90% classified correctly) compared to lighter ones (96.7% classified correctly). This could not be improved with Standard Normal Variate (SNV) data pre-treatment or selecting specific NIR regions. This finding is of particular importance; according to the Crime Survey for England and Wales (year ending March 2020) police recorded sexual offences demonstrated that those in Mixed and Black or Black British ethnic groups were significantly more likely to be a victim of sexual assault compared to White, Asian or Other ethnic groups. It is, therefore, crucial to add a wide range of foundation shades, particularly of darker tones, to the future database.
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Affiliation(s)
- Svetlana Skobeeva
- School of Life Sciences, Pharmacy and Chemistry, Faculty of Science, Engineering and Computing, Kingston University, Penrhyn Road, Kingston upon Thames KT1 2EE, UK.
| | - Alana Banyard
- School of Life Sciences, Pharmacy and Chemistry, Faculty of Science, Engineering and Computing, Kingston University, Penrhyn Road, Kingston upon Thames KT1 2EE, UK.
| | - Brian Rooney
- School of Life Sciences, Pharmacy and Chemistry, Faculty of Science, Engineering and Computing, Kingston University, Penrhyn Road, Kingston upon Thames KT1 2EE, UK.
| | - Ravtej Thatti
- School of Life Sciences, Pharmacy and Chemistry, Faculty of Science, Engineering and Computing, Kingston University, Penrhyn Road, Kingston upon Thames KT1 2EE, UK.
| | - Baljit Thatti
- School of Life Sciences, Pharmacy and Chemistry, Faculty of Science, Engineering and Computing, Kingston University, Penrhyn Road, Kingston upon Thames KT1 2EE, UK.
| | - John Fletcher
- School of Life Sciences, Pharmacy and Chemistry, Faculty of Science, Engineering and Computing, Kingston University, Penrhyn Road, Kingston upon Thames KT1 2EE, UK.
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17
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Kołodziej M, Kaznowska E, Paszek S, Cebulski J, Barnaś E, Cholewa M, Vongsvivut J, Zawlik I. Characterisation of breast cancer molecular signature and treatment assessment with vibrational spectroscopy and chemometric approach. PLoS One 2022; 17:e0264347. [PMID: 35263369 PMCID: PMC8906614 DOI: 10.1371/journal.pone.0264347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 02/08/2022] [Indexed: 11/18/2022] Open
Abstract
Triple negative breast cancer (TNBC) is regarded as the most aggressive breast cancer subtype with poor overall survival and lack of targeted therapies, resulting in many patients with recurrent. The insight into the detailed biochemical composition of TNBC would help develop dedicated treatments. Thus, in this study Fourier Transform Infrared microspectroscopy combined with chemometrics and absorbance ratios investigation was employed to compare healthy controls with TNBC tissue before and after chemotherapy within the same patient. The primary spectral differences between control and cancer tissues were found in proteins, polysaccharides, and nucleic acids. Amide I/Amide II ratio decrease before and increase after chemotherapy, whereas DNA, RNA, and glycogen contents increase before and decrease after the treatment. The chemometric results revealed discriminatory features reflecting a clinical response scheme and proved the chemotherapy efficacy assessment with infrared spectroscopy is possible.
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Affiliation(s)
| | - Ewa Kaznowska
- Centre for Innovative Research in Medical and Natural Sciences, Medical College of Rzeszow University, Rzeszow, Poland
- Department of Pathology, Medical College of Rzeszow University, Rzeszow, Poland
| | - Sylwia Paszek
- Centre for Innovative Research in Medical and Natural Sciences, Medical College of Rzeszow University, Rzeszow, Poland
- Department of Genetics, Institution of Experimental and Clinical Medicine, University of Rzeszow, Poland
| | - Józef Cebulski
- Centre for Innovation and Transfer of Natural Sciences and Engineering Knowledge, University of Rzeszow, Rzeszow, Poland
| | - Edyta Barnaś
- Institute of Obstetrics and Emergency Medicine, Medical College of Rzeszow University, Rzeszow, Poland
| | - Marian Cholewa
- Centre for Innovation and Transfer of Natural Sciences and Engineering Knowledge, University of Rzeszow, Rzeszow, Poland
| | | | - Izabela Zawlik
- Centre for Innovative Research in Medical and Natural Sciences, Medical College of Rzeszow University, Rzeszow, Poland
- Department of Genetics, Institution of Experimental and Clinical Medicine, University of Rzeszow, Poland
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18
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Neto V, Esteves-Ferreira S, Inácio I, Alves M, Dantas R, Almeida I, Guimarães J, Azevedo T, Nunes A. Metabolic Profile Characterization of Different Thyroid Nodules Using FTIR Spectroscopy: A Review. Metabolites 2022; 12:metabo12010053. [PMID: 35050174 PMCID: PMC8777789 DOI: 10.3390/metabo12010053] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/22/2021] [Accepted: 01/05/2022] [Indexed: 12/14/2022] Open
Abstract
Thyroid cancer’s incidence has increased in the last decades, and its diagnosis can be a challenge. Further and complementary testing based in biochemical alterations may be important to correctly identify thyroid cancer and prevent unnecessary surgery. Fourier-transform infrared (FTIR) spectroscopy is a metabolomic technique that has already shown promising results in cancer metabolome analysis of neoplastic thyroid tissue, in the identification and classification of prostate tumor tissues and of breast carcinoma, among others. This work aims to gather and discuss published information on the ability of FTIR spectroscopy to be used in metabolomic studies of the thyroid, including discriminating between benign and malignant thyroid samples and grading and classifying different types of thyroid tumors.
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Affiliation(s)
- Vanessa Neto
- Department of Medical Sciences, iBiMED—Institute of Biomedicine, University of Aveiro, 3810-193 Aveiro, Portugal; (V.N.); (I.A.)
| | - Sara Esteves-Ferreira
- Centro Hospitalar do Baixo Vouga, CHBV—Endocrinology Department, 3810-164 Aveiro, Portugal; (S.E.-F.); (I.I.); (M.A.); (R.D.); (J.G.); (T.A.)
| | - Isabel Inácio
- Centro Hospitalar do Baixo Vouga, CHBV—Endocrinology Department, 3810-164 Aveiro, Portugal; (S.E.-F.); (I.I.); (M.A.); (R.D.); (J.G.); (T.A.)
| | - Márcia Alves
- Centro Hospitalar do Baixo Vouga, CHBV—Endocrinology Department, 3810-164 Aveiro, Portugal; (S.E.-F.); (I.I.); (M.A.); (R.D.); (J.G.); (T.A.)
| | - Rosa Dantas
- Centro Hospitalar do Baixo Vouga, CHBV—Endocrinology Department, 3810-164 Aveiro, Portugal; (S.E.-F.); (I.I.); (M.A.); (R.D.); (J.G.); (T.A.)
| | - Idália Almeida
- Department of Medical Sciences, iBiMED—Institute of Biomedicine, University of Aveiro, 3810-193 Aveiro, Portugal; (V.N.); (I.A.)
| | - Joana Guimarães
- Centro Hospitalar do Baixo Vouga, CHBV—Endocrinology Department, 3810-164 Aveiro, Portugal; (S.E.-F.); (I.I.); (M.A.); (R.D.); (J.G.); (T.A.)
| | - Teresa Azevedo
- Centro Hospitalar do Baixo Vouga, CHBV—Endocrinology Department, 3810-164 Aveiro, Portugal; (S.E.-F.); (I.I.); (M.A.); (R.D.); (J.G.); (T.A.)
| | - Alexandra Nunes
- Department of Medical Sciences, iBiMED—Institute of Biomedicine, University of Aveiro, 3810-193 Aveiro, Portugal; (V.N.); (I.A.)
- Correspondence:
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19
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Yang X, Ou Q, Yang W, Shi Y, Liu G. Diagnosis of liver cancer by FTIR spectra of serum. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 263:120181. [PMID: 34311164 DOI: 10.1016/j.saa.2021.120181] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 06/10/2021] [Accepted: 07/09/2021] [Indexed: 06/13/2023]
Abstract
Liver cancer is the most common fatal malignant tumor in the world. Early diagnosis of liver cancer can improve the survival rate of the patients with liver disease. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with curve fitting and chemometrics was used to distinguish the serum from patients from that of healthy people. The curve fitting results in protein range of 1700-1600 cm-1 showed that there were differences in the secondary structure of protein in serum between the patients with liver cancer and healthy people. Principal component analysis (PCA) in lipid range of 2900-2800 cm-1 could distinguish the serum of patients with liver cancer from that of healthy people. The first two principal components PC1 and PC2 explained 95% of the total data variance. The sensitivity and specificity of partial least squares discriminant analysis (PLS-DA) in lipid range of 2900-2800 cm-1 reached 92.85% and 95.23% respectively. It is shown that FTIR spectroscopy might be developed as an effective method for the diagnosis of liver cancer.
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Affiliation(s)
- Xien Yang
- School of Physics and Electronic Information, Yunnan Normal University, Kunming 650500, China
| | - Quanhong Ou
- School of Physics and Electronic Information, Yunnan Normal University, Kunming 650500, China
| | - Weiye Yang
- School of Preclinical Medicine, Zunyi Medical University, Zunyi 563003, China
| | - Youming Shi
- School of Physics and Electronic Engineering, Qujing Normal University, Qujing 655011, China
| | - Gang Liu
- School of Physics and Electronic Information, Yunnan Normal University, Kunming 650500, China.
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20
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Peng W, Chen S, Kong D, Zhou X, Lu X, Chang C. Grade diagnosis of human glioma using Fourier transform infrared microscopy and artificial neural network. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 260:119946. [PMID: 34049006 DOI: 10.1016/j.saa.2021.119946] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/22/2021] [Accepted: 05/06/2021] [Indexed: 06/12/2023]
Abstract
The World Health Organization (WHO) grade diagnosis of cancer is essential for surgical outcomes and patient treatment. Traditional pathological grading diagnosis depends on dyes or other histological approaches, and the result interpretation highly relies on the pathologists, making the process time-consuming (>60 min, including the steps of dewaxing to water and H&E staining), resource-wasting, and labor-intensive. In the present study, we report an alternative workflow that combines the Fourier transform infrared (FTIR) microscopy and artificial neural network (ANN) to diagnose the grade of human glioma in a way that is faster (~20 min, including the processes of sample dewaxing, spectra acquisition and analysis), accurate (the prediction accuracy, specificity and sensitivity can reach above 99%), and without reagent. Moreover, this method is much superior to the common classification method of principal component analysis-linear discriminate analysis (PCA-LDA) (the prediction accuracy, specificity and sensitivity are only 87%, 89% and 86%, respectively). The ANN mainly learned the characteristic region of 800-1800 cm-1 to classify the major histopathologic classes of human glioma. These results demonstrate that the grade diagnosis of human glioma by FTIR microscopy plus ANN can be streamlined, and could serve as a complementary pathway that is independent of the traditional pathology laboratory.
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Affiliation(s)
- Wenyu Peng
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science, Xi'an Jiaotong University, Xi'an 710049, China; Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing 100071, China
| | - Shuo Chen
- Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing 100071, China
| | - Dongsheng Kong
- Department of Neurosurgery, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Xiaojie Zhou
- National Facility for Protein Science in Shanghai, Shanghai Advanced Research Institute, Chinese Academy of Science, Shanghai 201210, China
| | - Xiaoyun Lu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science, Xi'an Jiaotong University, Xi'an 710049, China.
| | - Chao Chang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science, Xi'an Jiaotong University, Xi'an 710049, China; Innovation Laboratory of Terahertz Biophysics, National Innovation Institute of Defense Technology, Beijing 100071, China.
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21
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Yang X, Ou Q, Qian K, Yang J, Bai Z, Yang W, Shi Y, Liu G. Diagnosis of Lung Cancer by ATR-FTIR Spectroscopy and Chemometrics. Front Oncol 2021; 11:753791. [PMID: 34660320 PMCID: PMC8515056 DOI: 10.3389/fonc.2021.753791] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 09/15/2021] [Indexed: 01/06/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related death in the world. Early diagnosis has great significance for the survival of patients with lung cancer. In this paper, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to study the serum samples from patients with lung cancer and healthy people. The results of spectral band area comparison showed that the concentrations of protein, lipid and nucleic acids molecules in serum of patients with lung cancer were increased compared with those in healthy people. The original spectra were preprocessed to improve the accuracy of principal component regression (PCR) and partial least squares-discriminant analysis (PLS-DA) models. PLS-DA results for first derivative spectral data in nucleic acids (1250-1000cm-1) band showed 80% sensitivity, 91.89% specificity and 87.10% accuracy with highR c 2 of 0.8949 andR v 2 of 0.8153, low RMSEC of 0.3136 and RMSEV of 0.4180. It is shown that ATR-FTIR spectroscopy combined with chemometrics might be developed as a simple method for clinical screening and diagnosis of lung cancer.
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Affiliation(s)
- Xien Yang
- School of Physics and Electronic Information, Yunnan Normal University, Kunming, China
| | - Quanhong Ou
- School of Physics and Electronic Information, Yunnan Normal University, Kunming, China
| | - Kai Qian
- Department of Thoracic Surgery, The First People’s Hospital of Yunnan Province, Kunming, China
| | - Jianru Yang
- Department of Clinical Laboratory, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Zhixun Bai
- Department of Internal Medicine, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Weiye Yang
- School of Physics and Electronic Information, Yunnan Normal University, Kunming, China
| | - Youming Shi
- School of Physics and Electronic Engineering, Qujing Normal University, Qujing, China
| | - Gang Liu
- School of Physics and Electronic Information, Yunnan Normal University, Kunming, China
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22
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Guo Y, Hu C, Xia B, Zhou X, Luo S, Gan R, Duan P, Tan Y. Iodine excess induces hepatic, renal and pancreatic injury in female mice as determined by attenuated total reflection Fourier-transform infrared spectrometry. J Appl Toxicol 2021; 42:600-616. [PMID: 34585417 DOI: 10.1002/jat.4242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 08/23/2021] [Accepted: 09/05/2021] [Indexed: 11/08/2022]
Abstract
Limited knowledge of the long-term effects of excessive iodine (EI) intake on biomolecular signatures in the liver/pancreas/kidney prompted this study. Herein, following 6 months of exposure in mice to 300, 600, 1200 or 2400 μg/L iodine, the biochemical signature of alterations to the liver/pancreas/kidney was profiled using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with principal component analysis-linear discriminant analysis (PCA-LDA). Our research showed that serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum creatinine (Scr), insulin, blood glucose levels and homeostasis model assessment for insulin resistance (HOMA-IR) index in the 1200 and 2400 μg/L iodine-treated groups were significantly increased compared with those in the control group. Moreover, histological analysis showed that the liver/kidney/pancreas tissues of mice exposed to EI treatment displayed substantial morphological abnormalities, such as a loss of hepatic architecture, glomerular cell vacuolation and pancreatic neutrophilic infiltration. Notably, EI treatment caused distinct biochemical signature segregation between EI-exposed versus the control liver/pancreas/kidney. The main biochemical alterations between EI-exposed and control groups were observed for protein phosphorylation, protein secondary structures and lipids. The ratios of amide I-to-amide II (1674 cm-1 /1570 cm-1 ), α-helix-to-β-sheet (1657 cm-1 /1635 cm-1 ), glycogen-to-phosphate (1030 cm-1 /1086 cm-1 ) and the peptide aggregation (1 630 cm-1 /1650 cm-1 ) level of EI-treated groups significantly differed from the control group. Our study demonstrated that EI induced hepatic, renal and pancreatic injury by disturbing the structure, metabolism and function of the cell membrane. This finding provides the new method and implication for human health assessment regarding long-term EI intake.
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Affiliation(s)
- Yang Guo
- Key Laboratory of Zebrafish Modeling and Drug Screening for Human Diseases of Xiangyang City, Department of Obstetrics and Gynaecology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China.,College of Pharmacy, Hubei University of Medicine, Shiyan, Hubei, China
| | - Chunhui Hu
- Department of Clinical Laboratory, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Bintong Xia
- Department of Urology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Xianwen Zhou
- Fourth Clinical College, Hubei University of Medicine, Shiyan, China
| | - Sihan Luo
- Fourth Clinical College, Hubei University of Medicine, Shiyan, China
| | - Ruijia Gan
- Fourth Clinical College, Hubei University of Medicine, Shiyan, China
| | - Peng Duan
- Key Laboratory of Zebrafish Modeling and Drug Screening for Human Diseases of Xiangyang City, Department of Obstetrics and Gynaecology, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yan Tan
- Hubei Key Laboratory of Embryonic Stem Cell Research, Hubei University of Medicine, Shiyan, Hubei, China.,Department of Andrology, Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei, China
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23
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Roadmap on Universal Photonic Biosensors for Real-Time Detection of Emerging Pathogens. PHOTONICS 2021. [DOI: 10.3390/photonics8080342] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The COVID-19 pandemic has made it abundantly clear that the state-of-the-art biosensors may not be adequate for providing a tool for rapid mass testing and population screening in response to newly emerging pathogens. The main limitations of the conventional techniques are their dependency on virus-specific receptors and reagents that need to be custom-developed for each recently-emerged pathogen, the time required for this development as well as for sample preparation and detection, the need for biological amplification, which can increase false positive outcomes, and the cost and size of the necessary equipment. Thus, new platform technologies that can be readily modified as soon as new pathogens are detected, sequenced, and characterized are needed to enable rapid deployment and mass distribution of biosensors. This need can be addressed by the development of adaptive, multiplexed, and affordable sensing technologies that can avoid the conventional biological amplification step, make use of the optical and/or electrical signal amplification, and shorten both the preliminary development and the point-of-care testing time frames. We provide a comparative review of the existing and emergent photonic biosensing techniques by matching them to the above criteria and capabilities of preventing the spread of the next global pandemic.
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24
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Li L, Wu J, Yang L, Wang H, Xu Y, Shen K. Fourier Transform Infrared Spectroscopy: An Innovative Method for the Diagnosis of Ovarian Cancer. Cancer Manag Res 2021; 13:2389-2399. [PMID: 33737836 PMCID: PMC7965685 DOI: 10.2147/cmar.s291906] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 02/03/2021] [Indexed: 12/24/2022] Open
Abstract
Ovarian cancer is the most lethal gynecologic malignancy due to the late diagnoses at advanced stages, drug resistance and the high recurrence rate. Thus, there is an urgent need to develop new techniques to diagnose and monitor ovarian cancer patients. Fourier transform infrared (FTIR) spectroscopy has great potential in the diagnosis of this disease, as well as the real-time monitoring of cancer development and chemoresistance. As a noninvasive, simple and convenient technique, it can not only distinguish the molecular differences between normal and malignant tissues, but also be used to identify the characteristics of different types of ovarian cancer. FTIR spectroscopy is also widely used in monitoring cancer cells in response to antitumor drugs, distinguishing cells in different growth states, and identifying new synthetic drugs. In this paper, the applications of FTIR spectroscopy for ovarian cancer diagnosis and other works carried out so far are described in detail.
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Affiliation(s)
- Lei Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jinguang Wu
- Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Rare Earth Materials Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing, People's Republic of China
| | - Limin Yang
- State Key Laboratory of Nuclear Physics and Technology, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Huizi Wang
- Medical Science Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Yizhuang Xu
- Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Rare Earth Materials Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing, People's Republic of China
| | - Keng Shen
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
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25
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Yang Q, Tian GL, Qin JW, Wu BQ, Tan L, Xu L, Wu SZ, Yang JT, Jiang JH, Yu RQ. Coupling bootstrap with synergy self-organizing map-based orthogonal partial least squares discriminant analysis: Stable metabolic biomarker selection for inherited metabolic diseases. Talanta 2020; 219:121370. [DOI: 10.1016/j.talanta.2020.121370] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 06/27/2020] [Accepted: 06/30/2020] [Indexed: 12/13/2022]
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26
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Suryadevara V, Nazeer SS, Sreedhar H, Adelaja O, Kajdacsy-Balla A, Natarajan V, Walsh MJ. Infrared spectral microscopy as a tool to monitor lung fibrosis development in a model system. BIOMEDICAL OPTICS EXPRESS 2020; 11:3996-4007. [PMID: 33014581 PMCID: PMC7510888 DOI: 10.1364/boe.394730] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 06/17/2020] [Accepted: 06/18/2020] [Indexed: 06/11/2023]
Abstract
Tissue fibrosis is a progressive and destructive disease process that can occur in many different organs including the liver, kidney, skin, and lungs. Fibrosis is typically initiated by inflammation as a result of chronic insults such as infection, chemicals and autoimmune diseases. Current approaches to examine organ fibrosis are limited to radiological and histological analyses. Infrared spectroscopic imaging offers a potential alternative approach to gain insight into biochemical changes associated with fibrosis progression. In this study, we demonstrate that IR imaging of a mouse model of pulmonary fibrosis can identify biochemical changes observed with fibrosis progression and the beginning of resolution using K-means analysis, spectral ratios and multivariate data analysis. This study demonstrates that IR imaging may be a useful approach to understand the biochemical events associated with fibrosis initiation, progression and resolution for both the clinical setting and for assessing novel anti-fibrotic drugs in a model system.
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Affiliation(s)
- Vidyani Suryadevara
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Shaiju S. Nazeer
- Department of Pathology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Hari Sreedhar
- Department of Pathology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Oluwatobi Adelaja
- Department of Pathology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - André Kajdacsy-Balla
- Department of Pathology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Viswanathan Natarajan
- Department of Pharmacology, University of Illinois at Chicago, Chicago, IL 60612, USA
- Contributed equally as senior co-authors
| | - Michael J. Walsh
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Pathology, University of Illinois at Chicago, Chicago, IL 60612, USA
- Contributed equally as senior co-authors
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27
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Breast cancer detection by ATR-FTIR spectroscopy of blood serum and multivariate data-analysis. Talanta 2020; 214:120857. [PMID: 32278436 DOI: 10.1016/j.talanta.2020.120857] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 02/16/2020] [Accepted: 02/19/2020] [Indexed: 12/31/2022]
Abstract
Detection of breast cancer has particular importance for the diagnosis of cancer diseases. This is the most common type of cancer among women. Breast cancer is a malignant tumor of the glandular tissue of the breast. It is proposed to use infrared spectroscopy of blood serum as a simple and quick way to detect breast cancer. The paper presents the results of research using the methods of multivariate processing of IR spectra of human blood serum obtained by ATR-FTIR spectroscopy. The paper presents the results of research using the methods of multivariate processing of IR spectra of human blood serum obtained by ATR-FTIR spectroscopy. A sufficiently large sample of patients and healthy donors was diagnosed. Blood samples are examined from 66 patients who are clinically diagnosed with breast cancer and 80 healthy volunteers. A feature of the applied approach was a combination of the method of principal component analysis (PCA) and principal component regression (PCR) for processing the IR spectra of blood serum. The PCA method allows us to determine the spectral bands referring for the intensity differences between the control group and the patient group. Shown, that the range of 1306-1250cm-1 in the IR spectrum of blood serum is diagnostically significant for breast cancer. This range corresponds to the vibrations of several functional groups of DNA and RNA, which play a key role in discrimination in breast cancer screening using ATR-FTIR spectroscopy. It is shown that the proposed method has advantages in ease of use for clinical diagnosis and gives good results for the identification of breast cancer. The values of sensitivity (92.3%) and specificity (87.1%) obtained using the PCR method are close to those of mammography and ultrasound. This indicates the possibility of using this method in real clinical laboratory diagnostics.
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28
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Tracking Extracellular Matrix Remodeling in Lungs Induced by Breast Cancer Metastasis. Fourier Transform Infrared Spectroscopic Studies. Molecules 2020; 25:molecules25010236. [PMID: 31935974 PMCID: PMC6982691 DOI: 10.3390/molecules25010236] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 12/25/2019] [Accepted: 01/03/2020] [Indexed: 11/17/2022] Open
Abstract
This work focused on a detailed assessment of lung tissue affected by metastasis of breast cancer. We used large-area chemical scanning implemented in Fourier transform infrared (FTIR) spectroscopic imaging supported with classical histological and morphological characterization. For the first time, we differentiated and defined biochemical changes due to metastasis observed in the lung parenchyma, atelectasis, fibrous, and muscle cells, as well as bronchi ciliate cells, in a qualitative and semi-quantitative manner based on spectral features. The results suggested that systematic extracellular matrix remodeling with the progress of the metastasis process evoked a decrease in the fraction of the total protein in atelectasis, fibrous, and muscle cells, as well as an increase of fibrillar proteins in the parenchyma. We also detected alterations in the secondary conformations of proteins in parenchyma and atelectasis and changes in the level of hydroxyproline residues and carbohydrate moieties in the parenchyma. The results indicate the usability of FTIR spectroscopy as a tool for the detection of extracellular matrix remodeling, thereby enabling the prediction of pre-metastatic niche formation.
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29
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Wang CY, Tang L, Li L, Zhou Q, Li YJ, Li J, Wang YZ. Geographic Authentication of Eucommia ulmoides Leaves Using Multivariate Analysis and Preliminary Study on the Compositional Response to Environment. FRONTIERS IN PLANT SCIENCE 2020; 11:79. [PMID: 32140161 PMCID: PMC7042207 DOI: 10.3389/fpls.2020.00079] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 01/21/2020] [Indexed: 05/03/2023]
Abstract
To explore the influences of different cultivated areas on the chemical profiles of Eucommia ulmoides leaves (EUL) and rapidly authenticate its geographical origins, 187 samples from 13 provinces in China were systematically investigated using three data fusion strategies (low, mid, and high level) combined with two discrimination model algorithms (partial least squares discrimination analysis; random forest, RF). RF models constructed by high-level data fusion with different modes of different spectral data (Fourier transform near-infrared spectrum and attenuated total reflection Fourier transform mid-infrared spectrum) were most suitable for identifying EULs from different geographical origins. The accuracy rates of calibration and validation set were 92.86% and 93.44%, respectively. In addition, climate parameters were systematically investigated the cluster difference in our study. Some interesting and novel information could be found from the clustering tree diagram of hierarchical cluster analysis. The Xinjiang Autonomous Region (Region 5) located in the high latitude area was the only region in the middle temperate zone of all sample collection areas in which the samples belonged to an individual class no matter their distance in the tree diagram. The samples were from a relatively high elevation in the Shennongjia Forest District in Hubei Province (>1200 m), which is the main difference from the samples from Xiangyang City (78 m). Thus, the sample clusters from region 9 are different from the sample clusters from other regions. The results would provide a reference for further research to those samples from the special cluster.
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Affiliation(s)
- Chao-Yong Wang
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Biological Resources and Environmental Sciences, Jishou University, Jishou, China
| | - Li Tang
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of A & F Science and Technology, Hunan Applied Technology University, Changde, China
| | - Li Li
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Biological Resources and Environmental Sciences, Jishou University, Jishou, China
| | - Qiang Zhou
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Biological Resources and Environmental Sciences, Jishou University, Jishou, China
| | - You-Ji Li
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Chemistry and Chemical Engineering, Jishou University, Jishou, China
| | - Jing Li
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Biological Resources and Environmental Sciences, Jishou University, Jishou, China
- *Correspondence: Jing Li, ; Yuan-Zhong Wang,
| | - Yuan-Zhong Wang
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, China
- *Correspondence: Jing Li, ; Yuan-Zhong Wang,
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30
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Su KY, Lee WL. Fourier Transform Infrared Spectroscopy as a Cancer Screening and Diagnostic Tool: A Review and Prospects. Cancers (Basel) 2020; 12:E115. [PMID: 31906324 PMCID: PMC7017192 DOI: 10.3390/cancers12010115] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 12/21/2019] [Accepted: 12/24/2019] [Indexed: 02/07/2023] Open
Abstract
Infrared spectroscopy has long been used to characterize chemical compounds, but the applicability of this technique to the analysis of biological materials containing highly complex chemical components is arguable. However, recent advances in the development of infrared spectroscopy have significantly enhanced the capacity of this technique in analyzing various types of biological specimens. Consequently, there is an increased number of studies investigating the application of infrared spectroscopy in screening and diagnosis of various diseases. The lack of highly sensitive and specific methods for early detection of cancer has warranted the search for novel approaches. Being more simple, rapid, accurate, inexpensive, non-destructive and suitable for automation compared to existing screening, diagnosis, management and monitoring methods, Fourier transform infrared spectroscopy can potentially improve clinical decision-making and patient outcomes by detecting biochemical changes in cancer patients at the molecular level. Besides the commonly analyzed blood and tissue samples, extracellular vesicle-based method has been gaining popularity as a non-invasive approach. Therefore, infrared spectroscopic analysis of extracellular vesicles could be a useful technique in the future for biomedical applications. In this review, we discuss the potential clinical applications of Fourier transform infrared spectroscopic analysis using various types of biological materials for cancer. Additionally, the rationale and advantages of using extracellular vesicles in the spectroscopic analysis for cancer diagnostics are discussed. Furthermore, we highlight the challenges and future directions of clinical translation of the technique for cancer.
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Affiliation(s)
| | - Wai-Leng Lee
- School of Science, Monash University Malaysia, Subang Jaya 47500, Malaysia
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31
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Krzhizhanovskaya VV, Závodszky G, Lees MH, Dongarra JJ, Sloot PMA, Brissos S, Teixeira J. Application of the Stochastic Gradient Method in the Construction of the Main Components of PCA in the Task Diagnosis of Multiple Sclerosis in Children. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7303678 DOI: 10.1007/978-3-030-50423-6_3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Many different medical problems are characterized by quite large spatial dimensions, which causes the task of recognizing patterns to become troublesome. This is a well-known phenomenon called curse of dimensionality. These problems force the creation of various methods of reducing dimensionality. These methods are based on selection and extraction of features. The most commonly used method in literature, regarding the later, is the analysis of the main components of pca. The natural problem of this method is the possibility of applying it to linear space. It is a natural problem to develop the pca concept for cases of nonlinear feature spaces, optimization of feature selection for principal components and the inclusion of classes in the task of supervised learning. An important problem in the perspective of machine learning is not only a reduction of features and attributes but also separation of classes. The developed method was tested in two computer experiments using real data of multiple sclerosis in children. The discussed problem, even from the very nature of the data itself, is important because it can contribute to practical implementations in medical diagnostics. The purpose of the research is to develop a method of extracting features with the application of the stochastic gradient method in the task diagnosis of multiple sclerosis in children. This solution could contribute to the increasing quality of classification and thus may be the basis for building systems that support the medical diagnostics in recognition of multiple sclerosis in children.
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32
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Chrabaszcz K, Meyer T, Bae H, Schmitt M, Jasztal A, Smeda M, Stojak M, Popp J, Malek K, Marzec KM. Comparison of standard and HD FT-IR with multimodal CARS/TPEF/SHG/FLIMS imaging in the detection of the early stage of pulmonary metastasis of murine breast cancer. Analyst 2020; 145:4982-4990. [DOI: 10.1039/d0an00762e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The comparison of the potential of FT-IR in standard and high definition modes with multimodal CARS/TPEF/SHG/FLIMS imaging for detection of the early stage of pulmonary metastasis of murine breast cancer is presented.
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Affiliation(s)
- Karolina Chrabaszcz
- Faculty of Chemistry
- Jagiellonian University
- 30-387 Krakow
- Poland
- Jagiellonian Centre for Experimental Therapeutics
| | - Tobias Meyer
- Leibniz-Institute of Photonic Technology e.V
- Member of Leibniz Health Technologies
- 07745 Jena
- Germany
- Institute of Physical Chemistry and Abbe Center of Photonics
| | - Hyeonsoo Bae
- Institute of Physical Chemistry and Abbe Center of Photonics
- Friedrich-Schiller-University
- 07745 Jena
- Germany
| | - Michael Schmitt
- Institute of Physical Chemistry and Abbe Center of Photonics
- Friedrich-Schiller-University
- 07745 Jena
- Germany
| | - Agnieszka Jasztal
- Jagiellonian Centre for Experimental Therapeutics
- Jagiellonian University
- 30-384 Krakow
- Poland
| | - Marta Smeda
- Jagiellonian Centre for Experimental Therapeutics
- Jagiellonian University
- 30-384 Krakow
- Poland
| | - Marta Stojak
- Jagiellonian Centre for Experimental Therapeutics
- Jagiellonian University
- 30-384 Krakow
- Poland
| | - Jürgen Popp
- Leibniz-Institute of Photonic Technology e.V
- Member of Leibniz Health Technologies
- 07745 Jena
- Germany
- Institute of Physical Chemistry and Abbe Center of Photonics
| | - Kamilla Malek
- Faculty of Chemistry
- Jagiellonian University
- 30-387 Krakow
- Poland
- Jagiellonian Centre for Experimental Therapeutics
| | - Katarzyna M. Marzec
- Jagiellonian Centre for Experimental Therapeutics
- Jagiellonian University
- 30-384 Krakow
- Poland
- Centre for Medical Genomics OMICRON
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33
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Zheng Q, Li J, Yang L, Zheng B, Wang J, Lv N, Luo J, Martin FL, Liu D, He J. Raman spectroscopy as a potential diagnostic tool to analyse biochemical alterations in lung cancer. Analyst 2019; 145:385-392. [PMID: 31844853 DOI: 10.1039/c9an02175b] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Patient survival remains poor even after diagnosis in lung cancer cases, and the molecular events resulting from lung cancer progression remain unclear. Raman spectroscopy could be used to noninvasively and accurately reveal the biochemical properties of biological tissues on the basis of their pathological status. This study aimed at probing biomolecular changes in lung cancer, using Raman spectroscopy as a potential diagnostic tool. Herein, biochemical alterations were evident in the Raman spectra (region of 600-1800 cm-1) in normal and cancerous lung tissues. The levels of saturated and unsaturated lipids and the protein-to-lipid, nucleic acid-to-lipid, and protein-to-nucleic acid ratios were significantly altered among malignant tissues compared to normal lung tissues. These biochemical alterations in tissues during neoplastic transformation have profound implications in not only the biochemical landscape of lung cancer progression but also cytopathological classification. Based on this spectroscopic approach, classification methods including k-nearest neighbour (kNN) and support vector machine (SVM) were successfully applied to cytopathologically diagnose lung cancer with an accuracy approaching 99%. The present results indicate that Raman spectroscopy is an excellent tool to biochemically interrogate and diagnose lung cancer.
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Affiliation(s)
- Qingfeng Zheng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Junyi Li
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Lin Yang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Bo Zheng
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jiangcai Wang
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Ning Lv
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianbin Luo
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Francis L Martin
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston PR1 2HE, UK
| | - Dameng Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Jie He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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Classification of aggressive and classic mantle cell lymphomas using synchrotron Fourier Transform Infrared microspectroscopy. Sci Rep 2019; 9:12857. [PMID: 31492883 PMCID: PMC6731317 DOI: 10.1038/s41598-019-49326-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 08/15/2019] [Indexed: 12/21/2022] Open
Abstract
Mantle cell lymphoma (MCL) is regarded as an incurable neoplasm, even to the novel drug strategies. It is known MCL has two morphological variants- classic and aggressive. Aggressive MCL is characterized by a higher mitotic index and proliferation rate, and poor overall survival in comparison to classic subtype. The insight into the detailed biochemical composition of MCL is crucial in the further development of diagnostic and treatment guidelines for MCL patients; therefore Synchrotron radiation Fourier Transform Infrared (S-FTIR) microspectroscopy combined with Principal Component Analysis (PCA) was used. The major spectral differences were observed in proteins and nucleic acids content, revealing a classification scheme of classic and aggressive MCLs. The results obtained suggest that FTIR microspectroscopy has reflected the histopathological discrimination of both MCL subtypes.
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Duan P, Li J, Yang W, Li X, Long M, Feng X, Zhang Y, Chen C, Morais CLM, Martin FL, Luo J, Liu D, Xiong C. Fourier transform infrared and Raman-based biochemical profiling of different grades of pure foetal-type hepatoblastoma. JOURNAL OF BIOPHOTONICS 2019; 12:e201800304. [PMID: 30993892 DOI: 10.1002/jbio.201800304] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 04/05/2019] [Accepted: 04/07/2019] [Indexed: 06/09/2023]
Abstract
The biomolecular events resulting from the progression of hepatoblastoma remain to be elucidated. Fourier-transform infrared (FTIR) and Raman spectroscopies are capable of noninvasively and accurately capturing the biochemical properties of biological tissue from its pathological status. Our aim was to probe critial biomolecular changes of liver accompanying the progression of pure foetal hepatoblastoma (PFH) by FTIR and Raman spectroscopies. Herein, biochemical alterations were both evident in the FTIR spectra (regions of 3100-2800 cm-1 and 1800-900 cm-1 ) and the Raman spectra (region of 1800-400 cm-1 ) among normal, borderline and malignant liver tissues. Compared with normal tissues, the ratios of protein-to-lipid, α-helix-to-β-sheet, RNA-to-DNA, CH3 methyl-to-CH2 methylene, glucose-to-phospholipids, and unsaturated-to-saturated lipids intensities were significantly higher in malignant tissues, while the ratios of RNA-to-Amide II, DNA-to-Amide II, glycogen-to-cholesterol and Amide I-to-Amide II intensities were remarkably lower. These biochemical alterations in the transition from normal to malignant have profound implications not only for cyto-pathological classification but also for molecular understanding of PFH progression. The successive changes of the spectral characteristics have been shown to be consistent with the development of PFH, indicating that FTIR and Raman spectroscopies are excellent tools to interrogate the biochemical features of different grades of PFH.
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Affiliation(s)
- Peng Duan
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Reproductive Medicine, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Junyi Li
- State Key Laboratory of Tribology, Tsinghua University, Beijing, China
| | - Weiyingxue Yang
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiandong Li
- Department of Clinical Laboratory, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Manman Long
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaobing Feng
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuge Zhang
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chunling Chen
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Camilo L M Morais
- Lancashire Teaching Hospitals NHS Trust, Preston, UK
- Biocel Ltd, Hull, UK
| | | | - Jianbin Luo
- State Key Laboratory of Tribology, Tsinghua University, Beijing, China
| | - Dameng Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing, China
| | - Chengliang Xiong
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Center for Reproductive Medicine, Wuhan Tongji Reproductive Medicine Hospital, Wuhan, China
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36
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Mittal S, Bhargava R. A comparison of mid-infrared spectral regions on accuracy of tissue classification. Analyst 2019; 144:2635-2642. [PMID: 30839958 DOI: 10.1039/c8an01782d] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Infrared (IR) spectroscopic imaging, utilizing both the molecular and structural disease signatures, enables extensive profiling of tumors and their microenvironments. Here, we examine the relative merits of using either the fingerprint or the high frequency regions of the IR spectrum for tissue histopathology. We selected a complex model as a test case, evaluating both stromal and epithelial segmentation for various breast pathologies. IR spectral classification in each of these spectral windows is quantitatively assessed by estimating area under the curve (AUC) of the receiver operating characteristic curve (ROC) for pixel level accuracy and images for diagnostic ability. We found only small differences, though some that may be sufficiently important in diagnostic tasks to be clinically significant, between the two regions with the fingerprint region-based classifiers consistently emerging as more accurate. The work provides added evidence and comparison with fingerprint region, complex models, and previously untested tissue type (breast) - that the use of restricted spectral regions can provide high accuracy. Our study indicates that the fingerprint region is ideal for epithelial and stromal models to obtain high pixel level accuracies. Glass slides provide a limited spectral feature set but provides accurate information at the patient level.
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Affiliation(s)
- Shachi Mittal
- Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
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37
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New Approaches in Metaheuristic to Classify Medical Data Using Artificial Neural Network. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019. [DOI: 10.1007/s13369-019-04026-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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38
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Sun Y, Zhang M, Bhandari B, Yang P. Intelligent detection of flavor changes in ginger during microwave vacuum drying based on LF-NMR. Food Res Int 2019; 119:417-425. [PMID: 30884672 DOI: 10.1016/j.foodres.2019.02.019] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 01/23/2019] [Accepted: 02/08/2019] [Indexed: 10/27/2022]
Abstract
Low-field nuclear magnetic resonance (LF-NMR) and electronic nose combined with Gas chromatography mass spectrometry (GC-MS) were used to collect the data of moisture state and volatile substances to predict the flavor change of ginger during drying. An back propagation artificial neural network (BP-ANN) model was established with the input values of LF-NMR parameters and the output values of sensors for different flavor substances obtained from electronic nose. The results showed that fresh ginger contained three water components: bound water (T21), immobilized water (T22) and free water (T23), with the corresponding peak areas of A21, A22 and A23, respectively. During drying, the changes of A21 and A22 were not significant, while A23 and ATotal decreased significantly (p < .05). Linear discriminant analysis (LDA) of electronic nose data showed that samples with different drying time can be well distinguished. Hierarchical clustering analysis (HCA) confirmed that the electronic nose characteristic sensor data S4, S5, S8 and S13 corresponded with the data measured by GC-MS. The correlation analysis between LF-NMR parameters and characteristic sensors showed that A23 and ATotal were significantly correlated with the volatile components (p < .05). The results of the BP-ANN prediction showed that the model fitted well and had strong approximation ability (R > 0.95 and error < 3.65%) and stability, which indicated that the ANN model can accurately predict the flavor change during ginger drying based on LF-NMR parameters.
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Affiliation(s)
- Yanan Sun
- State Key Laboratory of Food Science and Technology, Jiangnan University, 214122 Wuxi, Jiangsu, China
| | - Min Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, 214122 Wuxi, Jiangsu, China; Jiangsu Province Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, China.
| | - Bhesh Bhandari
- School of Agriculture and Food Sciences, University of Queensland, Brisbane, QLD, Australia
| | - Peiqiang Yang
- Suzhou Niumang Analytical Instrument Corporation, 215000 Suzhou, Jiangsu, China
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Oleneva E, Panchenko A, Khaydukova M, Gubareva E, Bibikova O, Artyushenko V, Legin A, Kirsanov D. In vivo and in vitro application of near-infrared fiber optic probe for Ehrlich carcinoma distinction: Towards the development of real-time tumor margins assessment tool. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 213:12-18. [PMID: 30677734 DOI: 10.1016/j.saa.2019.01.061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Revised: 10/26/2018] [Accepted: 01/15/2019] [Indexed: 06/09/2023]
Abstract
This report describes a full-scale experiment on intradermal Ehrlich carcinoma (EC) differentiation in mouse model using NIR spectroscopy in diffuse reflectance mode and chemometric data processing. EC is widely used as an experimental tumor model due to its resemblance with human undifferentiated epithelial tumors and can be applied as a preclinical testing in order to verify the capability of NIR spectroscopy to distinguish cancer from healthy tissues before a clinical research with an aim of creating a new analytical tool for on-line intraoperative tumor margins assessment. The study consists of five steps of NIR spectra measurements: in vivo on the early stage of carcinoma growth; in vivo on the advanced stage of carcinoma growth; in vivo during the surgery; in vitro study of the post-operative materials stored in formalin; in vitro study of the post-operative materials stored in paraffin. It was shown that reliable tumor differentiation with a compact optic fiber probe was possible in all these cases. The classification models were built on two data sets, obtained during in vivo and in vitro measurements; both of them demonstrated 100% specificity and sensitivity.
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Affiliation(s)
- Ekaterina Oleneva
- Laboratory of Artificial Sensory Systems, ITMO University, 197101, Kronverksky prospect, 49, St. Petersburg, Russia.
| | - Andrey Panchenko
- Laboratory of Carcinogenesis and Aging, FSBI "N.N. Petrov National Medical Research Center of Oncology" of the Ministry of Healthcare of the Russian Federation, 197758, Leningradskaya street, 68, Pesochny, St. Petersburg, Russia
| | - Maria Khaydukova
- Institute of Chemistry, St. Petersburg State University, 199034, Universitetskaya emb., 7-9, St. Petersburg, Russia
| | - Ekaterina Gubareva
- Laboratory of Carcinogenesis and Aging, FSBI "N.N. Petrov National Medical Research Center of Oncology" of the Ministry of Healthcare of the Russian Federation, 197758, Leningradskaya street, 68, Pesochny, St. Petersburg, Russia
| | - Olga Bibikova
- Art photonics GmbH, 12489, Rudower Chaussee, 46, Berlin, Germany
| | | | - Andrey Legin
- Laboratory of Artificial Sensory Systems, ITMO University, 197101, Kronverksky prospect, 49, St. Petersburg, Russia; Institute of Chemistry, St. Petersburg State University, 199034, Universitetskaya emb., 7-9, St. Petersburg, Russia
| | - Dmitry Kirsanov
- Laboratory of Artificial Sensory Systems, ITMO University, 197101, Kronverksky prospect, 49, St. Petersburg, Russia; Institute of Chemistry, St. Petersburg State University, 199034, Universitetskaya emb., 7-9, St. Petersburg, Russia
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Depciuch J, Stanek-Widera A, Skrzypiec D, Lange D, Biskup-Frużyńska M, Kiper K, Stanek-Tarkowska J, Kula M, Cebulski J. Spectroscopic identification of benign (follicular adenoma) and cancerous lesions (follicular thyroid carcinoma) in thyroid tissues. J Pharm Biomed Anal 2019; 170:321-326. [PMID: 30954022 DOI: 10.1016/j.jpba.2019.03.061] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 03/25/2019] [Accepted: 03/27/2019] [Indexed: 01/03/2023]
Abstract
Thyroid follicular nodules are quite common in the population, however only a small proportion is malignant. Thyroid cancer differs from adenoma by features of cellular atypia, angioinvasiveness and possibility of metastasis via blood vessels mainly in the lungs and bones. Pathomorphological examination of the postoperative material plays a significant role in the diagnosis of cystic thyroid lesions. De facto, there is no possibility to determine with certainty whether the lesion is benign or malignant before surgery, therefore new methods are being sought to meet clinical needs. The study aimed to investigate if Fourier-transform infrared spectroscopy (FTIR) spectroscopy and Raman spectroscopy combined with multidimensional analysis can be a useful tool in distinguishing between thyroid adenomas and carcinomas. The obtained results indicate quantitative and qualitative alterations within proteins and fats derived from patients' tissues samples. Raman spectroscopy additionally shows significant changes in the amount of tissue collagen due to the pathogenic process. In the spectra of the second FTIR derivative, shifts of vibrations corresponding to the β-sheet and α-helix structure are observed towards the lower rates of wave numbers in the case of neoplastic tissues. Using the leave-one-out cross-validation, sensitivity and specificity calculated with Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) clearly shows the possibility to distinguish between pathologically changed and normal thyroid tissue as well as differentiate follicular thyroid adenoma (FTA) from widely invasive follicular thyroid carcinoma (WI-FTC) tissues.
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Affiliation(s)
- Joanna Depciuch
- Institute of Nuclear Physics Polish Academy of Sciences, PL-31342, Krakow, Poland.
| | - Agata Stanek-Widera
- Department of Tumor Pathology, Maria Sklodowska-Curie Memorial Cancer Centre and Institute of Oncology, PL-44101, Gliwice, Poland
| | - Dominika Skrzypiec
- Center for Innovation and Transfer of Natural Sciences and Engineering Knowledge, University of Rzeszow, PL-35959, Rzeszow, Poland
| | - Dariusz Lange
- Department of Tumor Pathology, Maria Sklodowska-Curie Memorial Cancer Centre and Institute of Oncology, PL-44101, Gliwice, Poland
| | - Magdalena Biskup-Frużyńska
- Department of Tumor Pathology, Maria Sklodowska-Curie Memorial Cancer Centre and Institute of Oncology, PL-44101, Gliwice, Poland
| | - Krzysztof Kiper
- Faculty of Medicine, University of Rzeszow, PL-35959, Rzeszow, Poland
| | | | - Monika Kula
- Polish Academy of Sciences, The Franciszek Górski Institute of Plant Physiology, Niezapominajek 21, 30239, Krakow, Poland
| | - Jozef Cebulski
- Center for Innovation and Transfer of Natural Sciences and Engineering Knowledge, University of Rzeszow, PL-35959, Rzeszow, Poland
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41
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Chaber R, Arthur CJ, Łach K, Raciborska A, Michalak E, Bilska K, Drabko K, Depciuch J, Kaznowska E, Cebulski J. Predicting Ewing Sarcoma Treatment Outcome Using Infrared Spectroscopy and Machine Learning. Molecules 2019; 24:molecules24061075. [PMID: 30893786 PMCID: PMC6470837 DOI: 10.3390/molecules24061075] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/03/2019] [Accepted: 03/14/2019] [Indexed: 12/03/2022] Open
Abstract
Background: Improved outcome prediction is vital for the delivery of risk-adjusted, appropriate and effective care to paediatric patients with Ewing sarcoma—the second most common paediatric malignant bone tumour. Fourier transform infrared (FTIR) spectroscopy of tissues allows the bulk biochemical content of a biological sample to be probed and makes possible the study and diagnosis of disease. Methods: In this retrospective study, FTIR spectra of sections of biopsy-obtained bone tissue were recorded. Twenty-seven patients (between 5 and 20 years of age) with newly diagnosed Ewing sarcoma of bone were included in this study. The prognostic value of FTIR spectra obtained from Ewing sarcoma (ES) tumours before and after neoadjuvant chemotherapy were analysed in combination with various data-reduction and machine learning approaches. Results: Random forest and linear discriminant analysis supervised learning models were able to correctly predict patient mortality in 92% of cases using leave-one-out cross-validation. The best performing model for predicting patient relapse was a linear Support Vector Machine trained on the observed spectral changes as a result of chemotherapy treatment, which achieved 92% accuracy. Conclusion: FTIR spectra of tumour biopsy samples may predict treatment outcome in paediatric Ewing sarcoma patients with greater than 92% accuracy.
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Affiliation(s)
- Radosław Chaber
- Clinic of Paediatric Oncology and Haematology, Faculty of Medicine, University of Rzeszow, ul. Kopisto 2a, 35-310 Rzeszow, Poland.
| | | | - Kornelia Łach
- Clinic of Paediatric Oncology and Haematology, Faculty of Medicine, University of Rzeszow, ul. Kopisto 2a, 35-310 Rzeszow, Poland.
| | - Anna Raciborska
- Department of Surgical Oncology for Children and Youth, Institute of Mother and Child, 01-211 Warsaw, Poland.
| | - Elżbieta Michalak
- Department of Pathology, Institute of Mother and Child, 01-211 Warsaw, Poland.
| | - Katarzyna Bilska
- Department of Surgical Oncology for Children and Youth, Institute of Mother and Child, 01-211 Warsaw, Poland.
| | - Katarzyna Drabko
- Department of Pediatric Hematology, Oncology and Bone Marrow Transplant, Medical University of Lublin, 20-081 Lublin, Poland.
| | - Joanna Depciuch
- Institute of Nuclear Physics, Polish Academy of Sciences, 31-342 Krakow, Poland.
| | - Ewa Kaznowska
- Laboratory of Molecular Biology, Centre for Innovative Research in Medical and Natural Sciences, Faculty of Medicine, University of Rzeszow, 35-959 Rzeszow, Poland.
- Department of Human Histology, Chair of Morphological Sciences, Faculty of Medicine, University of Rzeszow, 35-959 Rzeszow, Poland.
| | - Józef Cebulski
- Center for Innovation and Transfer of Natural Sciences and Engineering Knowledge, University of Rzeszow, 35-959 Rzeszow, Poland.
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Gaydou V, Polette M, Gobinet C, Kileztky C, Angiboust JF, Birembaut P, Vuiblet V, Piot O. New insights into spectral histopathology: infrared-based scoring of tumour aggressiveness of squamous cell lung carcinomas. Chem Sci 2019; 10:4246-4258. [PMID: 31057753 PMCID: PMC6471539 DOI: 10.1039/c8sc04320e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 03/01/2019] [Indexed: 12/25/2022] Open
Abstract
Spectral histopathology, based on infrared interrogation of tissue sections, proved a promising tool for helping pathologists in characterizing histological structures in a quantitative and automatic manner.
Spectral histopathology, based on infrared interrogation of tissue sections, proved a promising tool for helping pathologists in characterizing histological structures in a quantitative and automatic manner. In cancer diagnosis, the use of chemometric methods permits establishing numerical models able to detect cancer cells and to characterize their tissular environment. In this study, we focused on exploiting multivariate infrared data to score the tumour aggressiveness in preneoplastic lesions and squamous cell lung carcinomas. These lesions present a wide range of aggressive phenotypes; it is also possible to encounter cases with various degrees of aggressiveness within the same lesion. Implementing an infrared-based approach for a more precise histological determination of the tumour aggressiveness should arouse interest among pathologists with direct benefits for patient care. In this study, the methodology was developed from a set of samples including all degrees of tumour aggressiveness and by constructing a chain of data processing steps for an automated analysis of tissues currently manipulated in routine histopathology.
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Affiliation(s)
- Vincent Gaydou
- BioSpecT Unit , EA 7506 , University of Reims Champagne-Ardenne , Pharmacy Department , 51 rue Cognacq-Jay , 51096 Reims , France .
| | - Myriam Polette
- INSERM UMR-S 1250 , University of Reims Champagne-Ardenne , 45, rue Cognacq-Jay , 51092 Reims , France.,Biopathology Laboratory , Centre Hospitalier et Universitaire de Reims , 45 Rue Cognacq-Jay , 51092 Reims , France
| | - Cyril Gobinet
- BioSpecT Unit , EA 7506 , University of Reims Champagne-Ardenne , Pharmacy Department , 51 rue Cognacq-Jay , 51096 Reims , France .
| | - Claire Kileztky
- INSERM UMR-S 1250 , University of Reims Champagne-Ardenne , 45, rue Cognacq-Jay , 51092 Reims , France
| | - Jean-François Angiboust
- BioSpecT Unit , EA 7506 , University of Reims Champagne-Ardenne , Pharmacy Department , 51 rue Cognacq-Jay , 51096 Reims , France .
| | - Philippe Birembaut
- INSERM UMR-S 1250 , University of Reims Champagne-Ardenne , 45, rue Cognacq-Jay , 51092 Reims , France.,Biopathology Laboratory , Centre Hospitalier et Universitaire de Reims , 45 Rue Cognacq-Jay , 51092 Reims , France
| | - Vincent Vuiblet
- BioSpecT Unit , EA 7506 , University of Reims Champagne-Ardenne , Pharmacy Department , 51 rue Cognacq-Jay , 51096 Reims , France . .,Biopathology Laboratory , Centre Hospitalier et Universitaire de Reims , 45 Rue Cognacq-Jay , 51092 Reims , France
| | - Olivier Piot
- BioSpecT Unit , EA 7506 , University of Reims Champagne-Ardenne , Pharmacy Department , 51 rue Cognacq-Jay , 51096 Reims , France . .,Platform of Cellular and Tissular Imaging (PICT) , University of Reims Champagne-Ardenne , 51 rue Cognacq-Jay , 51096 Reims , France
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43
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Distinguishing Ewing sarcoma and osteomyelitis using FTIR spectroscopy. Sci Rep 2018; 8:15081. [PMID: 30305666 PMCID: PMC6180062 DOI: 10.1038/s41598-018-33470-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 10/01/2018] [Indexed: 01/19/2023] Open
Abstract
The differential diagnosis of Ewing sarcoma and osteomyelitis can be challenging and can lead to delays in treatment with possibly devastating results. In this retrospective, small-cohort study we demonstrate, that the Fourier Transformed Infrared (FTIR) spectra of osteomyelitis bone tissue can be differentiated from Ewing sarcoma and normal bone tissue sampled outside tumour area. Significant differences in osteomyelitis samples can be seen in lipid and protein composition. Supervised learning using a quadratic discriminant analysis classifier was able to differentiate the osteomyelitis samples with high accuracy. FTIR spectroscopy, alongside routine radiological and histopathological methods, may offer an additional tool for the differential diagnosis of osteomyelitis and ES.
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