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Mokari A, Guo S, Bocklitz T. Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning. Molecules 2023; 28:6886. [PMID: 37836728 PMCID: PMC10574384 DOI: 10.3390/molecules28196886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/13/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.
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Affiliation(s)
- Azadeh Mokari
- Leibniz Institute of Photonic Technology, Member of Research Alliance “Leibniz Health Technologies”, 07745 Jena, Germany (S.G.)
- Institute of Physical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany
| | - Shuxia Guo
- Leibniz Institute of Photonic Technology, Member of Research Alliance “Leibniz Health Technologies”, 07745 Jena, Germany (S.G.)
| | - Thomas Bocklitz
- Leibniz Institute of Photonic Technology, Member of Research Alliance “Leibniz Health Technologies”, 07745 Jena, Germany (S.G.)
- Institute of Physical Chemistry, Friedrich Schiller University Jena, 07743 Jena, Germany
- Institute of Computer Science, Faculty of Mathematics, Physics & Computer Science, University Bayreuth, Universitaet sstraße 30, 95447 Bayreuth, Germany
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Lin H, Wang Z, Luo Y, Sun Q, Shen Y, Huang P. Post-mortem evaluation of the pathological degree of myocardial infarction by Fourier transform infrared microspectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 268:120630. [PMID: 34815176 DOI: 10.1016/j.saa.2021.120630] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/18/2021] [Accepted: 11/11/2021] [Indexed: 06/13/2023]
Abstract
In clinical and forensic investigations, accurate post-mortem diagnosis of the pathological degree of myocardial infarction (MI) is critical. However, because of the observer variability, the diagnosis cannot be made objectively. Many studies have shown that Fourier transform infrared (FTIR) microspectroscopy is non-invasive, observer-independent, and label-free when analyzing biological tissues. In this study, we used FTIR microspectroscopy in combination with intelligent algorithms to identify the pathological phases in human infarcted cardiac tissues, including ischemia, necrotic, granulation, and fibrotic stages. First, a comparison of infrared spectra corresponding to infarcted tissue pathological categories revealed various spectral properties. The results of unsupervised principal component analysis (PCA) revealed a clear distinction between these four pathological stages and the normal stage. Then, to identify these five stages, an automatic artificial neural network (ANN) classifier was effectively created. Finally, two-dimensional pseudo-color images of two infarcted cardiac tissue sections visualized via the ANN classifier showed great agreement with their histological images. These findings demonstrate that FTIR microspectroscopy has the potential for the post-mortem evaluation of the pathological degree of MI.
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Affiliation(s)
- Hancheng Lin
- Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
| | - Zhenyuan Wang
- Department of Forensic Pathology, Xi'an Jiaotong University, Xi'an 710061, China
| | - Yiwen Luo
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Institute of Forensic Science, Ministry of Justice, PRC, Shanghai 200063, China
| | - Qiran Sun
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Institute of Forensic Science, Ministry of Justice, PRC, Shanghai 200063, China
| | - Yiwen Shen
- Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.
| | - Ping Huang
- Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China; Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Institute of Forensic Science, Ministry of Justice, PRC, Shanghai 200063, China. @ssfjd.cn
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3
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Riera-Mestre A, Cerdà P, Iriarte A, Graupera M, Viñals F. Translational medicine in hereditary hemorrhagic telangiectasia. Eur J Intern Med 2022; 95:32-37. [PMID: 34538686 DOI: 10.1016/j.ejim.2021.09.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 09/04/2021] [Indexed: 12/18/2022]
Abstract
Scientific community have gained lots of new insights in the genetic and biochemical background of different conditions, rare diseases included, settling the basis for preclinical models that are helping to identify new biomarkers and therapeutic targets. Translational Medicine (TM) is an interdisciplinary area of biomedicine with an essential role in bench-to-bedside transition enhancement, generating a circular flow of knowledge transference between research environment and clinical setting, always centered in patient needs. Here, we present different tools used in TM and an overview of what is being done related to hereditary hemorrhagic telangiectasia (HHT), as a disease's model. This work is focused on how this combination of basic and clinical research impacts in HHT patient's daily clinical management and also looking into the future. Further randomized clinical trials with HHT patients should assess the findings of this bench-to-bedside transition. The benefits of this basic and clinical research combination, may not only be important for HHT patients but for patients with other vascular diseases sharing angiogenic disturbances.
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Affiliation(s)
- A Riera-Mestre
- HHT Unit. Internal Medicine Department. Hospital Universitari Bellvitge, C/ Feixa Llarga s/n., L'Hospitalet de Llobregat, Barcelona 08907, Spain; Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain; Faculty of Medicine and Health Sciences. Universitat de Barcelona, Barcelona, Spain.
| | - P Cerdà
- HHT Unit. Internal Medicine Department. Hospital Universitari Bellvitge, C/ Feixa Llarga s/n., L'Hospitalet de Llobregat, Barcelona 08907, Spain; Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - A Iriarte
- HHT Unit. Internal Medicine Department. Hospital Universitari Bellvitge, C/ Feixa Llarga s/n., L'Hospitalet de Llobregat, Barcelona 08907, Spain; Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - M Graupera
- Endothelial Pathobiology and Microenvironment, Josep Carreras Leukaemia Research Institute, Barcelona 08916, Spain; CIBERONC, Instituto de Salud Carlos III, Madrid, Spain
| | - F Viñals
- Physiological Sciences Department. Faculty of Medicine and Health Sciences. Universitat de Barcelona, Barcelona, Spain; Program Against Cancer Therapeutic Resistance, Hospital Duran i Reynals, Institut Catala d'Oncologia, Barcelona, Spain; Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
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4
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Faviez C, Chen X, Garcelon N, Neuraz A, Knebelmann B, Salomon R, Lyonnet S, Saunier S, Burgun A. Diagnosis support systems for rare diseases: a scoping review. Orphanet J Rare Dis 2020; 15:94. [PMID: 32299466 PMCID: PMC7164220 DOI: 10.1186/s13023-020-01374-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 03/31/2020] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION Rare diseases affect approximately 350 million people worldwide. Delayed diagnosis is frequent due to lack of knowledge of most clinicians and a small number of expert centers. Consequently, computerized diagnosis support systems have been developed to address these issues, with many relying on rare disease expertise and taking advantage of the increasing volume of generated and accessible health-related data. Our objective is to perform a review of all initiatives aiming to support the diagnosis of rare diseases. METHODS A scoping review was conducted based on methods proposed by Arksey and O'Malley. A charting form for relevant study analysis was developed and used to categorize data. RESULTS Sixty-eight studies were retained at the end of the charting process. Diagnosis targets varied from 1 rare disease to all rare diseases. Material used for diagnosis support consisted mostly of phenotype concepts, images or fluids. Fifty-seven percent of the studies used expert knowledge. Two-thirds of the studies relied on machine learning algorithms, and one-third used simple similarities. Manual algorithms were encountered as well. Most of the studies presented satisfying performance of evaluation by comparison with references or with external validation. Fourteen studies provided online tools, most of which aimed to support the diagnosis of all rare diseases by considering queries based on phenotype concepts. CONCLUSION Numerous solutions relying on different materials and use of various methodologies are emerging with satisfying preliminary results. However, the variability of approaches and evaluation processes complicates the comparison of results. Efforts should be made to adequately validate these tools and guarantee reproducibility and explicability.
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Affiliation(s)
- Carole Faviez
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.
| | - Xiaoyi Chen
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France
| | - Nicolas Garcelon
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.,Institut Imagine, Université de Paris, F-75015, Paris, France
| | - Antoine Neuraz
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.,Département d'informatique médicale, Hôpital Necker-Enfants Malades, Assistance Publique - Hôpitaux de Paris (AP-HP), F-75015, Paris, France
| | - Bertrand Knebelmann
- Service de Néphrologie Transplantation Adultes, Hôpital Necker-Enfants Malades, F-75015, Paris, France.,Université de Paris, F-75006, Paris, France.,Institut Necker-Enfants Malades, INSERM, Hôpital Necker-Enfants Malades, F-75015, Paris, France
| | - Rémi Salomon
- Institut Imagine, Université de Paris, F-75015, Paris, France.,Service de Néphrologie Pédiatrique, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris (AP-HP), Université de Paris, F-75015, Paris, France
| | - Stanislas Lyonnet
- Université de Paris, F-75006, Paris, France.,Laboratory of Embryology and Genetics of Congenital Malformations, INSERM UMR 1163, Université de Paris, Imagine Institute, F-75015, Paris, France.,Service de génétique, Hôpital Necker-Enfants Malades, Assistance Publique - Hôpitaux de Paris (AP-HP), F-75015, Paris, France
| | - Sophie Saunier
- Université de Paris, F-75006, Paris, France.,Laboratory of Renal Hereditary Diseases, INSERM UMR 1163, Université de Paris, Imagine Institute, F-75015, Paris, France
| | - Anita Burgun
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.,Département d'informatique médicale, Hôpital Necker-Enfants Malades, Assistance Publique - Hôpitaux de Paris (AP-HP), F-75015, Paris, France.,Université de Paris, F-75006, Paris, France.,PaRis Artificial Intelligence Research InstitutE (PRAIRIE), Paris, France
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5
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Brasil S, Pascoal C, Francisco R, dos Reis Ferreira V, A. Videira P, Valadão G. Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter? Genes (Basel) 2019; 10:genes10120978. [PMID: 31783696 PMCID: PMC6947640 DOI: 10.3390/genes10120978] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/19/2019] [Accepted: 11/20/2019] [Indexed: 02/06/2023] Open
Abstract
The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs’ challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs’ AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.
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Affiliation(s)
- Sandra Brasil
- Portuguese Association for CDG, 2820-381 Lisboa, Portugal; (S.B.); (C.P.); (R.F.); (P.A.V.)
- CDG & Allies—Professionals and Patient Associations International Network (CDG & Allies—PPAIN), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
| | - Carlota Pascoal
- Portuguese Association for CDG, 2820-381 Lisboa, Portugal; (S.B.); (C.P.); (R.F.); (P.A.V.)
- CDG & Allies—Professionals and Patient Associations International Network (CDG & Allies—PPAIN), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
- UCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
| | - Rita Francisco
- Portuguese Association for CDG, 2820-381 Lisboa, Portugal; (S.B.); (C.P.); (R.F.); (P.A.V.)
- CDG & Allies—Professionals and Patient Associations International Network (CDG & Allies—PPAIN), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
- UCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
| | - Vanessa dos Reis Ferreira
- Portuguese Association for CDG, 2820-381 Lisboa, Portugal; (S.B.); (C.P.); (R.F.); (P.A.V.)
- CDG & Allies—Professionals and Patient Associations International Network (CDG & Allies—PPAIN), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
- Correspondence:
| | - Paula A. Videira
- Portuguese Association for CDG, 2820-381 Lisboa, Portugal; (S.B.); (C.P.); (R.F.); (P.A.V.)
- CDG & Allies—Professionals and Patient Associations International Network (CDG & Allies—PPAIN), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
- UCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Lisboa, Portugal
| | - Gonçalo Valadão
- Instituto de Telecomunicações, 1049-001 Lisboa, Portugal;
- Departamento de Ciências e Tecnologias, Autónoma Techlab–Universidade Autónoma de Lisboa, 1169-023 Lisboa, Portugal
- Electronics, Telecommunications and Computers Engineering Department, Instituto Superior de Engenharia de Lisboa, 1959-007 Lisboa, Portugal
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Martín-Llahí M, Albillos A, Bañares R, Berzigotti A, García-Criado MÁ, Genescà J, Hernández-Gea V, Llop-Herrera E, Masnou-Ridaura H, Mateo J, Navascués CA, Puente Á, Romero-Gutiérrez M, Simón-Talero M, Téllez L, Turon F, Villanueva C, Zarrabeitia R, García-Pagán JC. Enfermedades vasculares del hígado. Guías Clínicas de la Sociedad Catalana de Digestología y de la Asociación Española para el Estudio del Hígado. GASTROENTEROLOGIA Y HEPATOLOGIA 2017; 40:538-580. [DOI: 10.1016/j.gastrohep.2017.03.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 03/29/2017] [Indexed: 12/11/2022]
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7
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Robaina Cabrera DM, Verde González MP, Tarazona Chocano B, Amado Fernández C, Zarrabeitia Puente R. Telangiectasia hemorrágica hereditaria: enfermedad de Rendu-Osler-Weber. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.fmc.2016.01.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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8
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Botella LM, Albiñana V, Ojeda-Fernandez L, Recio-Poveda L, Bernabéu C. Research on potential biomarkers in hereditary hemorrhagic telangiectasia. Front Genet 2015; 6:115. [PMID: 25873934 PMCID: PMC4379940 DOI: 10.3389/fgene.2015.00115] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2014] [Accepted: 03/05/2015] [Indexed: 12/21/2022] Open
Abstract
Hereditary hemorrhagic telangiectasia (HHT) is a genetically heterogeneous disorder, involving mutations in two predominant genes known as Endoglin (ENG; HHT1) and activin receptor-like kinase 1 (ACVRL1/ALK1; HHT2), as well as in some less frequent genes, such as MADH4/SMAD4 (JP-HHT) or BMP9/GDF2 (HHT5). The diagnosis of HHT patients currently remains at the clinical level, according to the “Curaçao criteria,” whereas the molecular diagnosis is used to confirm or rule out suspected HHT cases, especially when a well characterized index case is present in the family or in an isolated population. Unfortunately, many suspected patients do not present a clear HHT diagnosis or do not show pathogenic mutations in HHT genes, prompting the need to investigate additional biomarkers of the disease. Here, several HHT biomarkers and novel methodological approaches developed during the last years will be reviewed. On one hand, products detected in plasma or serum samples: soluble proteins (vascular endothelial growth factor, transforming growth factor β1, soluble endoglin, angiopoietin-2) and microRNA variants (miR-27a, miR-205, miR-210). On the other hand, differential HHT gene expression fingerprinting, next generation sequencing of a panel of genes involved in HHT, and infrared spectroscopy combined with artificial neural network patterns will also be reviewed. All these biomarkers might help to improve and refine HHT diagnosis by distinguishing from the non-HHT population.
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Affiliation(s)
- Luisa-María Botella
- Department of Cellular and Molecular Medicine, Centro de Investigaciones Biológicas, Consejo Superior de Investigaciones Cientificas , Madrid, Spain ; Centro de Investigación Biomédica en Red de Enfermedades Raras , Madrid, Spain
| | - Virginia Albiñana
- Department of Cellular and Molecular Medicine, Centro de Investigaciones Biológicas, Consejo Superior de Investigaciones Cientificas , Madrid, Spain
| | - Luisa Ojeda-Fernandez
- Department of Cellular and Molecular Medicine, Centro de Investigaciones Biológicas, Consejo Superior de Investigaciones Cientificas , Madrid, Spain ; Centro de Investigación Biomédica en Red de Enfermedades Raras , Madrid, Spain
| | - Lucia Recio-Poveda
- Department of Cellular and Molecular Medicine, Centro de Investigaciones Biológicas, Consejo Superior de Investigaciones Cientificas , Madrid, Spain
| | - Carmelo Bernabéu
- Department of Cellular and Molecular Medicine, Centro de Investigaciones Biológicas, Consejo Superior de Investigaciones Cientificas , Madrid, Spain ; Centro de Investigación Biomédica en Red de Enfermedades Raras , Madrid, Spain
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9
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Lacombe C, Untereiner V, Gobinet C, Zater M, Sockalingum GD, Garnotel R. Rapid screening of classic galactosemia patients: a proof-of-concept study using high-throughput FTIR analysis of plasma. Analyst 2015; 140:2280-6. [DOI: 10.1039/c4an01942c] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
FTIR as a new approach to screen a rare disease.
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Affiliation(s)
- Caroline Lacombe
- Université de Reims Champagne-Ardenne
- Equipe MéDIAN
- Biophotonique et Technologies pour la Santé
- UFR de Pharmacie
- 51096 Reims
| | - Valérie Untereiner
- Université de Reims Champagne-Ardenne
- Equipe MéDIAN
- Biophotonique et Technologies pour la Santé
- UFR de Pharmacie
- 51096 Reims
| | - Cyril Gobinet
- Université de Reims Champagne-Ardenne
- Equipe MéDIAN
- Biophotonique et Technologies pour la Santé
- UFR de Pharmacie
- 51096 Reims
| | - Mokhtar Zater
- Biochimie – Hôpital de Bicêtre
- Hôpitaux Universitaires Paris-Sud
- France
| | - Ganesh D. Sockalingum
- Université de Reims Champagne-Ardenne
- Equipe MéDIAN
- Biophotonique et Technologies pour la Santé
- UFR de Pharmacie
- 51096 Reims
| | - Roselyne Garnotel
- Université de Reims Champagne-Ardenne
- Equipe MéDIAN
- Biophotonique et Technologies pour la Santé
- UFR de Pharmacie
- 51096 Reims
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Zendehdel R, H. Shirazi F. Discrimination of Human Cell Lines by Infrared Spectroscopy and Mathematical Modeling. IRANIAN JOURNAL OF PHARMACEUTICAL RESEARCH : IJPR 2015; 14:803-10. [PMID: 26330868 PMCID: PMC4518108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Variations in biochemical features are extensive among cells. Identification of marker that is specific for each cell is essential for following the differentiation of stem cell and metastatic growing. Fourier transform infrared spectroscopy (FTIR) as a biochemical analysis more focused on diagnosis of cancerous cells. In this study, commercially obtained cell lines such as Human ovarian carcinoma (A2780), Human lung adenocarcinoma (A549) and Human hepatocarcinoma (HepG2) cell lines in 20 individual samples for each cell lines were used for FTIR spectral measurements. Data dimension were reduced through principal component analysis (PCA) and then subjected to neural network and linear discrimination analysis to classify FTIR pattern in different cell lines. The results showed dramatic changes of FTIR spectra among different cell types. These appeared to be associated with changes in lipid bands from CH2 symmetric and asymmetric bands, as well as amide I and amid II bands of proteins. The PCA-ANN analysis provided over 90% accuracy for classifying the spectrum of lipid section in different cell lines. This work supports future study to establish the data bank of FTIR feature for different cells and move forward to tissues as more complex systems.
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Affiliation(s)
- Rezvan Zendehdel
- Department of Occupational Hygiene, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Farshad H. Shirazi
- SBMU Pharmaceutical Research Center, Tehran, Iran.,Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Science, Tehran, Iran.,Corresponding author:
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