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Félix MM, Tavares MV, Santos IP, Batista de Carvalho ALM, Batista de Carvalho LAE, Marques MPM. Cervical Squamous Cell Carcinoma Diagnosis by FTIR Microspectroscopy. Molecules 2024; 29:922. [PMID: 38474435 DOI: 10.3390/molecules29050922] [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: 01/18/2024] [Revised: 02/09/2024] [Accepted: 02/17/2024] [Indexed: 03/14/2024] Open
Abstract
Cervical cancer was considered the fourth most common cancer worldwide in 2020. In order to reduce mortality, an early diagnosis of the tumor is required. Currently, this type of cancer occurs mostly in developing countries due to the lack of vaccination and screening against the Human Papillomavirus. Thus, there is an urgent clinical need for new methods aiming at a reliable screening and an early diagnosis of precancerous and cancerous cervical lesions. Vibrational spectroscopy has provided very good results regarding the diagnosis of various tumors, particularly using Fourier transform infrared microspectroscopy, which has proved to be a promising complement to the currently used histopathological methods of cancer diagnosis. This spectroscopic technique was applied to the analysis of cryopreserved human cervical tissue samples, both squamous cell carcinoma (SCC) and non-cancer samples. A dedicated Support Vector Machine classification model was constructed in order to categorize the samples into either normal or malignant and was subsequently validated by cross-validation, with an accuracy higher than 90%.
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Affiliation(s)
- Maria M Félix
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
| | - Mariana V Tavares
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
- Gynaecology Department, Portuguese Oncology Institute of Porto, 4200-072 Porto, Portugal
| | - Inês P Santos
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
| | - Ana L M Batista de Carvalho
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
| | - Luís A E Batista de Carvalho
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
| | - Maria Paula M Marques
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
- Department of Life Sciences, Faculty of Science and Technology, University of Coimbra, 3000-456 Coimbra, Portugal
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Aversano L, Bernardi ML, Cimitile M, Maiellaro A, Pecori R. A systematic review on artificial intelligence techniques for detecting thyroid diseases. PeerJ Comput Sci 2023; 9:e1394. [PMID: 37346658 PMCID: PMC10280452 DOI: 10.7717/peerj-cs.1394] [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: 10/28/2022] [Accepted: 04/21/2023] [Indexed: 06/23/2023]
Abstract
The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early. Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs. This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland. The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field. After a careful selection process, we selected and reviewed 72 papers, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection. The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo- and hyperthyroidism. The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common. Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages, and future developments in the application of artificial intelligence for thyroid diseases detection.
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Affiliation(s)
- Lerina Aversano
- Department of Engineering, University of Sannio, Benevento, Italy
| | | | - Marta Cimitile
- Dept. of Law and Digital Society, UnitelmaSapienza University, Rome, Italy
| | - Andrea Maiellaro
- Department of Engineering, University of Sannio, Benevento, Italy
| | - Riccardo Pecori
- Institute of Materials for Electronics and Magnetism, National Research Council, Parma, Italy
- SMARTEST Research Centre, eCampus University, Novedrate (CO), Italy
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3
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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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Villamanca JJ, Hermogino LJ, Ong KD, Paguia B, Abanilla L, Lim A, Angeles LM, Espiritu B, Isais M, Tomas RC, Albano PM. Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples. APPLIED SPECTROSCOPY 2022; 76:1412-1428. [PMID: 35821580 DOI: 10.1177/00037028221116083] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The early and accurate detection of colorectal cancer (CRC) significantly affects its prognosis and clinical management. However, current standard diagnostic procedures for CRC often lack sensitivity and specificity since most rely on visual examination. Hence, there is a need to develop more accurate methods for its diagnosis. Support vector machine (SVM) and feedforward neural network (FNN) models were designed using the Fourier transform infrared (FT-IR) spectral data of several colorectal tissues that were unanimously identified as either benign or malignant by different unrelated pathologists. The set of samples in which the pathologists had discordant readings were then analyzed using the AI models described above. Between the SVM and NN models, the NN model was able to outperform the SVM model based on their prediction confidence scores. Using the spectral data of the concordant samples as training set, the FNN was able to predict the histologically diagnosed malignant tissues (n = 118) at 59.9-99.9% confidence (average = 93.5%). Of the 118 samples, 84 (71.18%) were classified with an above average confidence score, 34 (28.81%) classified below the average confidence score, and none was misclassified. Moreover, it was able to correctly identify the histologically confirmed benign samples (n = 83) at 51.5-99.7% confidence (average = 91.64%). Of the 83 samples, 60 (72.29%) were classified with an above average confidence score, 22 (26.51%) classified below the average confidence score, and only 1 sample (1.20%) was misclassified. The study provides additional proof of the ability of attenuated total reflection (ATR) FT-IR enhanced by AI tools to predict the likelihood of CRC without dependence on morphological changes in tissues.
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Affiliation(s)
- John Jerald Villamanca
- Department of Biological Sciences, College of Science, 564927University of Santo Tomas, Manila, Philippines
| | - Lemuel John Hermogino
- Department of Biological Sciences, College of Science, 564927University of Santo Tomas, Manila, Philippines
| | - Katherine Denise Ong
- Department of Biological Sciences, College of Science, 564927University of Santo Tomas, Manila, Philippines
| | - Brian Paguia
- Department of Biological Sciences, College of Science, 564927University of Santo Tomas, Manila, Philippines
| | - Lorenzo Abanilla
- Department of Pathology, Divine Word Hospital, Tacloban City, Philippines
| | - Antonio Lim
- Department of Pathology, Divine Word Hospital, Tacloban City, Philippines
| | - Lara Mae Angeles
- Department of Pathology, 596481University of Santo Tomas Hospital, Manila, Philippines
| | - Bernadette Espiritu
- Department of Pathology, 603332Bulacan Medical Center, Malolos City, Philippines
| | - Maura Isais
- Department of Pathology, 603332Bulacan Medical Center, Malolos City, Philippines
- The Graduate School, 595547University of Santo Tomas, Manila, Philippines
| | - Rock Christian Tomas
- Department of Electrical Engineering, 54729University of the Philippines Los Baños, Los Baños, Philippines
| | - Pia Marie Albano
- Department of Biological Sciences, College of Science, 564927University of Santo Tomas, Manila, Philippines
- Department of Pathology, Divine Word Hospital, Tacloban City, Philippines
- Research Center for the Natural and Applied Sciences, 564927University of Santo Tomas, Manila, Philippines
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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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Tomas RC, Sayat AJ, Atienza AN, Danganan JL, Ramos MR, Fellizar A, Notarte KI, Angeles LM, Bangaoil R, Santillan A, Albano PM. Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks. PLoS One 2022; 17:e0262489. [PMID: 35081148 PMCID: PMC8791515 DOI: 10.1371/journal.pone.0262489] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 12/27/2021] [Indexed: 12/27/2022] Open
Abstract
In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to determine the most significant IR regions that distinguished benign from malignant samples. The result of the NN-based sensitivity analysis was compared to the obtained results from FTIR visual peak identification. In training each NN models, a 10-fold cross validation was performed and the performance metrics-area under the curve (AUC), accuracy, positive predictive value (PPV), specificity rate (SR), negative predictive value (NPV), and recall rate (RR)-were averaged for comparison. The NN models were compared to six (6) machine learning models-logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA)-for benchmarking. The NN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics; while only surpassing the SVM in accuracy, NPV and SR. The best performance metric among the NN models was 90.48% ± 10.30% for AUC, 96.06% ± 7.07% for ACC, 92.18 ± 11.88% for PPV, 94.19 ± 10.57% for NPV, 89.04% ± 16.75% for SR, and 94.34% ± 10.54% for RR. Results from the proposed sensitivity analysis were consistent with the visual peak identification. However, unlike the FTIR visual peak identification method, the NN-based method identified the IR region associated with C-OH C-OH group carbohydrates as significant. IR regions associated with amino acids and amide proteins were also determined as possible sources of variability. In conclusion, results show that ATR-FTIR via NN is a potential diagnostic tool. This study also suggests a possible more specific method in determining relevant regions within a sample's spectrum using NN.
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Affiliation(s)
- Rock Christian Tomas
- Department of Electrical Engineering, University of the Philippines Los Baños, Los Baños, Laguna, Philippines
| | - Anthony Jay Sayat
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Andrea Nicole Atienza
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Jannah Lianne Danganan
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Ma. Rollene Ramos
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Allan Fellizar
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- The Graduate School, University of Santo Tomas, Manila, Philippines
- Mariano Marcos Memorial Hospital and Medical Center, Batac, Ilocos Norte, Philippines
| | - Kin Israel Notarte
- Faculty of Medicine and Surgery, University of Santo Tomas, Manila, Philippines
| | - Lara Mae Angeles
- Faculty of Medicine and Surgery, University of Santo Tomas, Manila, Philippines
- University of Santo Tomas Hospital, 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
| | - 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
| | - Pia Marie Albano
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
- Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
- The Graduate School, University of Santo Tomas, Manila, Philippines
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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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