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Yi C, Zhang Z, Huang T, Xiao H. Identification of liquor adulteration by Raman spectroscopy method based on ICNAFS. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 312:124068. [PMID: 38417234 DOI: 10.1016/j.saa.2024.124068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/01/2024] [Accepted: 02/20/2024] [Indexed: 03/01/2024]
Abstract
The health of consumers can be impacted by the additives placed into the liquor. To address the issues of poor accuracy, low reliability, and complex operational procedures in identifying adulteration in existing liquor, an improved convex non-negative matrix factorization (ICNAFS) with an adaptive graph constraint for unsupervised feature extraction is proposed in this paper, with the goal of achieving rapid identification of adulteration in liquor by Raman spectroscopy through dimensionality reduction. For the sake to streamline the calculation process for effective feature extraction and increase the accuracy of the analyzed model, the proposed ICNAFS method incorporates two fundamental models, such as ridge regression and convex non-negative matrix factorization (NMF). In particular, dimensionality reduction of the original spectrum is initially conducted using Principal Component Analysis (PCA), Sequential Projection Algorithm (SPA), Convex Non-Negative Matrix Factorization with an Adaptive Graph Constraint (CNAFS), and ICNAFS respectively. k-means is subsequently employed to merge the four models for clustering analysis. The results suggest that the accuracy of the presented ICNAFS-assisted k-means model is higher than the other techniques, with a clustering accuracy of 98.67%, exhibiting a 4% improvement over the existing CNAFS, through examination of 150 sets of tainted liquor data from five categories of samples. This demonstrates the potency of the proposed ICNAFS-assisted k-means clustering model in conjunction with Raman spectroscopy as a method for detecting tainted liquor.
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Affiliation(s)
- Cancan Yi
- Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China.
| | - Zhenyu Zhang
- Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Tao Huang
- Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Han Xiao
- Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan 430081, China
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2
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Budiman A, Wardhana YW, Ainurofiq A, Nugraha YP, Qaivani R, Hakim SNAL, Aulifa DL. Drug-Coformer Loaded-Mesoporous Silica Nanoparticles: A Review of the Preparation, Characterization, and Mechanism of Drug Release. Int J Nanomedicine 2024; 19:281-305. [PMID: 38229702 PMCID: PMC10790662 DOI: 10.2147/ijn.s449159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 12/19/2023] [Indexed: 01/18/2024] Open
Abstract
Drug-coformer systems, such as coamorphous and cocrystal, are gaining recognition as highly effective strategies for enhancing the stability, solubility, and dissolution of drugs. These systems depend on the interactions between drug and coformer to prevent the conversion of amorphous drugs into the crystalline form and improve the solubility. Furthermore, mesoporous silica (MPS) is also a promising carrier commonly used for stabilization, leading to solubility improvement of poorly water-soluble drugs. The surface interaction of drug-MPS and the nanoconfinement effect prevent amorphous drugs from crystallizing. A novel method has been developed recently, which entails the loading of drug-coformer into MPS to improve the solubility, dissolution, and physical stability of the amorphous drug. This method uses the synergistic effects of drug-coformer interactions and the nanoconfinement effect within MPS. Several studies have reported successful incorporation of drug-coformer into MPS, indicating the potential for significant improvement in dissolution characteristics and physical stability of the drug. Therefore, this study aimed to discuss the preparation and characterization of drug-coformer within MPS, particularly the interaction in the nanoconfinement, as well as the impact on drug release and physical stability.
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Affiliation(s)
- Arif Budiman
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Bandung, West Java45363, Indonesia
| | - Yoga Windhu Wardhana
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Bandung, West Java45363, Indonesia
| | - Ahmad Ainurofiq
- Pharmaceutical Technology and Drug Delivery, Department of Pharmacy, Universitas Sebelas Maret, Surakarta, Central Java, 57126, Indonesia
| | - Yuda Prasetya Nugraha
- School of Pharmacy, Bandung Institute of Technology, Bandung, West Java, 40132, Indonesia
| | - Ridhatul Qaivani
- Department of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Universitas Padjadjaran, Bandung, West Java, 45363, Indonesia
| | - Siti Nazila Awaliyyah Lukmanul Hakim
- Department of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Universitas Padjadjaran, Bandung, West Java, 45363, Indonesia
| | - Diah Lia Aulifa
- Department of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Universitas Padjadjaran, Bandung, West Java, 45363, Indonesia
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3
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Ferguson C, Zhang Y, Palego C, Cheng X. Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:5990. [PMID: 37447838 DOI: 10.3390/s23135990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/17/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.
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Affiliation(s)
- Caroline Ferguson
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Cristiano Palego
- Department of Computer Science and Electronic Engineering, Bangor University, Bangor LL57 2DG, UK
| | - Xuanhong Cheng
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
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4
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Karnachoriti M, Stathopoulos I, Kouri M, Spyratou E, Orfanoudakis S, Lykidis D, Lambropoulou Μ, Danias N, Arkadopoulos N, Efstathopoulos EP, Raptis YS, Seimenis I, Kontos AG. Biochemical differentiation between cancerous and normal human colorectal tissues by micro-Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 299:122852. [PMID: 37216817 DOI: 10.1016/j.saa.2023.122852] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/29/2023] [Accepted: 05/08/2023] [Indexed: 05/24/2023]
Abstract
Human colorectal tissues obtained by ten cancer patients have been examined by multiple micro-Raman spectroscopic measurements in the 500-3200 cm-1 range under 785 nm excitation. Distinct spectral profiles are recorded from different spots on the samples: a predominant 'typical' profile of colorectal tissue, as well as those from tissue topologies with high lipid, blood or collagen content. Principal component analysis identified several Raman bands of amino acids, proteins and lipids which allow the efficient discrimination of normal from cancer tissues, the first presenting plurality of Raman spectral profiles while the last showing off quite uniform spectroscopic characteristics. Tree-based machine learning experiment was further applied on all data as well as on filtered data keeping only those spectra which characterize the largely inseparable data clusters of 'typical' and 'collagen-rich' spectra. This purposive sampling evidences statistically the most significant spectroscopic features regarding the correct identification of cancer tissues and allows matching spectroscopic results with the biochemical changes induced in the malignant tissues.
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Affiliation(s)
- M Karnachoriti
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece; Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - I Stathopoulos
- 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - M Kouri
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece; Medical Physics Program, University of Massachusetts Lowell, MA 01854, United States
| | - E Spyratou
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - S Orfanoudakis
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece; Alpha Information Technology S.A., Software & System Development, 68131 Alexandroupolis, Greece
| | - D Lykidis
- Laboratory of Histology-Embryology, Medical Department, Democritus University of Thrace, Alexandroupolis, Greece
| | - Μ Lambropoulou
- Laboratory of Histology-Embryology, Medical Department, Democritus University of Thrace, Alexandroupolis, Greece
| | - N Danias
- 4(th) Department of Surgery, School of Medicine, Attikon University Hospital, Univ. of Athens, 12462 Athens, Greece
| | - N Arkadopoulos
- 4(th) Department of Surgery, School of Medicine, Attikon University Hospital, Univ. of Athens, 12462 Athens, Greece
| | - E P Efstathopoulos
- 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Y S Raptis
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece
| | - I Seimenis
- Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - A G Kontos
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece.
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González-Viveros N, Castro-Ramos J, Gómez-Gil P, Cerecedo-Núñez HH, Gutiérrez-Delgado F, Torres-Rasgado E, Pérez-Fuentes R, Flores-Guerrero JL. Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks. Lasers Med Sci 2022; 37:3537-3549. [PMID: 36063232 PMCID: PMC9708775 DOI: 10.1007/s10103-022-03633-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 08/14/2022] [Indexed: 01/17/2023]
Abstract
Undiagnosed type 2 diabetes (T2D) remains a major public health concern. The global estimation of undiagnosed diabetes is about 46%, being this situation more critical in developing countries. Therefore, we proposed a non-invasive method to quantify glycated hemoglobin (HbA1c) and glucose in vivo. We developed a technique based on Raman spectroscopy, RReliefF as a feature selection method, and regression based on feed-forward artificial neural networks (FFNN). The spectra were obtained from the forearm, wrist, and index finger of 46 individuals. The use of FFNN allowed us to achieve an error in the predictive model of 0.69% for HbA1c and 30.12 mg/dL for glucose. Patients were classified according to HbA1c values into three categories: healthy, prediabetes, and T2D. The proposed method obtained a specificity and sensitivity of 87.50% and 80.77%, respectively. This work demonstrates the benefit of using artificial neural networks and feature selection techniques to enhance Raman spectra processing to determine glycated hemoglobin and glucose in patients with undiagnosed T2D.
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Affiliation(s)
- Naara González-Viveros
- Optics Coordination, National Institute of Astrophysics, Optics and Electronics (INAOE), 72840, Puebla, Mexico
| | - Jorge Castro-Ramos
- Optics Coordination, National Institute of Astrophysics, Optics and Electronics (INAOE), 72840, Puebla, Mexico
| | - Pilar Gómez-Gil
- Computer Science Coordination, National Institute of Astrophysics, Optics and Electronics (INAOE), 72840, Puebla, Mexico
| | | | | | - Enrique Torres-Rasgado
- Faculty of Medicine, Meritorious Autonomous University of Puebla (BUAP), 72589, Puebla, Mexico
| | - Ricardo Pérez-Fuentes
- Department of Chronic Disease Physiopathology, East Center of Biomedical Research, Mexican Social Security Institute (CIBIOR), 74360, Puebla, México
| | - Jose L Flores-Guerrero
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, WC1E 7HB, UK.
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6
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Raman spectroscopy combined with machine learning algorithms for rapid detection Primary Sjögren's syndrome associated with interstitial lung disease. Photodiagnosis Photodyn Ther 2022; 40:103057. [PMID: 35944848 DOI: 10.1016/j.pdpdt.2022.103057] [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: 05/27/2022] [Revised: 07/15/2022] [Accepted: 08/05/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Interstitial lung disease (ILD) is a major complication of Primary Sjögren's syndrome (pSS) patients.It is one of the main factors leading to death. The aim of this study is to evaluate the value of serum Raman spectroscopy combined with machine learning algorithms in the discriminatory diagnosis of patients with Primary Sjögren's syndrome associated with interstitial lung disease (pSS-ILD). METHODS Raman spectroscopy was performed on the serum of 30 patients with pSS, 28 patients with pSS-ILD and 30 healthy controls (HC). First, the data were pre-processed using baseline correction, smoothing, outlier removal and normalization operations. Then principal component analysis (PCA) is used to reduce the dimension of data. Finally, support vector machine(SVM), k nearest neighbor (KNN) and random forest (RF) models are established for classification. RESULTS In this study, SVM, KNN and RF were used as classification models, where SVM chooses polynomial kernel function (poly). The average accuracy, sensitivity, and precision of the three models were obtained after dimensionality reduction. The Accuracy of SVM (poly) was 5.71% higher than KNN and 6.67% higher than RF; Sensitivity was 5.79% higher than KNN and 8.56% higher than RF; Precision was 6.19% higher than KNN and 7.45% higher than RF. It can be seen that the SVM (poly) had better discriminative effect. In summary, SVM (poly) had a fine classification effect, and the average accuracy, sensitivity and precision of this model reached 89.52%, 91.27% and 89.52%, respectively, with an AUC value of 0.921. CONCLUSIONS This study demonstrates that serum RS combined with machine learning algorithms is a valuable tool for diagnosing patients with pSS-ILD. It has promising applications.
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7
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Law M, Jarrett P, Nieuwoudt MK, Holtkamp H, Giglio C, Broadbent E. The Effects of Interacting With a Paro Robot After a Stressor in Patients With Psoriasis: A Randomised Pilot Study. Front Psychol 2022; 13:871295. [PMID: 35645866 PMCID: PMC9133624 DOI: 10.3389/fpsyg.2022.871295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
Objective Stress can play a role in the onset and exacerbation of psoriasis. Psychological interventions to reduce stress have been shown to improve psychological and psoriasis-related outcomes. This pilot randomised study investigated the feasibility of a brief interaction with a Paro robot to reduce stress and improve skin parameters, after a stressor, in patients with psoriasis. Methods Around 25 patients with psoriasis participated in a laboratory stress task, before being randomised to either interact with a Paro robot or sit quietly (control condition) for 30 min. Raman spectroscopy and trans-epidermal water loss were measured at baseline, after the stressor and after the intervention as indexes of acute skin changes. Psychological variables, including self-reported stress and affect, were also measured at the three time-points. Results No statistically significant differences between the two conditions were found for any of the outcomes measured. However, effect sizes suggest significance could be possible with a larger sample size. Changes in the psychological and Raman spectroscopy outcomes across the experimental session were found, indicating the feasibility of the procedures. Conclusion This pilot study showed that a brief interaction with a Paro robot was a feasible intervention for patients with psoriasis, but future trials should broaden the inclusion criteria to try to increase recruitment rates. Studying people who are highly stressed, depressed or who are stress-responders may increase the power of the intervention to show effects using a longer-term intervention.
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Affiliation(s)
- Mikaela Law
- Department of Psychological Medicine, The University of Auckland, Auckland, New Zealand
| | - Paul Jarrett
- Department of Dermatology, Middlemore Hospital, Auckland, New Zealand.,Department of Medicine, The University of Auckland, Auckland, New Zealand
| | - Michel K Nieuwoudt
- The Photon Factory, The University of Auckland, Auckland, New Zealand.,School of Chemical Sciences, The University of Auckland, Auckland, New Zealand.,The MacDiarmid Institute for Advanced Materials and Nanotechnology, Wellington, New Zealand.,The Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
| | - Hannah Holtkamp
- The Photon Factory, The University of Auckland, Auckland, New Zealand.,School of Chemical Sciences, The University of Auckland, Auckland, New Zealand.,The MacDiarmid Institute for Advanced Materials and Nanotechnology, Wellington, New Zealand.,The Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
| | - Cannon Giglio
- The Photon Factory, The University of Auckland, Auckland, New Zealand.,School of Chemical Sciences, The University of Auckland, Auckland, New Zealand
| | - Elizabeth Broadbent
- Department of Psychological Medicine, The University of Auckland, Auckland, New Zealand
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Dai Y, Wang L, Luo C, Li W, Huang Q, Li W, Pang L. Featuring few essential Raman spectroscopic signatures between heterogeneous cells. JOURNAL OF BIOPHOTONICS 2022; 15:e202100338. [PMID: 34995013 DOI: 10.1002/jbio.202100338] [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: 11/02/2021] [Revised: 12/31/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2023]
Abstract
Here we demonstrate it is instructive to quantify cell Raman spectroscopy by sparse regularization. To be able to extract the specific spectral differences in a heterogeneous cell system with great spectroscopic similarities derived from many common biomolecular components, the maximum information entropy probability was proposed and exemplified by identifying normal lymphocytes from leukemia cells. The essential spectroscopic features were observed to locate at three Raman peaks whose spectral signatures were commensurate. The applicability of the extracted features was acknowledged by that the predicted identification accuracy of up to 93% was still achieved when only two peaks were loaded into decision tree model, which may provide the possibility of a clinically rapid hematological malignancy detection.
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Affiliation(s)
- Yixin Dai
- College of Physics, Sichuan University, Chengdu, China
| | - Liu Wang
- Deparment of Laboratory Medicine, Army Medical University Daping Hospital, Chongqing, China
| | - Chuan Luo
- Deparment of Laboratory Medicine, Army Medical University Southwest Hospital, Chongqing, China
| | - Wenkang Li
- College of Physics, Sichuan University, Chengdu, China
| | - Qing Huang
- Deparment of Laboratory Medicine, Army Medical University Daping Hospital, Chongqing, China
| | - Wenxue Li
- College of Physics, Sichuan University, Chengdu, China
- School of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Lin Pang
- College of Physics, Sichuan University, Chengdu, China
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He C, Zhu S, Wu X, Zhou J, Chen Y, Qian X, Ye J. Accurate Tumor Subtype Detection with Raman Spectroscopy via Variational Autoencoder and Machine Learning. ACS OMEGA 2022; 7:10458-10468. [PMID: 35382336 PMCID: PMC8973095 DOI: 10.1021/acsomega.1c07263] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 03/09/2022] [Indexed: 05/04/2023]
Abstract
Accurate diagnosis of cancer subtypes is a great guide for the development of surgical plans and prognosis in the clinic. Raman spectroscopy, combined with the machine learning algorithm, has been demonstrated to be a powerful tool for tumor identification. However, the analysis and classification of Raman spectra for biological samples with complex compositions are still challenges. In addition, the signal-to-noise ratio of the spectra also influences the accuracy of the classification. Herein, we applied the variational autoencoder (VAE) to Raman spectra for downscaling and noise reduction simultaneously. We validated the performance of the VAE algorithm at the cellular and tissue levels. VAE successfully downscaled high-dimensional Raman spectral data to two-dimensional (2D) data for three subtypes of non-small cell lung cancer cells and two subtypes of kidney cancer tissues. Gaussian naïve bayes was applied to subtype discrimination with the 2D data after VAE encoding at both the cellular and tissue levels, significantly outperforming the discrimination results using original spectra. Therefore, the analysis of Raman spectroscopy based on VAE and machine learning has great potential for rapid diagnosis of tumor subtypes.
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Affiliation(s)
- Chang He
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Shuo Zhu
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Xiaorong Wu
- Department
of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
| | - Jiale Zhou
- Department
of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
| | - Yonghui Chen
- Department
of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
| | - Xiaohua Qian
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Jian Ye
- State
Key Laboratory of Oncogenes and Related Genes, School of Biomedical
Engineering, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
- Shanghai
Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of
Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China
- Institute
of Medical Robotics, Shanghai Jiao Tong
University, Shanghai 200240, P.R. China
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10
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Kouri MA, Spyratou E, Karnachoriti M, Kalatzis D, Danias N, Arkadopoulos N, Seimenis I, Raptis YS, Kontos AG, Efstathopoulos EP. Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians' Hands for In Situ Cancer Diagnosis and Surgery Guidance. Cancers (Basel) 2022; 14:1144. [PMID: 35267451 PMCID: PMC8909093 DOI: 10.3390/cancers14051144] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/04/2022] [Accepted: 02/07/2022] [Indexed: 12/23/2022] Open
Abstract
Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic "molecular finger-print" of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.
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Affiliation(s)
- Maria Anthi Kouri
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (M.A.K.); (E.S.); (M.K.)
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
- Medical Physics Program, Department of Physics and Applied Physics, Kennedy College of Sciences, University of Massachusetts Lowell, 265 Riverside Street, Lowell, MA 01854, USA
| | - Ellas Spyratou
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (M.A.K.); (E.S.); (M.K.)
- Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Iroon Politechniou 9, 15780 Athens, Greece; (Y.S.R.); (A.G.K.)
| | - Maria Karnachoriti
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (M.A.K.); (E.S.); (M.K.)
- Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Iroon Politechniou 9, 15780 Athens, Greece; (Y.S.R.); (A.G.K.)
| | - Dimitris Kalatzis
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Nikolaos Danias
- 4th Department of Surgery, School of Medicine, Attikon University Hospital, University of Athens, 1 Rimini Street, 12462 Athens, Greece; (N.D.); (N.A.)
| | - Nikolaos Arkadopoulos
- 4th Department of Surgery, School of Medicine, Attikon University Hospital, University of Athens, 1 Rimini Street, 12462 Athens, Greece; (N.D.); (N.A.)
| | - Ioannis Seimenis
- Medical School, National and Kapodistrian University of Athens, 75 Mikras Assias Street, 11527 Athens, Greece;
| | - Yannis S. Raptis
- Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Iroon Politechniou 9, 15780 Athens, Greece; (Y.S.R.); (A.G.K.)
| | - Athanassios G. Kontos
- Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Iroon Politechniou 9, 15780 Athens, Greece; (Y.S.R.); (A.G.K.)
| | - Efstathios P. Efstathopoulos
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
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11
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Dai Y, Li W, Wang L, Luo C, Huang Q, Pang L. Correlation and Difference Between Raman Spectral Characteristic and Feature Evaluation for Leukocytes and Tumor Cells. APPLIED SPECTROSCOPY 2021; 75:1516-1525. [PMID: 34643137 DOI: 10.1177/00037028211050663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Tumor detection supported by Raman spectroscopy is becoming increasingly popular, yet the relevance of spectral variation and feature selection retains unclear. Here we determined the correlation and difference between spectral characteristic and feature evaluation for leukocytes and tumor cells. Some peaks were found to show noticeable spectral differences, and their intensity distributions were investigated, finding using log-normal distribution to describe Raman intensity pattern may be more appropriate. Further the importance of all Raman features was calculated, where some other peak features occupied the top status. By surveying the intensity variation and feature evaluation for those peaks, we concluded the peak with the highest importance does not correspond to the peak location with the most noticeable intensity difference in spectra. Moreover, the peak intensity ratio of I1517/I719 associated with protein to nucleic acid level presented the maximum separation, thus, it can be recognized as a special indicator to develop an alternative cancer detection. It is inspiring to introduce advanced statistical models into bio-spectroscopic fields but those intrinsic spectral variations rather than classification performance should be valued. Our explorations can provide possibilities to reveal the essences within tumor carcinogenesis based on Raman spectroscopy, further overwhelming the obstacles during the translation into clinical applications.
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Affiliation(s)
- Yixin Dai
- College of Physics, Sichuan University, Chengdu, China
| | - Wenxue Li
- College of Physics, Sichuan University, Chengdu, China
| | - Liu Wang
- Department of Laboratory Medicine, Army Medical University Daping Hospital, Chongqing, China
| | - Chuan Luo
- Department of Laboratory Medicine, Army Medical University Southwest Hospital, Chongqing, China
| | - Qing Huang
- Department of Laboratory Medicine, Army Medical University Daping Hospital, Chongqing, China
| | - Lin Pang
- College of Physics, Sichuan University, Chengdu, China
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12
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Ex Vivo Vibration Spectroscopic Analysis of Colorectal Polyps for the Early Diagnosis of Colorectal Carcinoma. Diagnostics (Basel) 2021; 11:diagnostics11112048. [PMID: 34829393 PMCID: PMC8621094 DOI: 10.3390/diagnostics11112048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 10/25/2021] [Accepted: 10/31/2021] [Indexed: 02/01/2023] Open
Abstract
Colorectal cancer is one of the most common and often fatal cancers in humans, but it has the highest chance of a cure if detected at an early precancerous stage. Carcinogenesis in the colon begins as an uncontrolled growth forming polyps. Some of these polyps can finally be converted to colon cancer. Early diagnosis of adenomatous polyps is the main approach for screening and preventing colorectal cancer, and vibration spectroscopy can be used for this purpose. This work is focused on evaluating FTIR and Raman spectroscopy as a tool in the ex vivo analysis of colorectal polyps, which could be important for the early diagnosis of colorectal carcinoma. Multivariate analyses (PCA and LDA) were used to assist the spectroscopic discrimination of normal colon tissue, as well as benign and malignant colon polyps. The spectra demonstrated evident differences in the characteristic bands of the main tissue constituents, i.e., proteins, nucleic acids, lipids, polysaccharides, etc. Suitable models for discriminating the three mentioned diagnostic groups were proposed based on multivariate analyses of the spectroscopic data. LDA classification was especially successful in the case of a combined set of 55 variables from the FTIR, FT Raman and dispersion Raman spectra. This model can be proposed for ex vivo colorectal cancer diagnostics in combination with the colonoscopic extraction of colon polyps for further testing. This pilot study is a precursor for the further evaluation of the diagnostic potential for the simultaneous in vivo application of colonoscopic Raman probes.
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Huang X, Song D, Li J, Qin J, Wang D, Li J, Wang H, Wang S. Validating Multivariate Classification Algorithms in Raman Spectroscopy-Based Osteosarcoma Cellular Analysis. ANAL LETT 2021. [DOI: 10.1080/00032719.2021.1982959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Xiaojun Huang
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi’an, Shaanxi, China
| | - Dongliang Song
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi’an, Shaanxi, China
| | - Jie Li
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi’an, Shaanxi, China
| | - Jie Qin
- Department of Orthopedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Difan Wang
- School of Life, Xidian University, Xi'an, Shaanxi, China
| | - Jing Li
- Department of Orthopedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Haifeng Wang
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi’an, Shaanxi, China
| | - Shuang Wang
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi’an, Shaanxi, China
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Deng L, Zhong Y, Wang M, Zheng X, Zhang J. Scale-adaptive Deep Model for Bacterial Raman Spectra Identification. IEEE J Biomed Health Inform 2021; 26:369-378. [PMID: 34543211 DOI: 10.1109/jbhi.2021.3113700] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The combination of Raman spectroscopy and deep learning technology provides an automatic, rapid, and accurate scheme for the clinical diagnosis of pathogenic bacteria. However, the accuracy of existing deep learning methods is still limited because of the single and fixed scales of deep neural networks. We propose a deep neural network that can learn multi-scale features of Raman spectra by using the automatic combination of multi-receptive fields of convolutional layers. This model is based on the expert knowledge that the discrimination information of Raman spectra is composed of multi-scale spectral peaks. We enhance the interpretability of the model by visualizing the activated wavenumbers of the bacterial spectrum that can be used for reference in related work. Compared with existing state-of-the-art methods, the proposed method achieves higher accuracy and efficiency for bacterial identification on isolate-level, empiric-treatment-level, and antibiotic-resistance-level tasks. The clinical bacterial identification task requires significantly fewer patient samples to achieve similar accuracy. Therefore, this method has tremendous potential for the identification of clinical pathogenic bacteria, antibiotic susceptibility testing, and prescription guidance.
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15
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Zhao Y, Tian S, Yu L, Zhang Z, Zhang W. Analysis and Classification of Hepatitis Infections Using Raman Spectroscopy and Multiscale Convolutional Neural Networks. JOURNAL OF APPLIED SPECTROSCOPY 2021; 88:441-451. [PMID: 33972806 PMCID: PMC8099702 DOI: 10.1007/s10812-021-01192-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Hepatitis infections represent a major health concern worldwide. Numerous computer-aided approaches have been devised for the early detection of hepatitis. In this study, we propose a method for the analysis and classification of cases of hepatitis-B virus ( HBV), hepatitis-C virus (HCV), and healthy subjects using Raman spectroscopy and a multiscale convolutional neural network (MSCNN). In particular, serum samples of HBV-infected patients (435 cases), HCV-infected patients (374 cases), and healthy persons (499 cases) are analyzed via Raman spectroscopy. The differences between Raman peaks in the measured serum spectra indicate specific biomolecular differences among the three classes. The dimensionality of the spectral data is reduced through principal component analysis. Subsequently, features are extracted, and then feature normalization is applied. Next, the extracted features are used to train different classifiers, namely MSCNN, a single-scale convolutional neural network, and other traditional classifiers. Among these classifiers, the MSCNN model achieved the best outcomes with a precision of 98.89%, sensitivity of 97.44%, specificity of 94.54%, and accuracy of 94.92%. Overall, the results demonstrate that Raman spectral analysis and MSCNN can be effectively utilized for rapid screening of hepatitis B and C cases.
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Affiliation(s)
- Y. Zhao
- Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000 China
| | - Sh. Tian
- Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000 China
| | - L. Yu
- College of Software Engineering at Xin Jiang University, Urumqi, 830000 China
| | - Zh. Zhang
- The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000 China
| | - W. Zhang
- Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000 China
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Ke ZY, Ning YJ, Jiang ZF, Zhu YY, Guo J, Fan XY, Zhang YB. The efficacy of Raman spectroscopy in lung cancer diagnosis: the first diagnostic meta-analysis. Lasers Med Sci 2021; 37:425-434. [PMID: 33856584 DOI: 10.1007/s10103-021-03275-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 02/10/2021] [Indexed: 01/05/2023]
Abstract
In recent years, many researches have explored the diagnostic value of Raman spectroscopy in multiple types of tumors. However, as an emerging clinical examination method, the diagnostic performance of Raman spectroscopy in lung cancer remains unclear. Relevant diagnostic studies published before 1 June 2020 were retrieved from the Cochrane Library, PubMed, EMBASE, China National Knowledge Internet (CNKI), and WanFang databases. After the literature was screened, two authors extracted the data from eligible studies according to the inclusion and exclusion criteria. Obtained data were pooled and analyzed using Stata 16.0, Meta-DiSc 1.4, and RevMan 5.3 software. Fourteen diagnostic studies were eligible for the pooled analysis which includes 779 patients. Total pooled sensitivity and specificity of Raman spectroscopy in diagnosing lung cancer were 0.92 (95% CI 0.87-0.95) and 0.94 (95% CI 0.88-0.97), respectively. The positive likelihood ratio was 15.2 (95% CI 7.5-30.9), the negative likelihood ratio was 0.09 (95% CI 0.05-0.14), and the area under the curve was 0.97 (95 % CI 0.95-0.98). Subgroup analysis suggested that the sensitivity and specificity of RS when analyzing human tissue, serum, and saliva samples were 0.95 (95% CI 0.88-0.98), 0.97 (95% CI 0.89-0.99), 0.88 (95% CI 0.80-0.93), 0.87 (95% CI 0.78-0.92), 0.91 (95% CI 0.80-0.96), and 0.95 (95% CI 0.73-0.99), respectively. No publication bias or threshold effects were detected in this meta-analysis. This initial meta-analysis indicated that Raman spectroscopy is a highly specific and sensitive diagnostic technology for detecting lung cancer. Further investigations are also needed to focus on real-time detection using Raman spectroscopy under bronchoscopy in vivo. Moreover, large-scale diagnostic studies should be conducted to confirm this conclusion.
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Affiliation(s)
- Zhang-Yan Ke
- Department of Geriatric Respiratory and Critical Care, Institute of Respiratory Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China
| | - Ya-Jing Ning
- Department of Geriatric Respiratory and Critical Care, Institute of Respiratory Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China
| | - Zi-Feng Jiang
- Department of Geriatric Respiratory and Critical Care, Institute of Respiratory Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China
| | - Ying-Ying Zhu
- Department of Geriatric Respiratory and Critical Care, Institute of Respiratory Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China
| | - Jia Guo
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
| | - Xiao-Yun Fan
- Department of Geriatric Respiratory and Critical Care, Institute of Respiratory Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China.
| | - Yan-Bei Zhang
- Department of Geriatric Respiratory and Critical Care, Institute of Respiratory Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, People's Republic of China.
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17
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Chen F, Chen C, Li W, Xiao M, Yang B, Yan Z, Gao R, Zhang S, Han H, Chen C, Lv X. Rapid detection of seven indexes in sheep serum based on Raman spectroscopy combined with DOSC-SPA-PLSR-DS model. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 248:119260. [PMID: 33307346 DOI: 10.1016/j.saa.2020.119260] [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: 09/02/2020] [Revised: 10/25/2020] [Accepted: 11/23/2020] [Indexed: 06/12/2023]
Abstract
Hepatic fascioliasis, ketosis of pregnancy, toxemia of pregnancy and other common sheep diseases will directly affect the concentration (/enzymatic activity) of seven indicators, such as cortisol and high-density lipoprotein cholesterol (HDL-C) in sheep serum. Whether the concentrations (/enzymatic activity) of these indicators can be detected quickly will directly affect the prevention of sheep diseases and the targeted adjustment of breeding methods, thereby affecting the economic benefits of sheep breeding. In this research, we established partial least square regression (PLSR), support vector regression based on genetic algorithm optimization (GA-SVR) and extreme learning machine (ELM) models. Due to the large differences in the content of different substances, it is difficult to directly use the RMSE to evaluate the quantitative effect of the model. This study is the first to propose conducting deviation standardization (DS) for the determination results of various substances. To further improve the performance of the model, we use the successive projections algorithm (SPA) to optimize feature extraction and combine it with the better-performing PLSR model for training. The results show that the optimized DOSC-SPA-PLSR-DS quantitative model has better determination results for 101 sheep serum samples. The average RMSEp* of the concentration of the six substances decreased from 0.0408 to 0.0387, the Rp2 increased from 0.9758 to 0.9846, and the running time was reduced from 0.1659 to 0.0008 s. And the determination performance of lipase (LPS) enzymatic activity has also been improved. The results of this research show that sheep serum Raman spectroscopy combined with DOSC-SPA-PLSR-DS optimization can efficiently monitor the concentration (/enzyme activity) of seven indicators in real time and provide a new strategy for future intelligent supervision of animal husbandry.
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Affiliation(s)
- Fangfang Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Wenrong Li
- Key Laboratory of Genetics, Breeding & Reproduction of Grass-Feeding Livestock, Ministry of Agriculture, Urumqi 830000, China; Key Laboratory of Animal Biotechnology of Xinjiang Institute of Animal Biotechnology, Xinjiang Academy of Animal Science, Urumqi 830000, China
| | - Meng Xiao
- The Fourth People's Hospital in Urumqi, Urumqi 830002, China
| | - Bo Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Ziwei Yan
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Rui Gao
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Shuailei Zhang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Huijie Han
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Cheng Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China.
| | - Xiaoyi Lv
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China; College of Software, Xinjiang University, Urumqi 830002, China.
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18
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Nogueira MS, Maryam S, Amissah M, Lu H, Lynch N, Killeen S, O’Riordain M, Andersson-Engels S. Evaluation of wavelength ranges and tissue depth probed by diffuse reflectance spectroscopy for colorectal cancer detection. Sci Rep 2021; 11:798. [PMID: 33436684 PMCID: PMC7804163 DOI: 10.1038/s41598-020-79517-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 12/04/2020] [Indexed: 01/29/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common type of cancer worldwide and the second most deadly. Recent research efforts have focused on developing non-invasive techniques for CRC detection. In this study, we evaluated the diagnostic capabilities of diffuse reflectance spectroscopy (DRS) for CRC detection by building 6 classification models based on support vector machines (SVMs). Our dataset consists of 2889 diffuse reflectance spectra collected from freshly excised ex vivo tissues of 47 patients over wavelengths ranging from 350 and 1919 nm with source-detector distances of 630-µm and 2500-µm to probe different depths. Quadratic SVMs were used and performance was evaluated using twofold cross-validation on 10 iterations of randomized training and test sets. We achieved (93.5 ± 2.4)% sensitivity, (94.0 ± 1.7)% specificity AUC by probing the superficial colorectal tissue and (96.1 ± 1.8)% sensitivity, (95.7 ± 0.6)% specificity AUC by sampling deeper tissue layers. To the best of our knowledge, this is the first DRS study to investigate the potential of probing deeper tissue layers using larger SDD probes for CRC detection in the luminal wall. The data analysis showed that using a broader spectrum and longer near-infrared wavelengths can improve the diagnostic accuracy of CRC as well as probing deeper tissue layers.
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Affiliation(s)
- Marcelo Saito Nogueira
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland ,grid.7872.a0000000123318773Department of Physics, University College Cork, College Road, Cork, Ireland
| | - Siddra Maryam
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland ,grid.7872.a0000000123318773Department of Physics, University College Cork, College Road, Cork, Ireland
| | - Michael Amissah
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland ,grid.7872.a0000000123318773Department of Physics, University College Cork, College Road, Cork, Ireland
| | - Huihui Lu
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland
| | - Noel Lynch
- grid.411785.e0000 0004 0575 9497Department of Surgery, Mercy University Hospital, Cork, Ireland
| | - Shane Killeen
- grid.411785.e0000 0004 0575 9497Department of Surgery, Mercy University Hospital, Cork, Ireland
| | - Micheal O’Riordain
- grid.411785.e0000 0004 0575 9497Department of Surgery, Mercy University Hospital, Cork, Ireland
| | - Stefan Andersson-Engels
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland ,grid.7872.a0000000123318773Department of Physics, University College Cork, College Road, Cork, Ireland
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Zhang H, Cheng C, Gao R, Yan Z, Zhu Z, Yang B, Chen C, Lv X, Li H, Huang Z. Rapid identification of cervical adenocarcinoma and cervical squamous cell carcinoma tissue based on Raman spectroscopy combined with multiple machine learning algorithms. Photodiagnosis Photodyn Ther 2020; 33:102104. [PMID: 33212265 DOI: 10.1016/j.pdpdt.2020.102104] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/03/2020] [Accepted: 11/09/2020] [Indexed: 11/17/2022]
Abstract
Cervical cancer has a long latency, and early screening greatly reduces mortality. In this study, cervical adenocarcinoma and cervical squamous cell carcinoma tissue data were collected by Raman spectroscopy, and then, the adaptive iteratively reweighted penalized least squares (airPLS) algorithm and Vancouver Raman algorithm (VRA) were used to subtract the background of the collected data. The following five feature extraction algorithms were applied: partial least squares (PLS), principal component analysis (PCA), kernel principal component analysis (KPCA), isometric feature mapping (isomap) and locally linear embedding (LLE). The k-nearest neighbour (KNN), extreme learning machine (ELM), decision tree (DT), backpropagation neural network (BP), genetic optimization backpropagation neural network (GA-BP) and linear discriminant analysis (LDA) classification models were then established through the features extracted by different feature extraction algorithms. In total, 30 types of classification models were established in this experiment. This research includes eight good models, airPLS-PLS-KNN, airPLS-PLS-ELM, airPLS-PLS-GA-BP, airPLS-PLS-BP, airPLS-PLS-LDA, airPLS-PCA-KNN, airPLS-PCA-LDA, and VRA-PLS-KNN, whose diagnostic accuracy was 96.3 %, 95.56 %, 95.06 %, 94.07 %, 92.59 %, 85.19 %, 85.19 % and 85.19 %, respectively. The experimental results showed that the model established in this article is simple to operate and highly accurate and has a good reference value for the rapid screening of cervical cancer.
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Affiliation(s)
- Huiting Zhang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Chen Cheng
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| | - Rui Gao
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Ziwei Yan
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Zhimin Zhu
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Bo Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- School of Software, Xinjiang University, Urumqi 840046, China.
| | - Hongyi Li
- Quality of Products Supervision and Inspection Institute, Urumqi 830011, Xinjiang, China
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Chen C, Yang L, Li H, Chen F, Chen C, Gao R, Lv XY, Tang J. Raman spectroscopy combined with multiple algorithms for analysis and rapid screening of chronic renal failure. Photodiagnosis Photodyn Ther 2020; 30:101792. [DOI: 10.1016/j.pdpdt.2020.101792] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 04/18/2020] [Accepted: 04/20/2020] [Indexed: 10/24/2022]
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21
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Wang D, Jiang J, Mo J, Tang J, Lv X. Rapid Screening of Thyroid Dysfunction Using Raman Spectroscopy Combined with an Improved Support Vector Machine. APPLIED SPECTROSCOPY 2020; 74:674-683. [PMID: 32031008 DOI: 10.1177/0003702820904444] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This study aimed to screen for thyroid dysfunction using Raman spectroscopy combined with an improved support vector machine (SVM). In spectral analysis, in order to further improve the classification accuracy of the SVM algorithm model, a genetic particle swarm optimization algorithm based on partial least squares is proposed to optimize support vector machine (PLS-GAPSO-SVM). In order to evaluate the performance of the algorithm, five optimization algorithms are used: grid search-based SVM (Grid-SVM), particle swarm optimization algorithm-based SVM (PSO-SVM), genetic algorithm-based SVM (GA-SVM), artificial fish coupled uniform design algorithm-based SVM (AFUD-SVM), and simulated annealing particle swarm optimization algorithm-based SVM (SAPSO-SVM). In this experiment, serum samples from 95 patients with confirmed thyroid dysfunction and 90 serum samples from normal thyroid function were used for Raman spectroscopy. The experimental results show that the GAPSO-SVM algorithm has a high average diagnostic accuracy of 95.08% and has high sensitivity and specificity (91.67%, 97.96%). Compared with the traditional optimization algorithm, the algorithm has high diagnostic accuracy, short execution time, and good reliability. It can be seen that Raman spectroscopy combined with GAPSO-SVM diagnostic algorithm has enormous potential in noninvasive screening of thyroid dysfunction.
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Affiliation(s)
- Dingding Wang
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Jing Jiang
- The first Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Jiaqing Mo
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Jun Tang
- Physics and Chemistry Detecting Center, Xinjiang University, Urumqi, China
| | - Xiaoyi Lv
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
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22
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Glover B, Teare J, Patel N. The Status of Advanced Imaging Techniques for Optical Biopsy of Colonic Polyps. Clin Transl Gastroenterol 2020; 11:e00130. [PMID: 32352708 PMCID: PMC7145035 DOI: 10.14309/ctg.0000000000000130] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 01/03/2020] [Indexed: 12/19/2022] Open
Abstract
The progressive miniaturization of photonic components presents the opportunity to obtain unprecedented microscopic images of colonic polyps in real time during endoscopy. This information has the potential to act as "optical biopsy" to aid clinical decision-making, including the possibility of adopting new paradigms such as a "resect and discard" approach for low-risk lesions. The technologies discussed in this review include confocal laser endomicroscopy, optical coherence tomography, multiphoton microscopy, Raman spectroscopy, and hyperspectral imaging. These are in different stages of development and clinical readiness, but all show the potential to produce reliable in vivo discrimination of different tissue types. A structured literature search of the imaging techniques for colorectal polyps has been conducted. The significant developments in endoscopic imaging were identified for each modality, and the status of current development was discussed. Of the advanced imaging techniques discussed, confocal laser endomicroscopy is in clinical use and, under optimal conditions with an experienced operator, can provide accurate histological assessment of tissue. The remaining techniques show potential for incorporation into endoscopic equipment and practice, although further component development is needed, followed by robust prospective validation of accuracy. Optical coherence tomography illustrates tissue "texture" well and gives good assessment of mucosal thickness and layers. Multiphoton microscopy produces high-resolution images at a subcellular resolution. Raman spectroscopy and hyperspectral imaging are less developed endoscopically but provide a tissue "fingerprint" which can distinguish between tissue types. Molecular imaging may become a powerful adjunct to other techniques, with its ability to precisely label specific molecules within tissue and thereby enhance imaging.
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Affiliation(s)
- Ben Glover
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Julian Teare
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Nisha Patel
- Department of Surgery and Cancer, Imperial College London, London, UK
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23
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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: 24] [Impact Index Per Article: 4.8] [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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Abramczyk H, Imiela A, Brożek-Płuska B, Kopeć M, Surmacki J, Śliwińska A. Aberrant Protein Phosphorylation in Cancer by Using Raman Biomarkers. Cancers (Basel) 2019; 11:E2017. [PMID: 31847192 PMCID: PMC6966530 DOI: 10.3390/cancers11122017] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 12/03/2019] [Accepted: 12/11/2019] [Indexed: 12/30/2022] Open
Abstract
(1) Background: Novel methods are required for analysing post-translational modifications of protein phosphorylation by visualizing biochemical landscapes of proteins in human normal and cancerous tissues and cells. (2) Methods: A label-free Raman method is presented for detecting spectral changes that arise in proteins due to phosphorylation in the tissue of human breasts, small intestines, and brain tumours, as well as in the normal human astrocytes and primary glioblastoma U-87 MG cell lines. Raman spectroscopy and Raman imaging are effective tools for monitoring and analysing the vibrations of functional groups involved in aberrant phosphorylation in cancer without any phosphorecognition of tag molecules. (3) Results: Our results based on 35 fresh human cancer and normal tissues prove that the aberrant tyrosine phosphorylation monitored by the unique spectral signatures of Raman vibrations is a universal characteristic in the metabolic regulation in different types of cancers. Overexpressed tyrosine phosphorylation in the human breast, small intestine and brain tissues and in the human primary glioblastoma U-87 MG cell line was monitored by using Raman biomarkers. (4) We showed that the bands at 1586 cm-1 and 829 cm-1, corresponding to phosphorylated tyrosine, play a pivotal role as a Raman biomarker of the phosphorylation status in aggressive cancers. We found that the best Raman biomarker of phosphorylation is the 1586/829 ratio showing the statistical significance at p Values of ≤ 0.05. (5) Conclusions: Raman spectroscopy and imaging have the potential to be used as screening functional assays to detect phosphorylated target proteins and will help researchers to understand the role of phosphorylation in cellular processes and cancer progression. The abnormal and excessive high level of tyrosine phosphorylation in cancer samples compared with normal samples was found in the cancerous human tissue of breasts, small intestines and brain tumours, as well as in the mitochondria and lipid droplets of the glioblastoma U-87 MG cell line. Detailed insights are presented into the intracellular oncogenic metabolic pathways mediated by phosphorylated tyrosine.
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Affiliation(s)
- Halina Abramczyk
- Laboratory of Laser Molecular Spectroscopy, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Wroblewskiego 15, 93-590 Lodz, Poland; (A.I.); (B.B.-P.); (M.K.); (J.S.)
| | - Anna Imiela
- Laboratory of Laser Molecular Spectroscopy, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Wroblewskiego 15, 93-590 Lodz, Poland; (A.I.); (B.B.-P.); (M.K.); (J.S.)
| | - Beata Brożek-Płuska
- Laboratory of Laser Molecular Spectroscopy, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Wroblewskiego 15, 93-590 Lodz, Poland; (A.I.); (B.B.-P.); (M.K.); (J.S.)
| | - Monika Kopeć
- Laboratory of Laser Molecular Spectroscopy, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Wroblewskiego 15, 93-590 Lodz, Poland; (A.I.); (B.B.-P.); (M.K.); (J.S.)
| | - Jakub Surmacki
- Laboratory of Laser Molecular Spectroscopy, Institute of Applied Radiation Chemistry, Faculty of Chemistry, Lodz University of Technology, Wroblewskiego 15, 93-590 Lodz, Poland; (A.I.); (B.B.-P.); (M.K.); (J.S.)
| | - Agnieszka Śliwińska
- Faculty of Medicine, Medical University of Lodz, Chair of Department of Nucleic Acids Biochemistry, Pomorska 251, 92-213 Lodz, Poland;
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25
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Mohanan SMPC, Beck RJ, West NP, Shires M, Perry SL, Jayne DG, Hand DP, Shephard JD. Preclinical evaluation of porcine colon resection using hollow core negative curvature fibre delivered ultrafast laser pulses. JOURNAL OF BIOPHOTONICS 2019; 12:e201900055. [PMID: 31240824 DOI: 10.1002/jbio.201900055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 05/17/2019] [Accepted: 06/25/2019] [Indexed: 06/09/2023]
Abstract
Ultrashort pulse lasers offer great promise for tissue resection with exceptional precision and minimal thermal damage. Surgery in the bowel requires high precision and minimal necrotic tissue to avoid severe complications such as perforation. The deployment of ultrashort lasers in minimally invasive or endoscopic procedures has been hindered by the lack of suitable optical fibres for high peak powers. However, recent developments of hollow core microstructured fibres provide potential for delivery of such pulses throughout the body. In this study, analysis of laser ablation via a scanning galvanometer on a porcine colon tissue model is presented. A thermally damaged region (<85 μm) and fine depth control of ablation using the pulse energies 46 and 33 μJ are demonstrated. It is further demonstrated that such pulses suitable for precision porcine colon resection can be flexibly delivered via a hollow core negative curvature fibre (HC-NCF) and again ablation depth can be controlled with a thermally damaged region <85 μm. Ablation volumes are comparable to that of early stage lesions in the inner lining of the colon. This study concludes that the combination of ultrashort pulses and flexible fibre delivery via HC-NCF present a viable route to new minimally invasive surgical procedures.
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Affiliation(s)
- Syam M P C Mohanan
- School of Engineering and Physical Sciences, Institute of Photonics and Quantum Sciences, Heriot-Watt University, Edinburgh, UK
| | - Rainer J Beck
- School of Engineering and Physical Sciences, Institute of Photonics and Quantum Sciences, Heriot-Watt University, Edinburgh, UK
| | - Nicholas P West
- Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK
| | - Michael Shires
- Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK
| | - Sarah L Perry
- Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK
| | - David G Jayne
- Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds, UK
| | - Duncan P Hand
- School of Engineering and Physical Sciences, Institute of Photonics and Quantum Sciences, Heriot-Watt University, Edinburgh, UK
| | - Jonathan D Shephard
- School of Engineering and Physical Sciences, Institute of Photonics and Quantum Sciences, Heriot-Watt University, Edinburgh, UK
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26
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Cui H, Cui D. Centroid-position-based autofocusing technique for Raman spectroscopy. OPTICS EXPRESS 2019; 27:27354-27368. [PMID: 31674598 DOI: 10.1364/oe.27.027354] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 08/28/2019] [Indexed: 06/10/2023]
Abstract
In Raman spectroscopy, it is crucial to focus the laser on the sample in order to guarantee the intensity and repeatability of the characteristic peaks, which is known as autofocus. In this paper, we propose a novel low-cost scheme based on the subtle placement of the laser source and the image sensor. We confirm the feasibility of monitoring the focus status through the centroid position of the laser spot's image (CPSI) in theory. Both the simulation and experimental results illustrate that the distance-ordinate function is similar in shape to the logarithm, which not only helps to shorten the autofocus time but also achieves the sub-decimeter measuring range and micrometer resolution near the focal point. Meanwhile, we discuss in detail how to obtain the desired performance by adjusting the extrinsic camera parameters and the way to overcome the disturbance of the noise, ambient light and non-normal incidence. An autofocus-free handheld Raman spectrograph utilizes this method to autofocus the alcohol in the centrifuge tube successfully and the spectral reproducibility is improved. Our results may pave the way to a novel autofocus approach for Raman mapping in vivo.
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27
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Song CL, Vardaki MZ, Goldin RD, Kazarian SG. Fourier transform infrared spectroscopic imaging of colon tissues: evaluating the significance of amide I and C-H stretching bands in diagnostic applications with machine learning. Anal Bioanal Chem 2019; 411:6969-6981. [PMID: 31418050 PMCID: PMC6834539 DOI: 10.1007/s00216-019-02069-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 07/23/2019] [Accepted: 08/01/2019] [Indexed: 12/16/2022]
Abstract
Fourier transform infrared (FTIR) spectroscopic imaging of colon biopsy tissues in transmission combined with machine learning for the classification of different stages of colon malignancy was carried out in this study. Two different approaches, an optical and a computational one, were applied for the elimination of the scattering background during the measurements and compared with the results of the machine learning model without correction for the scattering. Several different data processing pathways were implemented in order to obtain a high accuracy of the prediction model. This study demonstrates, for the first time, that C-H stretching and amide I bands are of little to no significance in the classification of the colon malignancy, based on the Gini importance values by random forest (RF). The best prediction outcome is found when supervised RF classification was carried out in the fingerprint region of the spectral data between 1500 and 1000 cm-1 (excluding the contribution of amide I and II bands). An overall prediction accuracy higher than 90% is achieved through the RF. The results also show that dysplastic and hyperplastic tissues are well distinguished. This leads to the insight that the important differences between hyperplastic and dysplastic colon tissues lie within the fingerprint region of FTIR spectra. In this study, computational correction performed better than optical correction, but the findings show that the disease states of colon biopsies can be distinguished effectively without elimination of Mie scattering effect. Graphical abstract.
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Affiliation(s)
- Cai Li Song
- Department of Chemical Engineering, South Kensington Campus, Imperial College London, London, SW7 2AZ, UK
| | - Martha Z Vardaki
- Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Robert D Goldin
- Department of Cellular Pathology, St. Mary's Campus, Imperial College London, W2 1NY, London, UK
| | - Sergei G Kazarian
- Department of Chemical Engineering, South Kensington Campus, Imperial College London, London, SW7 2AZ, UK.
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28
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Tong D, Chen C, Zhang J, Lv G, Zheng X, Zhang Z, Lv X. Application of Raman spectroscopy in the detection of hepatitis B virus infection. Photodiagnosis Photodyn Ther 2019; 28:248-252. [PMID: 31425766 DOI: 10.1016/j.pdpdt.2019.08.006] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 07/28/2019] [Accepted: 08/02/2019] [Indexed: 01/10/2023]
Abstract
OBJECTIVE Detection of hepatitis B virus (HBV) using Raman spectroscopy. METHODS Raman spectroscopy was used to examine the serum samples of 500 patients with HBV and 500 non-HBV persons. First, the adaptive iterative weighted penalty least squares method (airPLS) was used to deduct the fluorescence background in Raman spectra. Then, a principal component analysis (PCA) was used to extract the processed Raman spectra, and a support vector machine (SVM) was used for modeling and prediction. The particle swarm optimization (PSO) algorithm was selected to optimize the parameters of the SVM instead of a traditional grid search. Finally, 600 serum samples were detected by Raman spectroscopy, and the results wereverified using a double-blind method. RESULTS In the Raman spectra, the non-HBV human Raman peaks at 509, 957, 1002, 1153, 1260, 1512, 1648 and 2305 cm-1 were different from those of patients with HBV. The reported accuracy, sensitivity and specificity of the HBV serum model established using airPLS-PCA-PSO-SVM was 93.1%, 100% and 88%, respectively. The two groups were verified by a double-blind method. In the first group sensitivity was 87%, specificity was 92%, and the KAPPA value was 0.79; in the second group sensitivity was 80%, specificity was 79%, and the KAPPA value was 0.59. CONCLUSION This preliminary study shows that serum Raman spectroscopy combined with the airPLS-PCA-PSO-SVM model can be used for hepatitis B virus detection.
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Affiliation(s)
- Dongni Tong
- Department of Laboratory Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumuqi 83001, China
| | - Cheng Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - JingJing Zhang
- Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - GuoDong Lv
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China
| | - Xiangxiang Zheng
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Zhaoxia Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumuqi 83001, China.
| | - Xiaoyi Lv
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
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29
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Ralbovsky NM, Lednev IK. Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 219:463-487. [PMID: 31075613 DOI: 10.1016/j.saa.2019.04.067] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 04/20/2019] [Accepted: 04/24/2019] [Indexed: 05/14/2023]
Abstract
Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.
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Affiliation(s)
- Nicole M Ralbovsky
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, USA
| | - Igor K Lednev
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, USA.
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30
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Zheng Q, Kang W, Chen C, Shi X, Yang Y, Yu C. Diagnosis accuracy of Raman spectroscopy in colorectal cancer: A PRISMA-compliant systematic review and meta-analysis. Medicine (Baltimore) 2019; 98:e16940. [PMID: 31441886 PMCID: PMC6716686 DOI: 10.1097/md.0000000000016940] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND The clinical significance of Raman spectroscopy (RS) in colorectal cancer (CRC) patients still remains underestimated. We performed this meta-analysis to elucidate the diagnostic value in CRC patients. METHODS We systematically searched electronic databases for published articles. Fixed effect model and random effect model were used to calculate the pooled sensitivity, specificity, diagnostic accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and positive posttest probability (PPP) of CRC. Meta-regression and subgroup analysis were conducted to assess potential source of heterogeneity. We also used Egger linear regression tests to assess risk of publication bias. RESULTS Thirteen studies had been included (679 patients: 186 with premalignant lesions and 493 with malignant lesions). The pooled sensitivity, specificity, diagnostic accuracy, PLR, NLR, DOR and PPP for CRC screening using RS were 0.94 (0.92-0.96), 0.94 (0.88-0.97), 0.96 (0.94-0.98), 16.44 (7.80-34.63), 0.062 (0.043-0.090), 263.65 (99.03-701.96) and 86%, respectively. CONCLUSION RS is a potentially useful tool for future CRC screening. It also offers potentially early detection for CRC patients.
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Affiliation(s)
- Qiang Zheng
- Department of Gastrointestinal Surgery, Department of General Surgery, the First Affiliated Hospital of Anhui Medical University
| | - Weibiao Kang
- Department of Gastrointestinal Surgery, Department of General Surgery, the First Affiliated Hospital of Anhui Medical University
| | - Changyu Chen
- Department of General Surgery, First Affiliated Hospital of Anhui Traditional Medical University, Hefei, China
| | - Xinxin Shi
- Department of Gastrointestinal Surgery, Department of General Surgery, the First Affiliated Hospital of Anhui Medical University
| | - Yang Yang
- Department of Gastrointestinal Surgery, Department of General Surgery, the First Affiliated Hospital of Anhui Medical University
| | - Changjun Yu
- Department of Gastrointestinal Surgery, Department of General Surgery, the First Affiliated Hospital of Anhui Medical University
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31
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Zheng X, Lv G, Zhang Y, Lv X, Gao Z, Tang J, Mo J. Rapid and non-invasive screening of high renin hypertension using Raman spectroscopy and different classification algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 215:244-248. [PMID: 30831394 DOI: 10.1016/j.saa.2019.02.063] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2018] [Revised: 02/09/2019] [Accepted: 02/17/2019] [Indexed: 05/27/2023]
Abstract
This study presents a rapid and non-invasive method to screen high renin hypertension using serum Raman spectroscopy combined with different classification algorithms. The serum samples taken from 24 high renin hypertension patients and 22 non-high renin hypertension samples were measured in this experiment. Tentative assignments of the Raman peaks in the measured serum spectra suggested specific biomolecular changes between the groups. Principal component analysis (PCA) was first used for feature extraction and reduced the dimension of high-dimension spectral data. Then, support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (KNN) algorithms were employed to establish the discriminant diagnostic models. The accuracies of 93.5%, 93.5% and 89.1% were obtained from PCA-SVM, PCA-LDA and PCA-KNN models, respectively. The results from our study demonstrate that the serum Raman spectroscopy technique combined with multivariate statistical methods have great potential for the screening of high renin hypertension. This technique could be used to develop a portable, rapid, and non-invasive device for screening high renin hypertension.
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Affiliation(s)
- Xiangxiang Zheng
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Guodong Lv
- The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, China
| | - Ying Zhang
- The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, China
| | - Xiaoyi Lv
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Institute of Health and Environmental Medicine of AMMS, Tianjin 300050, China.
| | - Zhixian Gao
- Institute of Health and Environmental Medicine of AMMS, Tianjin 300050, China
| | - Jun Tang
- Physics and Chemistry Detecting Center, Xinjiang University, Urumqi 830046, China.
| | - Jiaqing Mo
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
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32
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Zhang YJ, Zeng QY, Li LF, Qi MN, Qi QC, Li SX, Xu JF. Label-free rapid identification of tumor cells and blood cells with silver film SERS substrate. OPTICS EXPRESS 2018; 26:33044-33056. [PMID: 30645462 DOI: 10.1364/oe.26.033044] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The detection of circulating tumor cells (CTCs) from peripheral blood is considered as great significance for the diagnosis and prognosis of cancer patients. Raman spectroscopy is a highly sensitive optical detection technique that can provide fingerprint molecular identification information. In this paper, the silver film substrate surface-enhanced Raman scattering (SERS) was used to research several tumor cells, immortalized cells, clinical cancer cells isolated from cancer patient's tissue and blood cells. The results display that there is great difference for the nucleic acid characteristic peaks of those cells. The red blood cells have almost none nucleic acid characteristic peak and the SERS signals of white blood cells are only a slight increase. Except for immortalized cells and few tumor cells, the nucleic acid characteristic peaks of some tumor cells have huge enhancement. Nucleic acid characteristic peaks of clinical cancer cells also have greater enhancement. The discriminant model established by the intensity ratio of the nucleic acid characteristic peak 730 cm-1 to the substrate background peak 900 cm-1 shows that some tumor cells and clinical sample cells can be separated from white blood cells, but tumor cells with relatively low-DNA index cannot be differentiated from white blood cells. This study demonstrates that thin-film SERS technology can distinguish between blood cells and some types of tumor cells. This study opens up a new possible method for the detection of CTCs with label-free SERS spectra.
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田 笑, 张 毅. [Research Progress of Raman Spectroscopy in the Diagnosis of Early Lung Cancer]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2018; 21:560-564. [PMID: 30037378 PMCID: PMC6058664 DOI: 10.3779/j.issn.1009-3419.2018.07.10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 02/06/2018] [Accepted: 02/10/2018] [Indexed: 11/05/2022]
Abstract
Lung cancer (LC) is the most common cancer and the leading cause of cancer-related death worldwide. The 5-year survival rate for LC remains low at 18% and is 5% for patients with metastatic disease, while the 5-year overall survival rate of patients with stage I NSCLC can reach 77.9%, hence early diagnosis and treatment of LC is the key to improve the prognosis. As a non-invasive detection technique, Raman spectroscopy can realize the non-destructive detection of the differences in molecular level structure between cancerous tissues and normal tissues, which can be used for the early diagnosis of lung cancer. The aim of this review is to summarize the progress of Raman spectroscopycombined with different tissue or body fluid samplesin the diagnosis of early LC.
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Affiliation(s)
- 笑如 田
- />100053 北京,首都医科大学宣武医院胸外科Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - 毅 张
- />100053 北京,首都医科大学宣武医院胸外科Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
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34
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Botta R, Chindaudom P, Eiamchai P, Horprathum M, Limwichean S, Chananonnawathorn C, Patthanasettakul V, Kaewseekhao B, Faksri K, Nuntawong N. Tuberculosis determination using SERS and chemometric methods. Tuberculosis (Edinb) 2018. [PMID: 29523323 DOI: 10.1016/j.tube.2017.12.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Nanostructures have been multiplying the advantages of Raman spectroscopy and further amplify the advantages of Raman spectroscopy is a continuous effort focused on the appropriate design of nanostructures. Herein, we designed different shapes of plasmonic nanostructures such as Vertical, Zig Zag, Slant nanorods and Spherical nanoparticles employing the DC magnetron sputtering system as SERS-active substrates for ultrasensitive detection of target molecules. The fabricated plasmonic nanostructures sensitivity and uniformity were exploited by reference dye analyte. These nanostructures were utilized in the label free detection of infectious disease, Tuberculosis (TB). For the first time, TB detection from serum samples using SERS has been demonstrated. Various multivariate statistical methods such as principal component analysis, support vector machine, decision tree and random forest were developed and tested their ability to discriminate the healthy and active TB samples. The results demonstrate the performance of the SERS spectra, chemometric methods and potential of the method in clinical diagnosis.
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Affiliation(s)
- Raju Botta
- National Electronics and Computer Technology Center (NECTEC), Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, Thailand.
| | - Pongpan Chindaudom
- National Electronics and Computer Technology Center (NECTEC), Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, Thailand
| | - Pitak Eiamchai
- National Electronics and Computer Technology Center (NECTEC), Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, Thailand
| | - Mati Horprathum
- National Electronics and Computer Technology Center (NECTEC), Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, Thailand
| | - Saksorn Limwichean
- National Electronics and Computer Technology Center (NECTEC), Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, Thailand
| | - Chanunthorn Chananonnawathorn
- National Electronics and Computer Technology Center (NECTEC), Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, Thailand
| | - Viyapol Patthanasettakul
- National Electronics and Computer Technology Center (NECTEC), Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, Thailand
| | - Benjawan Kaewseekhao
- Department of Microbiology and Research and Diagnostic Center for Emerging Infectious Diseases (RCEID), Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Kiatichai Faksri
- Department of Microbiology and Research and Diagnostic Center for Emerging Infectious Diseases (RCEID), Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Noppadon Nuntawong
- National Electronics and Computer Technology Center (NECTEC), Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, Thailand
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35
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Colorectal Cancer and Colitis Diagnosis Using Fourier Transform Infrared Spectroscopy and an Improved K-Nearest-Neighbour Classifier. SENSORS 2017; 17:s17122739. [PMID: 29186913 PMCID: PMC5750796 DOI: 10.3390/s17122739] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 10/31/2017] [Accepted: 11/02/2017] [Indexed: 01/03/2023]
Abstract
Combining Fourier transform infrared spectroscopy (FTIR) with endoscopy, it is expected that noninvasive, rapid detection of colorectal cancer can be performed in vivo in the future. In this study, Fourier transform infrared spectra were collected from 88 endoscopic biopsy colorectal tissue samples (41 colitis and 47 cancers). A new method, viz., entropy weight local-hyperplane k-nearest-neighbor (EWHK), which is an improved version of K-local hyperplane distance nearest-neighbor (HKNN), is proposed for tissue classification. In order to avoid limiting high dimensions and small values of the nearest neighbor, the new EWHK method calculates feature weights based on information entropy. The average results of the random classification showed that the EWHK classifier for differentiating cancer from colitis samples produced a sensitivity of 81.38% and a specificity of 92.69%.
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Wan QS, Wang T, Zhang KH. Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms. Tumour Biol 2017; 39:1010428317717984. [PMID: 28671054 DOI: 10.1177/1010428317717984] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Gastrointestinal cancer is a leading contributor to cancer-related morbidity and mortality worldwide. Early diagnosis currently plays a key role in the prognosis of patients with gastrointestinal cancer. Despite the advances in endoscopy over the last decades, missing lesions, undersampling and incorrect sampling in biopsies, as well as invasion still result in a poor diagnostic rate of early gastrointestinal cancers. Accordingly, there is a pressing need to develop non-invasive methods for the early detection of gastrointestinal cancers. Biomedical optical spectroscopy, including infrared spectroscopy, Raman spectroscopy, diffuse scattering spectroscopy and autofluorescence, is capable of providing structural and chemical information about biological specimens with the advantages of non-destruction, non-invasion and reagent-free and waste-free analysis and has thus been widely investigated for the diagnosis of oesophageal, gastric and colorectal cancers. This review will introduce the advances of biomedical optical spectroscopy techniques, highlight their applications for the early detection of gastrointestinal cancers and discuss their limitations.
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Affiliation(s)
- Qin-Si Wan
- Department of Gastroenterology, Jiangxi Institute of Gastroenterology & Hepatology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ting Wang
- Department of Gastroenterology, Jiangxi Institute of Gastroenterology & Hepatology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Kun-He Zhang
- Department of Gastroenterology, Jiangxi Institute of Gastroenterology & Hepatology, The First Affiliated Hospital of Nanchang University, Nanchang, China
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Darrigues E, Nima ZA, Majeed W, Vang-Dings KB, Dantuluri V, Biris AR, Zharov VP, Griffin RJ, Biris AS. Raman spectroscopy using plasmonic and carbon-based nanoparticles for cancer detection, diagnosis, and treatment guidance.Part 1: Diagnosis. Drug Metab Rev 2017; 49:212-252. [DOI: 10.1080/03602532.2017.1302465] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Emilie Darrigues
- Center for Integrative Nanotechnology Sciences, University of Arkansas at Little Rock, Little Rock, AR, USA
| | - Zeid A. Nima
- Center for Integrative Nanotechnology Sciences, University of Arkansas at Little Rock, Little Rock, AR, USA
| | - Waqar Majeed
- Center for Integrative Nanotechnology Sciences, University of Arkansas at Little Rock, Little Rock, AR, USA
| | - Kieng Bao Vang-Dings
- Center for Integrative Nanotechnology Sciences, University of Arkansas at Little Rock, Little Rock, AR, USA
| | - Vijayalakshmi Dantuluri
- Center for Integrative Nanotechnology Sciences, University of Arkansas at Little Rock, Little Rock, AR, USA
| | - Alexandru R. Biris
- National Institute for Research and Development of Isotopic and Molecular Technologies
| | - Vladimir P. Zharov
- Arkansas Nanomedicine Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Robert J. Griffin
- Arkansas Nanomedicine Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
- Department of Radiation Oncology, Arkansas Nanomedicine Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Alexandru S. Biris
- Center for Integrative Nanotechnology Sciences, University of Arkansas at Little Rock, Little Rock, AR, USA
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38
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Study on the echinococcosis blood serum detection based on Raman spectroscopy combined with neural network. ACTA ACUST UNITED AC 2017. [DOI: 10.1007/s11801-017-6259-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Ohta R, Ueno Y, Ajito K. Raman Spectroscopy of Pharmaceutical Cocrystals in Nanosized Pores of Mesoporous Silica. ANAL SCI 2017; 33:47-52. [PMID: 28070074 DOI: 10.2116/analsci.33.47] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The Raman spectroscopy of pharmaceutical cocrystals based on caffeine and oxalic acid in nanosized pores of mesoporous silica has been demonstrated at various molar amounts. The Raman peak shifts of caffeine molecules express the existence of pharmaceutical cocrystals in mesoporous silica. The molar amount dependence of the peak shifts describes that caffeine and oxalic acid cocrystallized on the surface of the nanosized pores and piled up layer by layer. This is the first report that shows the Raman spectroscopy is a powerful tool to observe the synthesis of pharmaceutical cocrystals incorporated in the nanosized pores of mesoporous silica. The results indicate a way to control the size of cocrystals on a nanometer scale, which will provide higher bioavailability of pharmaceuticals.
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Affiliation(s)
- Ryuichi Ohta
- NTT Device Technology Laboratories, NTT Corporation
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Gaifulina R, Maher AT, Kendall C, Nelson J, Rodriguez-Justo M, Lau K, Thomas GM. Label-free Raman spectroscopic imaging to extract morphological and chemical information from a formalin-fixed, paraffin-embedded rat colon tissue section. Int J Exp Pathol 2016; 97:337-350. [PMID: 27581376 PMCID: PMC5061758 DOI: 10.1111/iep.12194] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 05/04/2016] [Indexed: 12/19/2022] Open
Abstract
Animal models and archived human biobank tissues are useful resources for research in disease development, diagnostics and therapeutics. For the preservation of microscopic anatomical features and to facilitate long-term storage, a majority of tissue samples are denatured by the chemical treatments required for fixation, paraffin embedding and subsequent deparaffinization. These aggressive chemical processes are thought to modify the biochemical composition of the sample and potentially compromise reliable spectroscopic examination useful for the diagnosis or biomarking. As a result, spectroscopy is often conducted on fresh/frozen samples. In this study, we provide an extensive characterization of the biochemical signals remaining in processed samples (formalin fixation and paraffin embedding, FFPE) and especially those originating from the anatomical layers of a healthy rat colon. The application of chemometric analytical methods (unsupervised and supervised) was shown to eliminate the need for tissue staining and easily revealed microscopic features consistent with goblet cells and the dense populations of cells within the mucosa, principally via strong nucleic acid signals. We were also able to identify the collagenous submucosa- and serosa- as well as the muscle-associated signals from the muscular regions and blood vessels. Applying linear regression analysis to the data, we were able to corroborate this initial assignment of cell and tissue types by confirming the biological origin of each layer by reference to a subset of authentic biomolecular standards. Our results demonstrate the potential of using label-free Raman microspectroscopy to obtain superior imaging contrast in FFPE sections when compared directly to conventional haematoxylin and eosin (H&E) staining.
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Affiliation(s)
- Riana Gaifulina
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Andrew Thomas Maher
- Department of Cell and Developmental Biology, University College London, London, UK
- CoMPLEX, University College London, London, UK
| | - Catherine Kendall
- Biophotonics Research Unit, Gloucestershire Royal Hospital, Gloucester, UK
| | - James Nelson
- Department of Statistical Science, University College London, London, UK
| | | | - Katherine Lau
- Spectroscopy Products Division, Renishaw Plc, Wotton-under-Edge, UK
| | - Geraint Mark Thomas
- Department of Cell and Developmental Biology, University College London, London, UK.
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Kaur E, Sahu A, Hole AR, Rajendra J, Chaubal R, Gardi N, Dutt A, Moiyadi A, Krishna CM, Dutt S. Unique spectral markers discern recurrent Glioblastoma cells from heterogeneous parent population. Sci Rep 2016; 6:26538. [PMID: 27221528 PMCID: PMC4879554 DOI: 10.1038/srep26538] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Accepted: 05/04/2016] [Indexed: 02/08/2023] Open
Abstract
An inability to discern resistant cells from bulk tumour cell population contributes to poor prognosis in Glioblastoma. Here, we compared parent and recurrent cells generated from patient derived primary cultures and cell lines to identify their unique molecular hallmarks. Although morphologically similar, parent and recurrent cells from different samples showed variable biological properties like proliferation and radiation resistance. However, total RNA-sequencing revealed transcriptional landscape unique to parent and recurrent populations. These data suggest that global molecular differences but not individual biological phenotype could differentiate parent and recurrent cells. We demonstrate that Raman Spectroscopy a label-free, non-invasive technique, yields global information about biochemical milieu of recurrent and parent cells thus, classifying them into distinct clusters based on Principal-Component-Analysis and Principal-Component-Linear-Discriminant-Analysis. Additionally, higher lipid related spectral peaks were observed in recurrent population. Importantly, Raman spectroscopic analysis could further classify an independent set of naïve primary glioblastoma tumour tissues into non-responder and responder groups. Interestingly, spectral features from the non-responder patient samples show a considerable overlap with the in-vitro generated recurrent cells suggesting their similar biological behaviour. This feasibility study necessitates analysis of a larger cohort of naïve primary glioblastoma samples to fully envisage clinical utility of Raman spectroscopy in predicting therapeutic response.
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Affiliation(s)
- Ekjot Kaur
- Shilpee Dutt Laboratory, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Kharghar, Navi Mumbai 410210, India
| | - Aditi Sahu
- Chilakapati Laboratory, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Kharghar, Navi Mumbai 410210, India
| | - Arti R. Hole
- Chilakapati Laboratory, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Kharghar, Navi Mumbai 410210, India
| | - Jacinth Rajendra
- Shilpee Dutt Laboratory, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Kharghar, Navi Mumbai 410210, India
| | - Rohan Chaubal
- Integrated Cancer Genomics Laboratory, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Kharghar, Navi Mumbai 410210, India
| | - Nilesh Gardi
- Integrated Cancer Genomics Laboratory, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Kharghar, Navi Mumbai 410210, India
| | - Amit Dutt
- Integrated Cancer Genomics Laboratory, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Kharghar, Navi Mumbai 410210, India
| | - Aliasgar Moiyadi
- Department of Neurosurgery, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Kharghar, Navi Mumbai 410210, India
| | - C. Murali Krishna
- Chilakapati Laboratory, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Kharghar, Navi Mumbai 410210, India
| | - Shilpee Dutt
- Shilpee Dutt Laboratory, Tata Memorial Centre, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Kharghar, Navi Mumbai 410210, India
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