1
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Alam MR, Dobhal V, Singh S. Neuroprotective potential of solanesol against tramadol induced zebrafish model of Parkinson's disease: insights from neurobehavioral, molecular, and neurochemical evidence. Drug Chem Toxicol 2024:1-16. [PMID: 38938099 DOI: 10.1080/01480545.2024.2355542] [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: 07/24/2023] [Accepted: 05/10/2024] [Indexed: 06/29/2024]
Abstract
Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and subsequent depletion of dopamine in the striatum. Solanesol, an alcohol that acts as a precursor to coenzyme Q10, possesses potential applications in managing neurological disorders with antioxidant, anti-inflammatory, and neuromodulatory potential. In this study, a zebrafish model was employed to investigate the effects of solanesol in tramadol induced PD like symptoms. Zebrafish were administered tramadol injections (50 mg/kg) over a 20-day period. Solanesol was administered at doses of 25, 50, and 100 mg/kg, three hours prior to tramadol administration from day 11 to day 20. Behavioral tests assessing motor coordination were conducted on a weekly basis using open field and novel diving tank apparatus. On day 21, the zebrafish were euthanized, and brain tissues were examined for markers of oxidative stress, inflammation, and neurotransmitters level. Chronic tramadol treatment resulted in motor impairment, reduced antioxidant enzyme levels, enhanced release of proinflammatory cytokines in the striatum, and disrupted neurotransmitter balance. However, solanesol administration mitigated these effects and exhibited a neuroprotective effect against neurodegenerative alterations in the zebrafish model of PD. This was evident through improvements in behavior, modulation of biochemical markers, attenuation of neuroinflammation, restoration of neurotransmitters level, and enhancement of mitochondrial activity. The histopathological study also confirmed that solanesol dose dependently restored neuronal cell density which confirmed its neuroprotective potential. Further investigations are required to elucidate the underlying mechanisms of solanesol neuroprotective effects and evaluate its efficacy in human patients.
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Affiliation(s)
- Md Reyaz Alam
- Department of Pharmacology, Neuropharmacology Division, ISF College of Pharmacy, Moga, India
| | - Vaishali Dobhal
- Department of Pharmacology, Neuropharmacology Division, ISF College of Pharmacy, Moga, India
| | - Shamsher Singh
- Department of Pharmacology, Neuropharmacology Division, ISF College of Pharmacy, Moga, India
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2
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Félix MM, Tavares MV, Santos IP, Batista de Carvalho ALM, Batista de Carvalho LAE, Marques MPM. Cervical Squamous Cell Carcinoma Diagnosis by FTIR Microspectroscopy. Molecules 2024; 29:922. [PMID: 38474435 DOI: 10.3390/molecules29050922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/09/2024] [Accepted: 02/17/2024] [Indexed: 03/14/2024] Open
Abstract
Cervical cancer was considered the fourth most common cancer worldwide in 2020. In order to reduce mortality, an early diagnosis of the tumor is required. Currently, this type of cancer occurs mostly in developing countries due to the lack of vaccination and screening against the Human Papillomavirus. Thus, there is an urgent clinical need for new methods aiming at a reliable screening and an early diagnosis of precancerous and cancerous cervical lesions. Vibrational spectroscopy has provided very good results regarding the diagnosis of various tumors, particularly using Fourier transform infrared microspectroscopy, which has proved to be a promising complement to the currently used histopathological methods of cancer diagnosis. This spectroscopic technique was applied to the analysis of cryopreserved human cervical tissue samples, both squamous cell carcinoma (SCC) and non-cancer samples. A dedicated Support Vector Machine classification model was constructed in order to categorize the samples into either normal or malignant and was subsequently validated by cross-validation, with an accuracy higher than 90%.
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Affiliation(s)
- Maria M Félix
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
| | - Mariana V Tavares
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
- Gynaecology Department, Portuguese Oncology Institute of Porto, 4200-072 Porto, Portugal
| | - Inês P Santos
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
| | - Ana L M Batista de Carvalho
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
| | - Luís A E Batista de Carvalho
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
| | - Maria Paula M Marques
- Molecular Physical-Chemistry R&D Unit, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
- Department of Life Sciences, Faculty of Science and Technology, University of Coimbra, 3000-456 Coimbra, Portugal
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3
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Martin FL. Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics. J Pers Med 2023; 13:1511. [PMID: 37888122 PMCID: PMC10608143 DOI: 10.3390/jpm13101511] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/11/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023] Open
Abstract
As healthcare tools increasingly move towards a more digital and computational format, there is an increasing need for sensor-based technologies that allow for rapid screening and/or diagnostics [...].
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Affiliation(s)
- Francis L Martin
- Department of Cellular Pathology, Blackpool Teaching Hospitals NHS Foundation Trust, Whinney Heys Road, Blackpool FY3 8NR, UK
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4
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Dowling LM, Roach P, Magnussen EA, Kohler A, Pillai S, van Pittius DG, Yousef I, Sulé-Suso J. Fourier Transform Infrared microspectroscopy identifies single cancer cells in blood. A feasibility study towards liquid biopsy. PLoS One 2023; 18:e0289824. [PMID: 37616300 PMCID: PMC10449207 DOI: 10.1371/journal.pone.0289824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/25/2023] [Indexed: 08/26/2023] Open
Abstract
The management of cancer patients has markedly improved with the advent of personalised medicine where treatments are given based on tumour antigen expression amongst other. Within this remit, liquid biopsies will no doubt improve this personalised cancer management. Identifying circulating tumour cells in blood allows a better assessment for tumour screening, staging, response to treatment and follow up. However, methods to identify/capture these circulating tumour cells using cancer cells' antigen expression or their physical properties are not robust enough. Thus, a methodology that can identify these circulating tumour cells in blood regardless of the type of tumour is highly needed. Fourier Transform Infrared (FTIR) microspectroscopy, which can separate cells based on their biochemical composition, could be such technique. In this feasibility study, we studied lung cancer cells (squamous cell carcinoma and adenocarcinoma) mixed with peripheral blood mononuclear cells (PBMC). The data obtained shows, for the first time, that FTIR microspectroscopy together with Random Forest classifier is able to identify a single lung cancer cell in blood. This separation was easier when the region of the IR spectra containing lipids and the amide A (2700 to 3500 cm-1) was used. Furthermore, this work was carried out using glass coverslips as substrates that are widely used in pathology departments. This allows further histopathological cell analysis (staining, immunohistochemistry, …) after FTIR spectra are obtained. Hence, although further work is needed using blood samples from patients with cancer, FTIR microspectroscopy could become another tool to be used in liquid biopsies for the identification of circulating tumour cells, and in the personalised management of cancer.
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Affiliation(s)
- Lewis M. Dowling
- School of Pharmacy and Bioengineering, Guy Hilton Research Centre, Keele University, Stoke-on-Trent, United Kingdom
| | - Paul Roach
- Department of Chemistry, Loughborough University, Loughborough, Leicestershire, United Kingdom
| | - Eirik A. Magnussen
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Achim Kohler
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Srinivas Pillai
- Haematology Department, Royal Stoke University Hospital, University Hospitals of North Midlands (UHNM), Stoke-on-Trent, United Kingdom
| | - Daniel G. van Pittius
- Histopathology Department, Royal Stoke University Hospital, University Hospitals of North Midlands (UHNM), Stoke-on-Trent, United Kingdom
| | - Ibraheem Yousef
- ALBA Synchrotron Light Source, Cerdanyola del Vallès, Barcelona, Catalonia, Spain
| | - Josep Sulé-Suso
- School of Pharmacy and Bioengineering, Guy Hilton Research Centre, Keele University, Stoke-on-Trent, United Kingdom
- Oncology Department, Royal Stoke University Hospital, University Hospitals of North Midlands (UHNM), Stoke-on-Trent, United Kingdom
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5
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Dixit S, Kumar A, Srinivasan K. A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions. Diagnostics (Basel) 2023; 13:diagnostics13071353. [PMID: 37046571 PMCID: PMC10093759 DOI: 10.3390/diagnostics13071353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 03/25/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023] Open
Abstract
Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people’s lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI’s drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.
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Affiliation(s)
- Shriniket Dixit
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Anant Kumar
- School of Bioscience and Technology, Vellore Institute of Technology, Vellore 632014, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
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6
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Paulus A, Yogarasa S, Kansiz M, Martinsson I, Gouras GK, Deierborg T, Engdahl A, Borondics F, Klementieva O. Correlative imaging to resolve molecular structures in individual cells: Substrate validation study for super-resolution infrared microspectroscopy. NANOMEDICINE : NANOTECHNOLOGY, BIOLOGY, AND MEDICINE 2022; 43:102563. [PMID: 35504462 DOI: 10.1016/j.nano.2022.102563] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 04/14/2022] [Accepted: 04/20/2022] [Indexed: 06/14/2023]
Abstract
Light microscopy has been a favorite tool of biological studies for almost a century, recently producing detailed images with exquisite molecular specificity achieving spatial resolution at nanoscale. However, light microscopy is insufficient to provide chemical information as a standalone technique. An increasing amount of evidence demonstrates that optical photothermal infrared microspectroscopy (O-PTIR) is a valuable imaging tool that can extract chemical information to locate molecular structures at submicron resolution. To further investigate the applicability of sub-micron infrared microspectroscopy for biomedical applications, we analyzed the contribution of substrate chemistry to the infrared spectra acquired from individual neurons grown on various imaging substrates. To provide an example of correlative immunofluorescence/O-PTIR imaging, we used immunofluorescence to locate specific organelles for O-PTIR measurement, thus capturing molecular structures at the sub-cellular level directly in cells, which is not possible using traditional infrared microspectroscopy or immunofluorescence microscopy alone.
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Affiliation(s)
- Agnes Paulus
- Medical Microspectroscopy, Department of Experimental Medical Science, Lund University, Lund, Sweden; Experimental Neuroinflammation Lab, Department of Experimental Medical Science, Lund University, 22180 Lund, Sweden.
| | - Sahana Yogarasa
- Medical Microspectroscopy, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Mustafa Kansiz
- Photothermal Spectroscopy Corporation, Santa Barbara, CA 93101, USA
| | - Isak Martinsson
- Experimental Dementia Research, Department of Experimental Medical Science, Lund University, 22180 Lund, Sweden
| | - Gunnar K Gouras
- Experimental Dementia Research, Department of Experimental Medical Science, Lund University, 22180 Lund, Sweden
| | - Tomas Deierborg
- Experimental Neuroinflammation Lab, Department of Experimental Medical Science, Lund University, 22180 Lund, Sweden
| | - Anders Engdahl
- Medical Microspectroscopy, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Ferenc Borondics
- Synchrotron SOLEIL, L'Orme des Merisiers, BP 48, 91192 Gif Sur Yvette Cedex, France
| | - Oxana Klementieva
- Medical Microspectroscopy, Department of Experimental Medical Science, Lund University, Lund, Sweden; Lund Institute for Advanced Neutron and X-ray Science (LINXS), 223 70 Lund, Sweden.
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7
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Shakya BR, Teppo HR, Rieppo L. Optimization of measurement mode and sample processing for FTIR microspectroscopy in skin cancer research. Analyst 2022; 147:851-861. [PMID: 35122480 DOI: 10.1039/d1an01999f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
The use of Fourier Transform Infrared (FTIR) microspectroscopy to study cancerous cells and tissues has gained popularity due to its ability to provide spatially resolved information at the molecular level. Transmission and transflection are the commonly used measurement modes for FTIR microspectroscopy, and the tissue samples measured in these modes are often paraffinized or deparaffinized. Previous studies have shown that variability in the spectra acquired using different measurement modes and sample processing methods affect the result of the analysis. However, there is no protocol that standardizes the mode of measurement and sample processing method to achieve the best classification result. This study compares the spectra of primary (IPC-298) and metastatic (SK-MEL-30) melanoma cell lines acquired in both transmission and transflection modes using paraffinized and deparaffinized samples to determine the optimal combination for accurate classification. Significant differences were observed in the spectra of the same cell line measured in different modes and with or without deparaffinization. The PLS-DA model built for the classification of two cell lines showed high accuracy in each case, suggesting that both modes and sample processing alternatives are suitable for differentiating cultured cell samples using supervised multivariate analysis. The biochemical information contained in the cells capable of discriminating two melanoma cell lines is present regardless of mode or sample type used. However, the paraffinized samples measured in transflection mode provided the best classification.
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Affiliation(s)
- Bijay Ratna Shakya
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Aapistie 5 A, 90220, Oulu, Finland.
| | - Hanna-Riikka Teppo
- Cancer Research and Translational Medicine Research Unit, University of Oulu, Aapistie 5 A, 90220, Oulu, Finland.,Department of Pathology, Oulu University Hospital, Oulu, Finland.,Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Finland
| | - Lassi Rieppo
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Aapistie 5 A, 90220, Oulu, Finland.
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8
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Mamede AP, Santos IP, Batista de Carvalho ALM, Figueiredo P, Silva MC, Marques MPM, Batista de Carvalho LAE. Breast cancer or surrounding normal tissue? A successful discrimination by FTIR or Raman microspectroscopy. Analyst 2022; 147:4919-4932. [DOI: 10.1039/d2an00622g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Breast cancer is a type of cancer with the highest incidence worldwide in 2021, with early diagnosis and rapid treatment intervention being the reasons for the decreasing mortality rate associated with the disease.
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Affiliation(s)
- Adriana P. Mamede
- “Unidade de I&D Química-Física Molecular” (QFM-UC) Department of Chemistry, University of Coimbra, Coimbra, Portugal
| | - Inês P. Santos
- “Unidade de I&D Química-Física Molecular” (QFM-UC) Department of Chemistry, University of Coimbra, Coimbra, Portugal
| | - Ana L. M. Batista de Carvalho
- “Unidade de I&D Química-Física Molecular” (QFM-UC) Department of Chemistry, University of Coimbra, Coimbra, Portugal
| | - Paulo Figueiredo
- Pathology Department, Portuguese Institute of Oncology Francisco Gentil (IPOFG), Coimbra, Portugal
| | - Maria C. Silva
- Surgery Department, Portuguese Institute of Oncology Francisco Gentil (IPOFG), Coimbra, Portugal
| | - Maria P. M. Marques
- “Unidade de I&D Química-Física Molecular” (QFM-UC) Department of Chemistry, University of Coimbra, Coimbra, Portugal
- Department of Life Sciences, University of Coimbra, Coimbra, Portugal
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9
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Ferguson D, Henderson A, McInnes EF, Lind R, Wildenhain J, Gardner P. Infrared micro-spectroscopy coupled with multivariate and machine learning techniques for cancer classification in tissue: a comparison of classification method, performance, and pre-processing technique. Analyst 2022; 147:3709-3722. [DOI: 10.1039/d2an00775d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
A meta-analysis of various multivariate/Machine Learning (ML) classifiers trained on IR Micro-spectroscopy tissue datasets for cancer classification are directly compared using a calculated F1-Score metric alongside study pre-processing techniques.
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Affiliation(s)
- Dougal Ferguson
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- Department of Chemical Engineering and Analytical Science, School of Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Alex Henderson
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- Department of Chemical Engineering and Analytical Science, School of Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | | | - Rob Lind
- Syngenta, International Research Centre, Jealotts Hill, Bracknell, RG42 6EY, UK
| | - Jan Wildenhain
- Syngenta, International Research Centre, Jealotts Hill, Bracknell, RG42 6EY, UK
| | - Peter Gardner
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
- Department of Chemical Engineering and Analytical Science, School of Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
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10
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A New Look into Cancer-A Review on the Contribution of Vibrational Spectroscopy on Early Diagnosis and Surgery Guidance. Cancers (Basel) 2021; 13:cancers13215336. [PMID: 34771500 PMCID: PMC8582426 DOI: 10.3390/cancers13215336] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/14/2021] [Accepted: 10/18/2021] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Cancer is a leading cause of death worldwide, with the detection of the disease in its early stages, as well as a correct assessment of the tumour margins, being paramount for a successful recovery. While breast cancer is one of most common types of cancer, head and neck cancer is one of the types of cancer with a lower prognosis and poor aesthetic results. Vibrational spectroscopy detects molecular vibrations, being sensitive to different sample compositions, even when the difference was slight. The use of spectroscopy in biomedicine has been extensively explored, since it allows a broader assessment of the biochemical fingerprint of several diseases. This literature review covers the most recent advances in breast and head and neck cancer early diagnosis and intraoperative margin assessment, through Raman and Fourier transform infrared spectroscopies. The rising field of spectral histopathology was also approached. The authors aimed at expounding in a more concise and simple way the challenges faced by clinicians and how vibrational spectroscopy has evolved to respond to those needs for the two types of cancer with the highest potential for improvement regarding an early diagnosis, surgical margin assessment and histopathology. Abstract In 2020, approximately 10 million people died of cancer, rendering this disease the second leading cause of death worldwide. Detecting cancer in its early stages is paramount for patients’ prognosis and survival. Hence, the scientific and medical communities are engaged in improving both therapeutic strategies and diagnostic methodologies, beyond prevention. Optical vibrational spectroscopy has been shown to be an ideal diagnostic method for early cancer diagnosis and surgical margins assessment, as a complement to histopathological analysis. Being highly sensitive, non-invasive and capable of real-time molecular imaging, Raman and Fourier transform infrared (FTIR) spectroscopies give information on the biochemical profile of the tissue under analysis, detecting the metabolic differences between healthy and cancerous portions of the same sample. This constitutes tremendous progress in the field, since the cancer-prompted morphological alterations often occur after the biochemical imbalances in the oncogenic process. Therefore, the early cancer-associated metabolic changes are unnoticed by the histopathologist. Additionally, Raman and FTIR spectroscopies significantly reduce the subjectivity linked to cancer diagnosis. This review focuses on breast and head and neck cancers, their clinical needs and the progress made to date using vibrational spectroscopy as a diagnostic technique prior to surgical intervention and intraoperative margin assessment.
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Tang J, Henderson A, Gardner P. Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets. Analyst 2021; 146:5880-5891. [PMID: 34570844 DOI: 10.1039/d0an02155e] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The use of infrared spectroscopy to augment decision-making in histopathology is a promising direction for the diagnosis of many disease types. Hyperspectral images of healthy and diseased tissue, generated by infrared spectroscopy, are used to build chemometric models that can provide objective metrics of disease state. It is important to build robust and stable models to provide confidence to the end user. The data used to develop such models can have a variety of characteristics which can pose problems to many model-building approaches. Here we have compared the performance of two machine learning algorithms - AdaBoost and Random Forests - on a variety of non-uniform data sets. Using samples of breast cancer tissue, we devised a range of training data capable of describing the problem space. Models were constructed from these training sets and their characteristics compared. In terms of separating infrared spectra of cancerous epithelium tissue from normal-associated tissue on the tissue microarray, both AdaBoost and Random Forests algorithms were shown to give excellent classification performance (over 95% accuracy) in this study. AdaBoost models were more robust when datasets with large imbalance were provided. The outcomes of this work are a measure of classification accuracy as a function of training data available, and a clear recommendation for choice of machine learning approach.
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Affiliation(s)
- Jiayi Tang
- Department of Chemical Engineering and Analytical Science, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
| | - Alex Henderson
- Department of Chemical Engineering and Analytical Science, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
| | - Peter Gardner
- Department of Chemical Engineering and Analytical Science, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
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12
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Liu Y, Liu C, Dang A, Sun S, Zhang D, Wang M, Chen F, Li Y, Xue R, Chen J, Lan Z. Pathological analysis and genetic characterization of the first outbreak H5N8 subtype avian influenza virus isolated from wild swan in Shandong, China. Transbound Emerg Dis 2021; 68:3200-3206. [PMID: 34359099 DOI: 10.1111/tbed.14279] [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: 05/13/2021] [Revised: 07/14/2021] [Accepted: 08/04/2021] [Indexed: 11/27/2022]
Abstract
In January 2021, an outbreak of H5N8 subtype avian influenza was identified from wild mute swans (Cygnus olor) in an overwintering site located within Yellow River Delta Nature Reserve in Shandong, China, for the first time. We sequenced the hemagglutinin (HA) and neuraminidase (NA) genome segments of the virus, named A/mute swan/Shandong/1/2021 (H5N8), and explored the genetic characterization and comparative phylogenetic analysis to track its origin. The wild swans prior to death showed nervous signs accompanied by a twisted neck. The major pathological changes of the heart, lung, trachea, kidney, spleen, and glandular stomach were hemorrhage, and extensive necrotic lesions in the liver and pancreas were also observed. Further analysis of the HA and NA phylogenetic trees generated by H5N8 strains isolated worldwide revealed that the isolate has a close genetic relationship with the H5N8 subtype avian influenza virus (AIV) recently isolated in Korea and Japan from 2020 to 2021, suggesting the potential role of mute swans in the global dissemination of the H5N8 subtype AIVs along the migration routes. Therefore, enhanced active surveillance in wild and domestic birds is required to monitor the introduction and spread of the H5N8 subtype AIV by migratory birds in order to decrease the risk of H5N8 outbreaks.
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Affiliation(s)
- Yanhan Liu
- Shandong Provincial Center for Animal Disease Control, Jinan, Shandong, China
| | - Cun Liu
- Shandong Provincial Center for Animal Disease Control, Jinan, Shandong, China
| | - Ankun Dang
- Shandong Provincial Center for Animal Disease Control, Jinan, Shandong, China
| | - Shengfu Sun
- Shandong Provincial Center for Animal Disease Control, Jinan, Shandong, China
| | - Dong Zhang
- Shandong Provincial Center for Animal Disease Control, Jinan, Shandong, China
| | - Miaoli Wang
- Shandong Provincial Center for Animal Disease Control, Jinan, Shandong, China
| | - Feng Chen
- Shandong Provincial Center for Animal Disease Control, Jinan, Shandong, China
| | - Yujie Li
- Shandong Provincial Center for Animal Disease Control, Jinan, Shandong, China
| | - Ruixue Xue
- Shandong Provincial Center for Animal Disease Control, Jinan, Shandong, China
| | - Jing Chen
- Shandong Provincial Center for Animal Disease Control, Jinan, Shandong, China
| | - Zouran Lan
- Shandong Provincial Center for Animal Disease Control, Jinan, Shandong, China
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13
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Kansiz M, Dowling LM, Yousef I, Guaitella O, Borondics F, Sulé-Suso J. Optical Photothermal Infrared Microspectroscopy Discriminates for the First Time Different Types of Lung Cells on Histopathology Glass Slides. Anal Chem 2021; 93:11081-11088. [PMID: 34355885 DOI: 10.1021/acs.analchem.1c00309] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The debate of whether a glass substrate can be used in Fourier transform infrared spectroscopy is strongly linked to its potential clinical application. Histopathology glass slides of 1 mm thickness absorb the mid-IR spectrum in the rich fingerprint spectral region. Thus, it is important to assess whether emerging IR techniques can be employed to study biological samples placed on glass substrates. For this purpose, we used optical photothermal infrared (O-PTIR) spectroscopy to study for the first time malignant and non-malignant lung cells with the purpose of identifying IR spectral differences between these cells placed on standard pathology glass slides. The data in this feasibility study showed that O-PTIR can be used to obtain good-quality IR spectra from cells from both the lipid region (3000-2700 cm-1) and the fingerprint region between 1770 and 950 cm-1 but with glass contributions from 1350 to 950 cm-1. A new single-unit dual-range (C-H/FP) quantum cascade laser (QCL) IR pump source was applied for the first time, delivering a clear synergistic benefit to the classification results. Furthermore, O-PTIR is able to distinguish between lung cancer cells and non-malignant lung cells both in the lipid and fingerprint regions. However, when these two spectral ranges are combined, classification accuracies are enhanced with Random Forest modeling classification accuracy results ranging from 96 to 99% across all three studied cell lines. The methodology described here for the first time with a single-unit dual-range QCL for O-PTIR on glass is another step toward its clinical application in pathology.
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Affiliation(s)
- Mustafa Kansiz
- Photothermal Spectroscopy Corp., 325 Chapala Street, Santa Barbara, California 93101, United States
| | - Lewis M Dowling
- School of Pharmacy and Bioengineering, Guy Hilton Research Centre, Keele University, Stoke-on-Trent ST4 7QB, U.K
| | - Ibraheem Yousef
- ALBA Synchrotron Light Source, Cerdanyola del Vallès, Barcelona 08290, Catalonia
| | - Olivier Guaitella
- Laboratoire de Physique des Plasmas, École Polytechnique-CNRS-Université Paris-Sud-Sorbonne Université, 91128 Palaiseau, France
| | - Ferenc Borondics
- Synchrotron SOLEIL, L'Orme des Merisiers, Saint-Aubin, BP 48, 91192 Gif-sur-Yvette Cedex, France
| | - Josep Sulé-Suso
- School of Pharmacy and Bioengineering, Guy Hilton Research Centre, Keele University, Stoke-on-Trent ST4 7QB, U.K.,Oncology Department, Cancer Centre, Royal Stoke University Hospital, University Hospitals of North Midlands, Stoke-on-Trent ST4 6QG, U.K
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14
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Goertzen N, Pappesch R, Fassunke J, Brüning T, Ko YD, Schmidt J, Großerueschkamp F, Buettner R, Gerwert K. Quantum Cascade Laser-Based Infrared Imaging as a Label-Free and Automated Approach to Determine Mutations in Lung Adenocarcinoma. THE AMERICAN JOURNAL OF PATHOLOGY 2021; 191:1269-1280. [PMID: 34004158 DOI: 10.1016/j.ajpath.2021.04.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/09/2021] [Accepted: 04/22/2021] [Indexed: 12/28/2022]
Abstract
Therapeutic decisions in lung cancer critically depend on the determination of histologic types and oncogene mutations. Therefore, tumor samples are subjected to standard histologic and immunohistochemical analyses and examined for relevant mutations using comprehensive molecular diagnostics. In this study, an alternative diagnostic approach for automatic and label-free detection of mutations in lung adenocarcinoma tissue using quantum cascade laser-based infrared imaging is presented. For this purpose, a five-step supervised classification algorithm was developed, which was not only able to detect tissue types and tumor lesions, but also the tumor type and mutation status of adenocarcinomas. Tumor detection was verified on a data set of 214 patient samples with a specificity of 97% and a sensitivity of 95%. Furthermore, histology typing was verified on samples from 203 of the 214 patients with a specificity of 97% and a sensitivity of 94% for adenocarcinoma. The most frequently occurring mutations in adenocarcinoma (KRAS, EGFR, and TP53) were differentiated by this technique. Detection of mutations was verified in 60 patient samples from the data set with a sensitivity and specificity of 95% for each mutation. This demonstrates that quantum cascade laser infrared imaging can be used to analyze morphologic differences as well as molecular changes. Therefore, this single, one-step measurement provides comprehensive diagnostics of lung cancer histology types and most frequent mutations.
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Affiliation(s)
- Nina Goertzen
- Center for Protein Diagnostics, Biospectroscopy, Germany; Department of Biophysics, Faculty of Biology and Biotechnology, Ruhr University Bochum, Bochum, Germany
| | | | - Jana Fassunke
- Institut für Pathologie, Universitätsklinikum Köln, Germany
| | - Thomas Brüning
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum, Bochum, Germany
| | - Yon-Dschun Ko
- Department of Internal Medicine, Johanniter-Kliniken Bonn GmbH, Johanniter Krankenhaus, Bonn, Germany
| | - Joachim Schmidt
- Lung Cancer Center Bonn, Department of Thoracic Surgery, Helios Klinikum Bonn/Rhein-Sieg and Department of Surgery, Division of Thoracic Surgery, Universitätsklinikum Bonn, Germany
| | - Frederik Großerueschkamp
- Center for Protein Diagnostics, Biospectroscopy, Germany; Department of Biophysics, Faculty of Biology and Biotechnology, Ruhr University Bochum, Bochum, Germany
| | | | - Klaus Gerwert
- Center for Protein Diagnostics, Biospectroscopy, Germany; Department of Biophysics, Faculty of Biology and Biotechnology, Ruhr University Bochum, Bochum, Germany.
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15
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Dowling LM, Roach P, Rutter AV, Yousef I, Pillai S, Latham D, van Pittius DG, Sulé-Suso J. Optimization of Sample Preparation Using Glass Slides for Spectral Pathology. APPLIED SPECTROSCOPY 2021; 75:343-350. [PMID: 32662291 PMCID: PMC7961677 DOI: 10.1177/0003702820945748] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 06/30/2020] [Indexed: 06/11/2023]
Abstract
The clinical translation of Fourier transform infrared (FT-IR) microspectroscopy in pathology will require bringing this technique as close as possible to standard practice in pathology departments. An important step is sample preparation for both FT-IR microspectroscopy and pathology. This should entail minimal disruption of standard clinical practice while achieving good quality FT-IR spectral data. In fact, the recently described possibility of obtaining FT-IR spectra of cells placed on glass substrates brings FT-IR microspectroscopy closer to a clinical application. We have now furthered this work in order to identify two different types of lung cancer cells placed on glass coverslips. Two types of sample preparation which are widely used in pathology, cytospin and smear, have been used. Samples were fixed with either methanol, used in pathology, or formalin (4% paraformaldehyde) used widely in spectroscopy. Fixation with methanol (alcohol-based fixative) removed lipids from cells causing a decrease in intensity of the peaks at 2850 cm-1 and 2920 cm-1. Nevertheless, we show for the first time that using either type of sample preparation and fixation on thin glass coverslips allowed to differentiate between two different types of lung cancer cells using either the lipid region or the fingerprint region ranging from 1800 cm-1 to 1350 cm-1. We believe that formalin-fixed cytospin samples would be preferred to study cells on thin coverslips using FT-IR microspectroscopy. This work presents a clear indication for future advances in clinical assessment of samples within pathology units to gain a deeper understanding of cells/tissues under investigation.
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Affiliation(s)
- Lewis M. Dowling
- School of Pharmacy and Bioengineering, Guy Hilton Research Centre, Keele University, Stoke-on-Trent, UK
| | - Paul Roach
- Department of Chemistry, Loughborough University, Leicestershire, UK
| | - Abigail V. Rutter
- School of Pharmacy and Bioengineering, Guy Hilton Research Centre, Keele University, Stoke-on-Trent, UK
| | - Ibraheem Yousef
- ALBA Synchrotron Light Source, Cerdanyola del Vallès, Barcelona, Catalonia
| | - Srinivas Pillai
- Haematology Department, Cancer Centre, Royal Stoke University Hospital, University Hospitals of North Midlands, Stoke-on-Trent, UK
| | - Deborah Latham
- Histopathology Department, Royal Stoke University Hospital, University Hospitals of North Midlands, Stoke-on-Trent, UK
| | - Daniel G. van Pittius
- Histopathology Department, Royal Stoke University Hospital, University Hospitals of North Midlands, Stoke-on-Trent, UK
| | - Josep Sulé-Suso
- School of Pharmacy and Bioengineering, Guy Hilton Research Centre, Keele University, Stoke-on-Trent, UK
- Oncology Department, Cancer Centre, Royal Stoke University Hospital, University Hospitals of North Midlands, Stoke-on-Trent, UK
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16
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Wang R, Wang Y. Fourier Transform Infrared Spectroscopy in Oral Cancer Diagnosis. Int J Mol Sci 2021; 22:1206. [PMID: 33530491 PMCID: PMC7865696 DOI: 10.3390/ijms22031206] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/19/2021] [Accepted: 01/21/2021] [Indexed: 12/13/2022] Open
Abstract
Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.
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Affiliation(s)
| | - Yong Wang
- School of Dentistry, University of Missouri–Kansas City, Kansas City, MO 64108, USA;
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17
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Tuck M, Blanc L, Touti R, Patterson NH, Van Nuffel S, Villette S, Taveau JC, Römpp A, Brunelle A, Lecomte S, Desbenoit N. Multimodal Imaging Based on Vibrational Spectroscopies and Mass Spectrometry Imaging Applied to Biological Tissue: A Multiscale and Multiomics Review. Anal Chem 2020; 93:445-477. [PMID: 33253546 DOI: 10.1021/acs.analchem.0c04595] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Michael Tuck
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Landry Blanc
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Rita Touti
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232-8575, United States
| | - Sebastiaan Van Nuffel
- Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Sandrine Villette
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Jean-Christophe Taveau
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Andreas Römpp
- Bioanalytical Sciences and Food Analysis, University of Bayreuth, Universitätsstraße 30, 95440 Bayreuth, Germany
| | - Alain Brunelle
- Laboratoire d'Archéologie Moléculaire et Structurale, LAMS UMR 8220, CNRS, Sorbonne Université, 4 Place Jussieu, 75005 Paris, France
| | - Sophie Lecomte
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Nicolas Desbenoit
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
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18
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Malonek D, Dekel BZ, Haran G, Reens-Carmel R, Groisman GM, Hallak M, Bruchim I. Rapid intraoperative diagnosis of gynecological cancer by ATR-FTIR spectroscopy of fresh tissue biopsy. JOURNAL OF BIOPHOTONICS 2020; 13:e202000114. [PMID: 32463546 DOI: 10.1002/jbio.202000114] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/25/2020] [Accepted: 05/26/2020] [Indexed: 06/11/2023]
Abstract
A rapid and reliable intraoperative diagnostic technique to support clinical decisions was developed using Fourier-transform infrared (FTIR) spectroscopy. Twenty-six fresh tissue samples were collected intraoperatively from patients undergoing gynecological surgeries. Frozen section (FS) histopathology aimed to discriminate between malignant and benign tumors was performed, and attenuated total reflection (ATR) FTIR spectra were collected from these samples. Digital dehydration and principal component analysis and linear discriminant analysis (PCA-LDA) models were developed to classify samples into malignant and benign groups. Two validation schemes were employed: k-fold and "leave one out." FTIR absorption spectrum of a fresh tissue sample was obtained in less than 5 minutes. The fingerprint spectral region of malignant tumors was consistently different from that of benign tumors. The PCA-LDA discrimination model correctly classified the samples into malignant and benign groups with accuracies of 96% and 93% for the k-fold and "leave one out" validation schemes, respectively. We showed that a simple tissue preparation followed by ATR-FTIR spectroscopy provides accurate means for very rapid tumor classification into malignant and benign gynecological tumors. With further development, the proposed method has high potential to be used as an adjunct to the intraoperative FS histopathology technique.
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Affiliation(s)
- Dov Malonek
- Ruppin Academic Center, Department of Electrical and Computer Engineering, Emek-Hefer, Israel
| | - Ben-Zion Dekel
- Ruppin Academic Center, Department of Electrical and Computer Engineering, Emek-Hefer, Israel
| | - Gabi Haran
- Gynecologic Oncology Division, Department of Obstetrics and Gynecology, Hillel Yaffe Medical Center, Hadera, Israel
- Technion Israel Institute of Technology, Haifa, Israel
| | - Renat Reens-Carmel
- Gynecologic Oncology Division, Department of Obstetrics and Gynecology, Hillel Yaffe Medical Center, Hadera, Israel
- Technion Israel Institute of Technology, Haifa, Israel
| | - Gabriel M Groisman
- Department of Pathology, Hillel Yaffe Medical Center, Hadera, Israel
- Technion Israel Institute of Technology, Haifa, Israel
| | - Mordechai Hallak
- Gynecologic Oncology Division, Department of Obstetrics and Gynecology, Hillel Yaffe Medical Center, Hadera, Israel
- Technion Israel Institute of Technology, Haifa, Israel
| | - Ilan Bruchim
- Gynecologic Oncology Division, Department of Obstetrics and Gynecology, Hillel Yaffe Medical Center, Hadera, Israel
- Technion Israel Institute of Technology, Haifa, Israel
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19
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Rutter AV, Crees J, Wright H, Raseta M, van Pittius DG, Roach P, Sulé-Suso J. Identification of a Glass Substrate to Study Cells Using Fourier Transform Infrared Spectroscopy: Are We Closer to Spectral Pathology? APPLIED SPECTROSCOPY 2020; 74:178-186. [PMID: 31517513 DOI: 10.1177/0003702819875828] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The rising incidence of cancer worldwide is causing an increase in the workload in pathology departments. This, coupled with advanced analysis methodologies, supports a developing need for techniques that could identify the presence of cancer cells in cytology and tissue samples in an objective, fast, and automated way. Fourier transform infrared (FT-IR) microspectroscopy can identify cancer cells in such samples objectively. Thus, it has the potential to become another tool to help pathologists in their daily work. However, one of the main drawbacks is the use of glass substrates by pathologists. Glass absorbs IR radiation, removing important mid-IR spectral data in the fingerprint region (1800 cm-1 to 900 cm-1). In this work, we hypothesized that, using glass coverslips of differing compositions, some regions within the fingerprint area could still be analyzed. We studied three different types of cells (peripheral blood mononuclear cells, a leukemia cell line, and a lung cancer cell line) and lymph node tissue placed on four different types of glass coverslips. The data presented here show that depending of the type of glass substrate used, information within the fingerprint region down to 1350 cm-1 can be obtained. Furthermore, using principal component analysis, separation between the different cell lines was possible using both the lipid region and the fingerprint region between 1800 cm-1 and 1350 cm-1. This work represents a further step towards the application of FT-IR microspectroscopy in histopathology departments.
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Affiliation(s)
- Abigail V Rutter
- Guy Hilton Research Centre, Keele University, Stoke-on-Trent, UK
| | - Jamie Crees
- Histopathology Department, Royal Derby Hospital, Derby, UK
| | - Helen Wright
- Directorate of Research, Innovation and Engagement, Keele University, Staffordshire, UK
| | - Marko Raseta
- Institute for Primary Care and Health Sciences and Research Design Service, Keele University, Staffordshire, UK
| | - Daniel G van Pittius
- Histopathology Department, Royal Stoke University Hospital, University Hospitals of North Midlands (UHNM), Stoke-on-Trent, UK
| | - Paul Roach
- Department of Chemistry, Loughborough University, Leicestershire, UK
| | - Josep Sulé-Suso
- Guy Hilton Research Centre, Keele University, Stoke-on-Trent, UK
- Oncology Department, Royal Stoke University Hospital, University Hospitals of North Midlands, Stoke-on-Trent, UK
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20
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Mankar R, Bueso-Ramos CE, Yin CC, Hidalgo-Lopez JE, Berisha S, Kansiz M, Mayerich D. Automated Osteosclerosis Grading of Clinical Biopsies Using Infrared Spectroscopic Imaging. Anal Chem 2020; 92:749-757. [PMID: 31793292 PMCID: PMC7055712 DOI: 10.1021/acs.analchem.9b03015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Osteosclerosis and myefibrosis are complications of myeloproliferative neoplasms. These disorders result in excess growth of trabecular bone and collagen fibers that replace hematopoietic cells, resulting in abnormal bone marrow function. Treatments using imatinib and JAK2 pathway inhibitors can be effective on osteosclerosis and fibrosis; therefore, accurate grading is critical for tracking treatment effectiveness. Current grading standards use a four-class system based on analysis of biopsies stained with three histological stains: hematoxylin and eosin (H&E), Masson's trichrome, and reticulin. However, conventional grading can be subjective and imprecise, impacting the effectiveness of treatment. In this Article, we demonstrate that mid-infrared spectroscopic imaging may serve as a quantitative diagnostic tool for quantitatively tracking disease progression and response to treatment. The proposed approach is label-free and provides automated quantitative analysis of osteosclerosis and collagen fibrosis.
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Affiliation(s)
- Rupali Mankar
- Department of Electrical and Computer Engineering, University of Houston, Houston, Texas 77004, United States
| | - Carlos E. Bueso-Ramos
- Department of Hematopathology, MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - C. Cameron Yin
- Department of Hematopathology, MD Anderson Cancer Center, Houston, Texas 77030, United States
| | | | - Sebastian Berisha
- Department of Electrical and Computer Engineering, University of Houston, Houston, Texas 77004, United States
| | - Mustafa Kansiz
- Photothermal Spectroscopy Corp., Santa Barbara, California 93101, United States
| | - David Mayerich
- Department of Electrical and Computer Engineering, University of Houston, Houston, Texas 77004, United States
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21
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Raczkowska MK, Koziol P, Urbaniak-Wasik S, Paluszkiewicz C, Kwiatek WM, Wrobel TP. Influence of denoising on classification results in the context of hyperspectral data: High Definition FT-IR imaging. Anal Chim Acta 2019; 1085:39-47. [DOI: 10.1016/j.aca.2019.07.045] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 07/16/2019] [Accepted: 07/22/2019] [Indexed: 12/31/2022]
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22
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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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23
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Mittal S, Bhargava R. A comparison of mid-infrared spectral regions on accuracy of tissue classification. Analyst 2019; 144:2635-2642. [PMID: 30839958 DOI: 10.1039/c8an01782d] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Infrared (IR) spectroscopic imaging, utilizing both the molecular and structural disease signatures, enables extensive profiling of tumors and their microenvironments. Here, we examine the relative merits of using either the fingerprint or the high frequency regions of the IR spectrum for tissue histopathology. We selected a complex model as a test case, evaluating both stromal and epithelial segmentation for various breast pathologies. IR spectral classification in each of these spectral windows is quantitatively assessed by estimating area under the curve (AUC) of the receiver operating characteristic curve (ROC) for pixel level accuracy and images for diagnostic ability. We found only small differences, though some that may be sufficiently important in diagnostic tasks to be clinically significant, between the two regions with the fingerprint region-based classifiers consistently emerging as more accurate. The work provides added evidence and comparison with fingerprint region, complex models, and previously untested tissue type (breast) - that the use of restricted spectral regions can provide high accuracy. Our study indicates that the fingerprint region is ideal for epithelial and stromal models to obtain high pixel level accuracies. Glass slides provide a limited spectral feature set but provides accurate information at the patient level.
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Affiliation(s)
- Shachi Mittal
- Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
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24
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de Boer LL, Kho E, Nijkamp J, Van de Vijver KK, Sterenborg HJCM, ter Beek LC, Ruers TJM. Method for coregistration of optical measurements of breast tissue with histopathology: the importance of accounting for tissue deformations. JOURNAL OF BIOMEDICAL OPTICS 2019; 24:1-12. [PMID: 31347338 PMCID: PMC6995961 DOI: 10.1117/1.jbo.24.7.075002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 07/09/2019] [Indexed: 05/24/2023]
Abstract
For the validation of optical diagnostic technologies, experimental results need to be benchmarked against the gold standard. Currently, the gold standard for tissue characterization is assessment of hematoxylin and eosin (H&E)-stained sections by a pathologist. When processing tissue into H&E sections, the shape of the tissue deforms with respect to the initial shape when it was optically measured. We demonstrate the importance of accounting for these tissue deformations when correlating optical measurement with routinely acquired histopathology. We propose a method to register the tissue in the H&E sections to the optical measurements, which corrects for these tissue deformations. We compare the registered H&E sections to H&E sections that were registered with an algorithm that does not account for tissue deformations by evaluating both the shape and the composition of the tissue and using microcomputer tomography data as an independent measure. The proposed method, which did account for tissue deformations, was more accurate than the method that did not account for tissue deformations. These results emphasize the need for a registration method that accounts for tissue deformations, such as the method presented in this study, which can aid in validating optical techniques for clinical use.
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Affiliation(s)
- Lisanne L. de Boer
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
| | - Esther Kho
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
| | - Jasper Nijkamp
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
| | - Koen K. Van de Vijver
- The Netherlands Cancer Institute, Department of Pathology, Amsterdam, The Netherlands
- Ghent University Hospital, Department of Pathology, Gent, Belgium
| | - Henricus J. C. M. Sterenborg
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
- Amsterdam University Medical Center, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | - Leon C. ter Beek
- The Netherlands Cancer Institute, Department of Medical Physics, Amsterdam, The Netherlands
| | - Theo J. M. Ruers
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
- University of Twente, Faculty of Science and Technology, Enschede, The Netherlands
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25
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Lotfollahi M, Berisha S, Daeinejad D, Mayerich D. Digital Staining of High-Definition Fourier Transform Infrared (FT-IR) Images Using Deep Learning. APPLIED SPECTROSCOPY 2019; 73:556-564. [PMID: 30657342 PMCID: PMC6499711 DOI: 10.1177/0003702818819857] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Histological stains, such as hematoxylin and eosin (H&E), are routinely used in clinical diagnosis and research. While these labels offer a high degree of specificity, throughput is limited by the need for multiple samples. Traditional histology stains, such as immunohistochemical labels, also rely only on protein expression and cannot quantify small molecules and metabolites that may aid in diagnosis. Finally, chemical stains and dyes permanently alter the tissue, making downstream analysis impossible. Fourier transform infrared (FT-IR) spectroscopic imaging has shown promise for label-free characterization of important tissue phenotypes and can bypass the need for many chemical labels. Fourier transform infrared classification commonly leverages supervised learning, requiring human annotation that is tedious and prone to errors. One alternative is digital staining, which leverages machine learning to map IR spectra to a corresponding chemical stain. This replaces human annotation with computer-aided alignment. Previous work relies on alignment of adjacent serial tissue sections. Since the tissue samples are not identical at the cellular level, this technique cannot be applied to high-definition FT-IR images. In this paper, we demonstrate that cellular-level mapping can be accomplished using identical samples for both FT-IR and chemical labels. In addition, higher-resolution results can be achieved using a deep convolutional neural network that integrates spatial and spectral features.
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Affiliation(s)
- Mahsa Lotfollahi
- Department of Electrical and Computer Engineering, University of Houston
| | - Sebastian Berisha
- Department of Electrical and Computer Engineering, University of Houston
| | - Davar Daeinejad
- Department of Electrical and Computer Engineering, University of Houston
| | - David Mayerich
- Department of Electrical and Computer Engineering, University of Houston
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Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections. Sci Rep 2018; 8:7717. [PMID: 29769696 PMCID: PMC5955970 DOI: 10.1038/s41598-018-26098-w] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 05/02/2018] [Indexed: 02/01/2023] Open
Abstract
A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.
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Pucetaite M, Velicka M, Urboniene V, Ceponkus J, Bandzeviciute R, Jankevicius F, Zelvys A, Sablinskas V, Steiner G. Rapid intra-operative diagnosis of kidney cancer by attenuated total reflection infrared spectroscopy of tissue smears. JOURNAL OF BIOPHOTONICS 2018; 11:e201700260. [PMID: 29316381 DOI: 10.1002/jbio.201700260] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 01/06/2018] [Indexed: 05/13/2023]
Abstract
Herein, a technique to analyze air-dried kidney tissue impression smears by means of attenuated total reflection infrared (ATR-IR) spectroscopy is presented. Spectral tumor markers-absorption bands of glycogen-are identified in the ATR-IR spectra of the kidney tissue smear samples. Thin kidney tissue cryo-sections currently used for IR spectroscopic analysis lack such spectral markers as the sample preparation causes irreversible molecular changes in the tissue. In particular, freeze-thaw cycle results in degradation of the glycogen and reduction or complete dissolution of its content. Supervised spectral classification was applied to the recorded spectra of the smears and the test spectra were classified with a high accuracy of 92% for normal tissue and 94% for tumor tissue, respectively. For further development, we propose that combination of the method with optical fiber ATR probes could potentially be used for rapid real-time intra-operative tissue analysis without interfering with either the established protocols of pathological examination or the ordinary workflow of operating surgeon. Such approach could ensure easier transition of the method to clinical applications where it may complement the results of gold standard histopathology examination and aid in more precise resection of kidney tumors.
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Affiliation(s)
- Milda Pucetaite
- Department of General Physics and Spectroscopy, Vilnius University, Vilnius, Lithuania
| | - Martynas Velicka
- Department of General Physics and Spectroscopy, Vilnius University, Vilnius, Lithuania
| | - Vidita Urboniene
- Department of General Physics and Spectroscopy, Vilnius University, Vilnius, Lithuania
| | - Justinas Ceponkus
- Department of General Physics and Spectroscopy, Vilnius University, Vilnius, Lithuania
| | - Rimante Bandzeviciute
- Department of General Physics and Spectroscopy, Vilnius University, Vilnius, Lithuania
| | - Feliksas Jankevicius
- Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Lithuanian National Cancer Institute, Vilnius, Lithuania
| | - Arunas Zelvys
- Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Valdas Sablinskas
- Department of General Physics and Spectroscopy, Vilnius University, Vilnius, Lithuania
| | - Gerald Steiner
- Department of General Physics and Spectroscopy, Vilnius University, Vilnius, Lithuania
- Faculty of Medicine Carl Gustav Carus, Clinical Sensoring and Monitoring, Dresden University of Technology, Dresden, Germany
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28
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Yi F, Yang L, Wang S, Guo L, Huang C, Xie Y, Xiao G. Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks. BMC Bioinformatics 2018; 19:64. [PMID: 29482496 PMCID: PMC5828328 DOI: 10.1186/s12859-018-2055-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 02/13/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Pathological angiogenesis has been identified in many malignancies as a potential prognostic factor and target for therapy. In most cases, angiogenic analysis is based on the measurement of microvessel density (MVD) detected by immunostaining of CD31 or CD34. However, most retrievable public data is generally composed of Hematoxylin and Eosin (H&E)-stained pathology images, for which is difficult to get the corresponding immunohistochemistry images. The role of microvessels in H&E stained images has not been widely studied due to their complexity and heterogeneity. Furthermore, identifying microvessels manually for study is a labor-intensive task for pathologists, with high inter- and intra-observer variation. Therefore, it is important to develop automated microvessel-detection algorithms in H&E stained pathology images for clinical association analysis. RESULTS In this paper, we propose a microvessel prediction method using fully convolutional neural networks. The feasibility of our proposed algorithm is demonstrated through experimental results on H&E stained images. Furthermore, the identified microvessel features were significantly associated with the patient clinical outcomes. CONCLUSIONS This is the first study to develop an algorithm for automated microvessel detection in H&E stained pathology images.
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Affiliation(s)
- Faliu Yi
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX 75390 USA
| | - Lin Yang
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX 75390 USA
- Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100023 P. R. China
| | - Shidan Wang
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX 75390 USA
| | - Lei Guo
- Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100023 P. R. China
| | - Chenglong Huang
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX 75390 USA
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX 75390 USA
- Department of Bioinformatics, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX 75390 USA
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX 75390 USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX 75390 USA
- Department of Bioinformatics, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX 75390 USA
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX 75390 USA
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Baker MJ, Byrne HJ, Chalmers J, Gardner P, Goodacre R, Henderson A, Kazarian SG, Martin FL, Moger J, Stone N, Sulé-Suso J. Clinical applications of infrared and Raman spectroscopy: state of play and future challenges. Analyst 2018; 143:1735-1757. [DOI: 10.1039/c7an01871a] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
This review examines the state-of-the-art of clinical applications of infrared absorption and Raman spectroscopy, outstanding challenges, and progress towards translation.
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Affiliation(s)
- Matthew J. Baker
- WestCHEM
- Technology and Innovation Centre
- Department of Pure and Applied Chemistry
- University of Strathclyde
- Glasgow G1 1RD
| | - Hugh J. Byrne
- FOCAS Research Institute
- Dublin Institute of Technology
- Dublin 8
- Ireland
| | | | - Peter Gardner
- Manchester Institute of Biotechnology (MIB)
- University of Manchester
- Manchester
- UK
| | - Royston Goodacre
- Manchester Institute of Biotechnology (MIB)
- University of Manchester
- Manchester
- UK
| | - Alex Henderson
- Manchester Institute of Biotechnology (MIB)
- University of Manchester
- Manchester
- UK
| | - Sergei G. Kazarian
- Department of Chemical Engineering
- Imperial College London
- South Kensington Campus
- London
- UK
| | - Francis L. Martin
- School of Pharmacy and Biomedical Sciences
- University of Central Lancashire
- Preston PR1 2HE
- UK
| | - Julian Moger
- Biomedical Physics
- School of Physics and Astronomy
- University of Exeter
- Exeter EX4 4QL
- UK
| | - Nick Stone
- Biomedical Physics
- School of Physics and Astronomy
- University of Exeter
- Exeter EX4 4QL
- UK
| | - Josep Sulé-Suso
- Institute for Science and Technology in Medicine
- Keele University
- Guy Hilton Research Centre
- Stoke on Trent ST4 7QB
- UK
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30
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Mayerhöfer TG, Pahlow S, Hübner U, Popp J. Removing interference-based effects from the infrared transflectance spectra of thin films on metallic substrates: a fast and wave optics conform solution. Analyst 2018; 143:3164-3175. [DOI: 10.1039/c8an00526e] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We provide a formalism that is able to correct electric field standing wave “artifacts” in infrared transflection spectra.
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Affiliation(s)
- Thomas G. Mayerhöfer
- Leibniz Institute of Photonic Technology (IPHT)
- D-07745 Jena
- Germany
- Institute of Physical Chemistry and Abbe Center of Photonics
- Friedrich Schiller University
| | - Susanne Pahlow
- Institute of Physical Chemistry and Abbe Center of Photonics
- Friedrich Schiller University
- Jena
- Germany
| | - Uwe Hübner
- Leibniz Institute of Photonic Technology (IPHT)
- D-07745 Jena
- Germany
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology (IPHT)
- D-07745 Jena
- Germany
- Institute of Physical Chemistry and Abbe Center of Photonics
- Friedrich Schiller University
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31
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Rutter AV, Crees J, Wright H, van Pittius DG, Yousef I, Sulé-Suso J. Fourier transform infrared spectra of cells on glass coverslips. A further step in spectral pathology. Analyst 2018; 143:5711-5717. [DOI: 10.1039/c8an01634h] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
FTIR spectra of cells on glass coverslips allows the study of the Amide I region.
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Affiliation(s)
- A. V. Rutter
- Keele University
- Guy Hilton Research Centre
- Stoke on Trent ST4 7QB
- UK
| | - J. Crees
- Histopathology Department
- Royal Stoke University Hospital
- University Hospitals of North Midlands (UHNM)
- Staffordshire ST4 6QG
- UK
| | - H. Wright
- Research and Development Department
- Royal Stoke University Hospital
- University Hospitals of North Midlands
- Staffordshire ST4 6QG
- UK
| | - D. G. van Pittius
- Histopathology Department
- Royal Stoke University Hospital
- University Hospitals of North Midlands (UHNM)
- Staffordshire ST4 6QG
- UK
| | | | - J. Sulé-Suso
- Keele University
- Guy Hilton Research Centre
- Stoke on Trent ST4 7QB
- UK
- Oncology Department
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