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Fitzgerald S, Akhtar J, Schartner E, Ebendorff-Heidepriem H, Mahadevan-Jansen A, Li J. Multimodal Raman spectroscopy and optical coherence tomography for biomedical analysis. JOURNAL OF BIOPHOTONICS 2023; 16:e202200231. [PMID: 36308009 PMCID: PMC10082563 DOI: 10.1002/jbio.202200231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 10/19/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Optical techniques hold great potential to detect and monitor disease states as they are a fast, non-invasive toolkit. Raman spectroscopy (RS) in particular is a powerful label-free method capable of quantifying the biomolecular content of tissues. Still, spontaneous Raman scattering lacks information about tissue morphology due to its inability to rapidly assess a large field of view. Optical Coherence Tomography (OCT) is an interferometric optical method capable of fast, depth-resolved imaging of tissue morphology, but lacks detailed molecular contrast. In many cases, pairing label-free techniques into multimodal systems allows for a more diverse field of applications. Integrating RS and OCT into a single instrument allows for both structural imaging and biochemical interrogation of tissues and therefore offers a more comprehensive means for clinical diagnosis. This review summarizes the efforts made to date toward combining spontaneous RS-OCT instrumentation for biomedical analysis, including insights into primary design considerations and data interpretation.
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Affiliation(s)
- Sean Fitzgerald
- Vanderbilt Biophotonics Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Jobaida Akhtar
- School of Physical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, South Australia, Australia
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, South Australia, Australia
| | - Erik Schartner
- School of Physical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, South Australia, Australia
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, South Australia, Australia
| | - Heike Ebendorff-Heidepriem
- School of Physical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, South Australia, Australia
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, South Australia, Australia
| | - Anita Mahadevan-Jansen
- Vanderbilt Biophotonics Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Jiawen Li
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, South Australia, Australia
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Adelaide, South Australia, Australia
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, South Australia, Australia
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Avci E, Yilmaz H, Sahiner N, Tuna BG, Cicekdal MB, Eser M, Basak K, Altıntoprak F, Zengin I, Dogan S, Çulha M. Label-Free Surface Enhanced Raman Spectroscopy for Cancer Detection. Cancers (Basel) 2022; 14:cancers14205021. [PMID: 36291805 PMCID: PMC9600112 DOI: 10.3390/cancers14205021] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/04/2022] [Accepted: 10/10/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Blood is considered a rich reservoir of biomarkers for disease diagnosis. Surface-enhanced Raman scattering (SERS) is known for its high sensitivity and has been successfully employed to differentiate blood samples from cancer patients versus healthy individuals. Different from previous reports, this study aims at investigating the reliability of the observed results by varying several parameters influencing the observed spectra. Thus, blood taken from 30 healthy individuals as the control group, 30 patients with different types of cancers, and 15 patients with various types of chronic diseases were used in the study. The results revealed that spectral differences in the cancer group was directly related to the presence of cancer-related biomarkers. Although data were obtained from only small group of patients, the recorded sensitivity and specificity values clearly show the power of the technique to detect cancer. Abstract Blood is a vital reservoir housing numerous disease-related metabolites and cellular components. Thus, it is also of interest for cancer diagnosis. Surface-enhanced Raman spectroscopy (SERS) is widely used for molecular detection due to its very high sensitivity and multiplexing properties. Its real potential for cancer diagnosis is not yet clear. In this study, using silver nanoparticles (AgNPs) as substrates, a number of experimental parameters and scenarios were tested to disclose the potential for this technique for cancer diagnosis. The discrimination of serum samples from cancer patients, healthy individuals and patients with chronic diseases was successfully demonstrated with over 90% diagnostic accuracies. Moreover, the SERS spectra of the blood serum samples obtained from cancer patients before and after tumor removal were compared. It was found that the spectral pattern for serum from cancer patients evolved into the spectral pattern observed with serum from healthy individuals after the removal of tumors. The data strongly suggests that the technique has a tremendous potential for cancer detection and screening bringing the possibility of early detection onto the table.
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Affiliation(s)
- Ertug Avci
- Department of Genetics and Bioengineering, Faculty of Engineering, Yeditepe University, Istanbul 34755, Turkey
| | - Hulya Yilmaz
- Sabanci University Nanotechnology Research and Application Center (SUNUM), Istanbul 34956, Turkey
| | - Nurettin Sahiner
- Department of Ophthalmology, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA
- Department of Chemistry, Canakkale Onsekiz Mart University, Canakkale 17020, Turkey
| | - Bilge Guvenc Tuna
- Department of Biophysics, School of Medicine, Yeditepe University, Istanbul 34755, Turkey
| | - Munevver Burcu Cicekdal
- Department of Medical Biology, School of Medicine, Yeditepe University, Istanbul 34755, Turkey
| | - Mehmet Eser
- Department of General Surgery, School of Medicine, Istinye University, Istanbul 34010, Turkey
| | - Kayhan Basak
- Department of Pathology, Kartal Dr. Lütfi Kırdar City Hospital, University of Health Sciences, Istanbul 34865, Turkey
| | - Fatih Altıntoprak
- Department of General Surgery, Research and Educational Hospital, Sakarya University, Serdivan 54100, Turkey
| | - Ismail Zengin
- Department of General Surgery, Research and Educational Hospital, Sakarya University, Serdivan 54100, Turkey
| | - Soner Dogan
- Department of Medical Biology, School of Medicine, Yeditepe University, Istanbul 34755, Turkey
| | - Mustafa Çulha
- Sabanci University Nanotechnology Research and Application Center (SUNUM), Istanbul 34956, Turkey
- The Knight Cancer Institute, Cancer Early Detection Advanced Research Center (CEDAR), Oregon Health and Science University, Portland, OR 97239, USA
- Department of Chemistry and Physics, College of Science and Mathematics, Augusta University, Augusta, GA 30912, USA
- Correspondence: or or
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A Comparison of PCA-LDA and PLS-DA Techniques for Classification of Vibrational Spectra. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115345] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Vibrational spectroscopies provide information about the biochemical and structural environment of molecular functional groups inside samples. Over the past few decades, Raman and infrared-absorption-based techniques have been extensively used to investigate biological materials under different pathological conditions. Interesting results have been obtained, so these techniques have been proposed for use in a clinical setting for diagnostic purposes, as complementary tools to conventional cytological and histological techniques. In most cases, the differences between vibrational spectra measured for healthy and diseased samples are small, even if these small differences could contain useful information to be used in the diagnostic field. Therefore, the interpretation of the results requires the use of analysis techniques able to highlight the minimal spectral variations that characterize a dataset of measurements acquired on healthy samples from a dataset of measurements relating to samples in which a pathology occurs. Multivariate analysis techniques, which can handle large datasets and explore spectral information simultaneously, are suitable for this purpose. In the present study, two multivariate statistical techniques, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were used to analyse three different datasets of vibrational spectra, each one including spectra of two different classes: (i) a simulated dataset comprising control-like and exposed-like spectra, (ii) a dataset of Raman spectra measured for control and proton beam-exposed MCF10A breast cells and (iii) a dataset of FTIR spectra measured for malignant non-metastatic MCF7 and metastatic MDA-MB-231 breast cancer cells. Both PCA-LDA and PLS-DA techniques were first used to build a discrimination model by using calibration sets of spectra extracted from the three datasets. Then, the classification performance was established by using test sets of unknown spectra. The achieved results point out that the built classification models were able to distinguish the different spectra types with accuracy between 93% and 100%, sensitivity between 86% and 100% and specificity between 90% and 100%. The present study confirms that vibrational spectroscopy combined with multivariate analysis techniques has considerable potential for establishing reliable diagnostic models.
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Naqvi SMZA, Zhang Y, Ahmed S, Abdulraheem MI, Hu J, Tahir MN, Raghavan V. Applied surface enhanced Raman Spectroscopy in plant hormones detection, annexation of advanced technologies: A review. Talanta 2022; 236:122823. [PMID: 34635213 DOI: 10.1016/j.talanta.2021.122823] [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: 06/02/2021] [Revised: 08/20/2021] [Accepted: 08/22/2021] [Indexed: 12/13/2022]
Abstract
Plant hormones are the molecules that control the vigorous development of plants and help to cope with the stress conditions efficiently due to vital and mechanized physiochemical regulations. Biologists and analytical chemists, both endorsed the extreme problems to quantify plant hormones due to their low level existence in plants and the technological support is devastatingly required to established reliable and efficient detection methods of plant hormones. Surface Enhanced Raman Spectroscopy (SERS) technology is becoming vigorously favored and can be used to accurately and specifically identify biological and chemical molecules. Subsistence molecular properties with varying excitation wavelength require the pertinent substrate to detect SERS signals from plant hormones. Three typical mechanisms of Raman signal enhancement have been discovered, electromagnetic, chemical and Tip-enhanced Raman spectroscopy (TERS). Though, complex detection samples hinder in consistent and reproducible results of SERS-based technology. However, different algorithmic models applied on preprocessed data enhanced the prediction performances of Raman spectra by many folds and decreased the fluorescence value. By incorporating SERS measurements into the microfluidic platform, further highly repeatable SERS results can be obtained. This review paper tends to study the fundamental working principles, methods, applications of SERS systems and their execution in experiments of rapid determination of plant hormones as well as several ways of integrated SERS substrates. The challenges to develop an SERS-microfluidic framework with reproducible and accurate results for plant hormone detection are discussed comprehensively and highlighted the key areas for future investigation briefly.
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Affiliation(s)
- Syed Muhammad Zaigham Abbas Naqvi
- Department of Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou, 450002, China.
| | - Yanyan Zhang
- Department of Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou, 450002, China.
| | - Shakeel Ahmed
- Department of Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou, 450002, China.
| | - Mukhtar Iderawumi Abdulraheem
- Department of Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou, 450002, China; Oyo State College of Education, Lanlate, 202001, Nigeria.
| | - Jiandong Hu
- Department of Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, Zhengzhou, 450002, China.
| | - Muhammad Naveed Tahir
- Department of Agronomy, PMAS-Arid Agriculture University Rawalpindi, 46300, Pakistan.
| | - Vijaya Raghavan
- Department of Bioresource Engineering, Faculty of Agriculture and Environmental Studies, McGill University, Sainte-Anne-de-Bellevue, QC, H9X 3V9, Canada
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5
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Cialla-May D, Krafft C, Rösch P, Deckert-Gaudig T, Frosch T, Jahn IJ, Pahlow S, Stiebing C, Meyer-Zedler T, Bocklitz T, Schie I, Deckert V, Popp J. Raman Spectroscopy and Imaging in Bioanalytics. Anal Chem 2021; 94:86-119. [PMID: 34920669 DOI: 10.1021/acs.analchem.1c03235] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Dana Cialla-May
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Christoph Krafft
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Tanja Deckert-Gaudig
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Torsten Frosch
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Izabella J Jahn
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Susanne Pahlow
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Clara Stiebing
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Tobias Meyer-Zedler
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Thomas Bocklitz
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Iwan Schie
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Ernst-Abbe-Hochschule Jena, University of Applied Sciences, Department of Biomedical Engineering and Biotechnology, Carl-Zeiss-Promenade 2, 07745 Jena, Germany
| | - Volker Deckert
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Jürgen Popp
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
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6
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Guan H, Huang C, Lu D, Chen G, Lin J, Hu J, He Y, Huang Z. Label-free Raman spectroscopy: A potential tool for early diagnosis of diabetic keratopathy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 256:119731. [PMID: 33819764 DOI: 10.1016/j.saa.2021.119731] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 06/12/2023]
Abstract
Diabetes has become a major public health problem worldwide, and the incidence of diabetes has been increasing progressively. Diabetes is prone to cause various complications, among which diabetic keratopathy (DK) emphasizes the significant impact on the cornea. The current diagnosis of DK lacks biochemical markers that can be used for early and non-invasive screening and detection. In contrast, in this study, Raman spectroscopy, which demonstrates non-destructive, label-free features, especially the unique advantage of providing molecular fingerprint information for target substances, were utilized to interrogate the intrinsic information of the corneal tissues from normal and diabetic mouse models, respectively. Visually, the Raman spectral response derived from the biochemical components and biochemical differences between the two groups were compared. Moreover, multivariate analysis methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were carried out for advanced statistical analysis. PCA yields a diagnostic results of 57.4% sensitivity, 89.2% specificity, 74.8% accuracy between the diabetic group and control group; Moreover, PLS-DA was employed to enhance the diagnostic ability, showing 76.1% sensitivity, 86.1% specificity, and 87.6% accuracy between the diabetic group and control group. Our proof-of-concept results show the potential of Raman spectroscopy-based techniques to help explore the underlying pathogenesis of DK disease and thus be further expanded for potential applications in the early screening of diabetic diseases.
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Affiliation(s)
- Haohao Guan
- Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Chunyan Huang
- Department of Ophthalmology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Dechan Lu
- Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Guannan Chen
- Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Juqiang Lin
- Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Jianzhang Hu
- Department of Ophthalmology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Youwu He
- Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
| | - Zufang Huang
- Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China.
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Hook AL, Hogwood J, Gray E, Mulloy B, Merry CLR. High sensitivity analysis of nanogram quantities of glycosaminoglycans using ToF-SIMS. Commun Chem 2021; 4:67. [PMID: 36697531 PMCID: PMC9814553 DOI: 10.1038/s42004-021-00506-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 04/07/2021] [Indexed: 01/28/2023] Open
Abstract
Glycosaminoglycans (GAGs) are important biopolymers that differ in the sequence of saccharide units and in post polymerisation alterations at various positions, making these complex molecules challenging to analyse. Here we describe an approach that enables small quantities (<200 ng) of over 400 different GAGs to be analysed within a short time frame (3-4 h). Time of flight secondary ion mass spectrometry (ToF-SIMS) together with multivariate analysis is used to analyse the entire set of GAG samples. Resultant spectra are derived from the whole molecules and do not require pre-digestion. All 6 possible GAG types are successfully discriminated, both alone and in the presence of fibronectin. We also distinguish between pharmaceutical grade heparin, derived from different animal species and from different suppliers, to a sensitivity as low as 0.001 wt%. This approach is likely to be highly beneficial in the quality control of GAGs produced for therapeutic applications and for characterising GAGs within biomaterials or from in vitro cell culture.
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Affiliation(s)
- Andrew L. Hook
- grid.4563.40000 0004 1936 8868Advanced Materials and Healthcare Technology, University of Nottingham, Nottingham, UK
| | - John Hogwood
- grid.70909.370000 0001 2199 6511National Institute for Biological Standards and Control, Potters Bar, UK
| | - Elaine Gray
- grid.70909.370000 0001 2199 6511National Institute for Biological Standards and Control, Potters Bar, UK ,grid.13097.3c0000 0001 2322 6764Institute for Pharmaceutical Science, King’s College London, Franklin-Wilkins Building, Stamford Street, London, UK
| | - Barbara Mulloy
- grid.13097.3c0000 0001 2322 6764Institute for Pharmaceutical Science, King’s College London, Franklin-Wilkins Building, Stamford Street, London, UK
| | - Catherine L. R. Merry
- grid.4563.40000 0004 1936 8868Stem Cell Glycobiology Group, Biodiscovery Institute, Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
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Wang K, Bian X, Tan X, Wang H, Li Y. A new ensemble modeling method for multivariate calibration of near infrared spectra. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:1374-1380. [PMID: 33650616 DOI: 10.1039/d1ay00017a] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Ensemble modeling has gained increasing attention for improving the performance of quantitative models in near infrared (NIR) spectral analysis. Based on Monte Carlo (MC) resampling, least absolute shrinkage and selection operator (LASSO) and partial least squares (PLS), a new ensemble strategy named MC-LASSO-PLS is proposed for NIR spectral multivariate calibration. In this method, the training subsets for building the sub-models are generated by sampling from both samples and variables to ensure the diversity of the models. In detail, a certain number of samples as sample subsets are randomly selected from training set. Then, LASSO is used to shrink the variables of the sample subset to form the training subset, which is used to build the PLS sub-model. This process is repeated N times and N sub-models are obtained. Finally, the predictions of these sub-models are used to produce the final prediction by simple average. The prediction ability of the proposed method was compared with those of LASSO-PLS, MC-PLS and PLS models on the NIR spectra of corn, blend oil and orange juice samples. The superiority of MC-LASSO-PLS in prediction ability is demonstrated.
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Affiliation(s)
- Kaiyi Wang
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, P. R. China.
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9
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Gautam R, Peoples D, Jansen K, O'Connor M, Thomas G, Vanga S, Pence IJ, Mahadevan-Jansen A. Feature Selection and Rapid Characterization of Bloodstains on Different Substrates. APPLIED SPECTROSCOPY 2020; 74:1238-1251. [PMID: 32519560 DOI: 10.1177/0003702820937776] [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] [Indexed: 05/06/2023]
Abstract
Establishing the precise timeline of a crime can be challenging as current analytical techniques used suffer from many limitations and are destructive to the body fluids encountered at crime scenes. Raman spectroscopy has demonstrated excellent potential in forensic science as it provides direct information about the structural and molecular changes without the need for processing or extracting samples. However, its current applicability is limited to pure body fluids, as signals from the substrate underlying these fluids greatly influence the current models used for age estimation. In this study, we utilized Raman spectroscopy to identify selective spectral markers that delineate the bloodstain age in the presence of interfering signals from the substrate. The pure bloodstains and the bloodstains on the substrate were aged for two weeks at 21 ± 2 ℃ in the dark. Least absolute shrinkage and selection operator (LASSO) regression was employed to guide the feature selection in the presence of interference from substrates to accurately predict the bloodstain age. Substrate-specific regression models guided by an automated feature selection algorithm yielded low values of predictive root mean square error (0.207, 0.204, 0.222 h in logarithmic scale) and high R2 (0.924, 0.926, 0.913) on test data consisting of blood spectra on floor tile, facial tissue, and linoleum-polymer substrates, respectively. This framework for an automated feature selection algorithm relies entirely on pure bloodstain spectra to train substrate-specific models for estimating the age of composite (blood on substrate) spectra. The model can thus be easily applied to any new composite spectra and is highly scalable to new environments. This study demonstrates that Raman spectroscopy coupled with LASSO could serve as a reliable and nondestructive technique to determine the age of bloodstains on any surface while aiding forensic investigations in real-world scenarios.
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Affiliation(s)
- Rekha Gautam
- Department of Biomedical Engineering, 5718Vanderbilt University, Nashville, USA
| | - Deandra Peoples
- Department of Biomedical Engineering, 5718Vanderbilt University, Nashville, USA
| | - Kiana Jansen
- Department of Biomedical Engineering, 5718Vanderbilt University, Nashville, USA
| | - Maggie O'Connor
- Department of Biomedical Engineering, 5718Vanderbilt University, Nashville, USA
| | - Giju Thomas
- Department of Biomedical Engineering, 5718Vanderbilt University, Nashville, USA
| | | | - Isaac J Pence
- Department of Biomedical Engineering, 5718Vanderbilt University, Nashville, USA
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Liang X, Miao X, Xiao W, Ye Q, Wang S, Lin J, Li C, Huang Z. Filter-Membrane-Based Ultrafiltration Coupled with Surface-Enhanced Raman Spectroscopy for Potential Differentiation of Benign and Malignant Thyroid Tumors from Blood Plasma. Int J Nanomedicine 2020; 15:2303-2314. [PMID: 32280222 PMCID: PMC7132009 DOI: 10.2147/ijn.s233663] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 02/01/2020] [Indexed: 12/24/2022] Open
Abstract
Objective The objective of this study is to evaluate the performance and feasibility of surface-enhanced Raman spectroscopy coupled with a filter membrane and advanced multivariate data analysis on identifying and differentiating benign and malignant thyroid tumors from blood plasma. Patients and Methods We proposed a membrane filter SERS technology for the differentiation between benign thyroid tumor and thyroid cancer. That is to say, by using filter membranes with optimal pore size, the blood plasma samples from thyroid tumor patients were pretreated with the macromolecular proteins being filtered out prior to SERS measurement. The SERS spectra of blood plasma ultrafiltrate obtained using filter membranes from 102 patients with thyroid tumors (70 thyroid cancers and 32 benign thyroid tumors) were then analyzed and compared. Two multivariate statistical analyses, principal component analysis-linear discriminate analysis (PCA-LDA) and Lasso-partial least squares-discriminant analysis (Lasso-PLS-DA), were performed on the SERS spectral data after background subtraction and normalization, as well as the first derivative processing, to analyze and compare the differential diagnosis of benign thyroid tumors and thyroid cancer. Results SERS measurements were performed in blood plasma acquired from a total of 102 thyroid tumor patients (benign thyroid tumor N=32; thyroid cancer N=70). By using filter membranes, the macromolecular proteins in blood plasma were effectively filtered out to yield high-quality SERS spectra. 84.3% discrimination accuracy between benign and malignant thyroid tumor was achieved using PCA-LDA method, while Lasso-PLS-DA yields a discrimination accuracy of 90.2%. Conclusion Our results demonstrate that SERS spectroscopy, coupled with ultrafiltration and multivariate analysis has the potential of providing a non-invasive, rapid, and objective detection and differentiation of benign and malignant thyroid tumors.
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Affiliation(s)
- Xiaozhou Liang
- Fujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, People's Republic of China
| | - Xuchao Miao
- Fujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, People's Republic of China
| | - Weijin Xiao
- Department of Pathology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, People's Republic of China
| | - Qin Ye
- Department of Head and Neck Surgery, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, People's Republic of China
| | - Sisi Wang
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, People's Republic of China
| | - Juqiang Lin
- Fujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, People's Republic of China
| | - Chao Li
- Department of Pathology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, People's Republic of China.,Department of Pathology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, People's Republic of China.,Fujian Provincial Key Laboratory of Translational Cancer Medicine, Fuzhou, People's Republic of China
| | - Zufang Huang
- Fujian Normal University, Ministry of Education, Key Laboratory of Optoelectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fuzhou, People's Republic of China
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11
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Ibrahim J, Op de Beeck K, Fransen E, Peeters M, Van Camp G. The Gasdermin E Gene Has Potential as a Pan-Cancer Biomarker, While Discriminating between Different Tumor Types. Cancers (Basel) 2019; 11:cancers11111810. [PMID: 31752152 PMCID: PMC6896019 DOI: 10.3390/cancers11111810] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 11/04/2019] [Accepted: 11/06/2019] [Indexed: 01/08/2023] Open
Abstract
Due to the elevated rates of incidence and mortality of cancer, early and accurate detection is crucial for achieving optimal treatment. Molecular biomarkers remain important screening and detection tools, especially in light of novel blood-based assays. DNA methylation in cancer has been linked to tumorigenesis, but its value as a biomarker has not been fully explored. In this study, we have investigated the methylation patterns of the Gasdermin E gene across 14 different tumor types using The Cancer Genome Atlas (TCGA) methylation data (N = 6502). We were able to identify six CpG sites that could effectively distinguish tumors from normal samples in a pan-cancer setting (AUC = 0.86). This combination of pan-cancer biomarkers was validated in six independent datasets (AUC = 0.84–0.97). Moreover, we tested 74,613 different combinations of six CpG probes, where we identified tumor-specific signatures that could differentiate one tumor type versus all the others (AUC = 0.79–0.98). In all, methylation patterns exhibited great variation between cancer and normal tissues, but were also tumor specific. Our analyses highlight that a Gasdermin E methylation biomarker assay, not only has the potential for being a methylation-specific pan-cancer detection marker, but it also possesses the capacity to discriminate between different types of tumors.
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Affiliation(s)
- Joe Ibrahim
- Centre of Medical Genetics, University of Antwerp and Antwerp University Hospital, Prins Boudewijnlaan 43, 2650 Edegem, Belgium; (J.I.); (K.O.d.B.); (E.F.)
- Centre for Oncological Research, University of Antwerp and Antwerp University Hospital, Wilrijkstraat 10, 2650 Edegem, Belgium;
| | - Ken Op de Beeck
- Centre of Medical Genetics, University of Antwerp and Antwerp University Hospital, Prins Boudewijnlaan 43, 2650 Edegem, Belgium; (J.I.); (K.O.d.B.); (E.F.)
- Centre for Oncological Research, University of Antwerp and Antwerp University Hospital, Wilrijkstraat 10, 2650 Edegem, Belgium;
| | - Erik Fransen
- Centre of Medical Genetics, University of Antwerp and Antwerp University Hospital, Prins Boudewijnlaan 43, 2650 Edegem, Belgium; (J.I.); (K.O.d.B.); (E.F.)
- StatUa Centre for Statistics, University of Antwerp, 2000 Antwerp, Belgium
| | - Marc Peeters
- Centre for Oncological Research, University of Antwerp and Antwerp University Hospital, Wilrijkstraat 10, 2650 Edegem, Belgium;
- Department of Medical Oncology, Antwerp University Hospital, Wilrijkstraat 10, 2650 Edegem, Belgium
| | - Guy Van Camp
- Centre of Medical Genetics, University of Antwerp and Antwerp University Hospital, Prins Boudewijnlaan 43, 2650 Edegem, Belgium; (J.I.); (K.O.d.B.); (E.F.)
- Centre for Oncological Research, University of Antwerp and Antwerp University Hospital, Wilrijkstraat 10, 2650 Edegem, Belgium;
- Correspondence: ; Tel.: +32-3275-9762
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12
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Li P, Zhou B, Cao X, Tang X, Yang L, Hu L, Liu J. Functionalized Acupuncture Needle as Surface-Enhanced Resonance Raman Spectroscopy Sensor for Rapid and Sensitive Detection of Dopamine in Serum and Cerebrospinal Fluid. Chemistry 2017; 23:14278-14285. [DOI: 10.1002/chem.201702607] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Indexed: 02/01/2023]
Affiliation(s)
- Pan Li
- Institute of Intelligent Machines Institution; Chinese Academy of Sciences; Hefei 230031 P.R. China
| | - Binbin Zhou
- Institute of Intelligent Machines Institution; Chinese Academy of Sciences; Hefei 230031 P.R. China
| | - Xiaomin Cao
- Institute of Intelligent Machines Institution; Chinese Academy of Sciences; Hefei 230031 P.R. China
| | - Xianghu Tang
- Institute of Intelligent Machines Institution; Chinese Academy of Sciences; Hefei 230031 P.R. China
| | - Liangbao Yang
- Institute of Intelligent Machines Institution; Chinese Academy of Sciences; Hefei 230031 P.R. China
| | - Ling Hu
- School of Acupuncture and Osteology; Institution Anhui University of Chinese Medicine; No. 103 Meishan Road Hefei P.R. China
| | - Jinhuai Liu
- Institute of Intelligent Machines Institution; Chinese Academy of Sciences; Hefei 230031 P.R. China
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