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Georgiev D, Pedersen SV, Xie R, Fernández-Galiana Á, Stevens MM, Barahona M. RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis. Anal Chem 2024; 96:8492-8500. [PMID: 38747470 PMCID: PMC11140669 DOI: 10.1021/acs.analchem.4c00383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/29/2024]
Abstract
Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardization, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of tools for spectroscopic analysis that supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning in Python. RamanSPy is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.
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Affiliation(s)
- Dimitar Georgiev
- Department
of Computing & UKRI Centre
for Doctoral Training in AI for Healthcare, Imperial College London, London SW7 2AZ, United
Kingdom
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Simon Vilms Pedersen
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Ruoxiao Xie
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Álvaro Fernández-Galiana
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Molly M. Stevens
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Mauricio Barahona
- Department
of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
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2
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Iglesias G, Talavera E, Troya J, Díaz-Álvarez A, García-Remesal M. Artificial intelligence model for tumoral clinical decision support systems. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 253:108228. [PMID: 38810378 DOI: 10.1016/j.cmpb.2024.108228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 04/21/2024] [Accepted: 05/13/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND AND OBJECTIVE Comparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system is able of retrieving the most similar cases of brain tumors for a given query. The primary objective is to enhance the diagnostic process by generating more accurate representations of medical images, with a particular focus on patient-specific normal features and pathologies. A key distinction from previous models lies in its ability to produce enriched image descriptors solely from binary information, eliminating the need for costly and difficult to obtain tumor segmentation. METHODS The proposed model uses Artificial Intelligence to detect patient features to recommend the most similar cases from a database. The system not only suggests similar cases but also balances the representation of healthy and abnormal features in its design. This not only encourages the generalization of its use but also aids clinicians in their decision-making processes. This generalization makes possible for future research in different medical diagnosis areas with almost not any change in the system. RESULTS We conducted a comparative analysis of our approach in relation to similar studies. The proposed architecture obtains a Dice coefficient of 0.474 in both tumoral and healthy regions of the patients, which outperforms previous literature. Our proposed model excels at extracting and combining anatomical and pathological features from brain Magnetic Resonances (MRs), achieving state-of-the-art results while relying on less expensive label information. This substantially reduces the overall cost of the training process. Our findings highlight the significant potential for improving the efficiency and accuracy of comparative diagnostics and the treatment of tumoral pathologies. CONCLUSIONS This paper provides substantial grounds for further exploration of the broader applicability and optimization of the proposed architecture to enhance clinical decision-making. The novel approach presented in this work marks a significant advancement in the field of medical diagnosis, particularly in the context of Artificial Intelligence-assisted image retrieval, and promises to reduce costs and improve the quality of patient care using Artificial Intelligence as a support tool instead of a black box system.
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Affiliation(s)
- Guillermo Iglesias
- Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, Spain.
| | - Edgar Talavera
- Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, Spain.
| | - Jesús Troya
- Infanta Leonor University Hospital. Madrid., Spain
| | - Alberto Díaz-Álvarez
- Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, Spain.
| | - Miguel García-Remesal
- Biomedical Informatics Group, Departamento de Inteligencia Artificial, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Spain.
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3
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Mi Y, Li X, Zeng X, Cai Y, Sun X, Yan Y, Jiang Y. Diagnosis of neuropsychiatric systemic lupus erythematosus by label-free serum microsphere-coupled SERS fingerprints with machine learning. Biosens Bioelectron 2024; 260:116414. [PMID: 38815463 DOI: 10.1016/j.bios.2024.116414] [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: 01/27/2024] [Revised: 04/08/2024] [Accepted: 05/20/2024] [Indexed: 06/01/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a powerful optical technique for non-invasive and label-free bioanalysis of liquid biopsy, facilitating to diagnosis of potential diseases. Neuropsychiatric systemic lupus erythematosus (NPSLE) is one of the subgroups of systemic lupus erythematosus (SLE) with serious manifestations for a high mortality rate. Unfortunately, lack of well-established gold standards results in the clinical diagnosis of NPSLE being a challenge so far. Here we develop a novel Raman fingerprinting machine learning (ML-) assisted diagnostic method. The microsphere-coupled SERS (McSERS) substrates are employed to acquire Raman spectra for analysis via convolutional neural network (CNN). The McSERS substrates demonstrate better performance to distinguish the Raman spectra from serums between SLE and NPSLE, attributed to the boosted signal-to-noise ratio of Raman intensities due to the multiple optical regulation in microspheres and AuNPs. Eight statistically-significant (p-value <0.05) Raman shifts are identified, for the first time, as the characteristic spectral markers. The classification model established by CNN algorithm demonstrates 95.0% in accuracy, 95.9% in sensitivity, and 93.5% in specificity for NPSLE diagnosis. The present work paves a new way achieving clinical label-free serum diagnosis of rheumatic diseases by enhanced Raman fingerprints with machine learning.
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Affiliation(s)
- Yanlin Mi
- School of Physics and Optoelectronic Engineering, Beijing University of Technology, Beijing, 100124, China
| | - Xue Li
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, 100044, China
| | - Xingyue Zeng
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, 100044, China
| | - Yuyang Cai
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Xiaolin Sun
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, 100044, China.
| | - Yinzhou Yan
- School of Physics and Optoelectronic Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Trans-scale Laser Manufacturing Technology (Beijing University of Technology), Ministry of Education, Beijing, 100124, China; Beijing Engineering Research Center of Laser Technology, Beijing University of Technology, Beijing, 100124, China.
| | - Yijian Jiang
- School of Physics and Optoelectronic Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Trans-scale Laser Manufacturing Technology (Beijing University of Technology), Ministry of Education, Beijing, 100124, China; Beijing Engineering Research Center of Laser Technology, Beijing University of Technology, Beijing, 100124, China
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4
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Wang Y, Wang Y, Chen X, Zhu M, Xu Y, Wu Y, Gao S, Zhang M, Su L, Han W, Chi M. Label-Free Identification of AML1-ETO Positive Acute Myeloid Leukemia Using Single-Cell Raman Spectroscopy. APPLIED SPECTROSCOPY 2024:37028241254403. [PMID: 38772561 DOI: 10.1177/00037028241254403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
Acute myeloid leukemia (AML) is a malignant hematological tumor disease. Chromosomal abnormality is an independent prognostic factor in AML. AML with t(8:21) (q22; q22)/AML1-ETO (AE) is an independent disease group. In this research, a new method based on Raman spectroscopy is reported for label-free single-cell identification and analysis of AE fusion genes in clinical AML patients. Raman spectroscopy reflects the intrinsic vibration information of molecules in a label-free and non-destructive manner, and the fingerprint Raman spectrum of cells characterizes intracellular molecular types and relative concentration information, so as to realize the identification and molecular metabolism analysis of different kinds of cells. We collected the Raman spectra of bone marrow cells from clinically diagnosed AML M2 patients with and without the AE fusion gene. Through comparison of the average spectra and identification analysis based on multivariate statistical methods such as principal component analysis and linear discriminant analysis, the distinction between AE positive and negative sample cells in M2 AML patients was successfully achieved, and the single-cell identification accuracy was more than 90%. At the same time, the Raman spectra of the two types of cells were analyzed by the multivariate curve resolution alternating least squares decomposition method. It was found that the presence of the AE fusion gene may lead to the metabolic changes of lipid and nucleic acid in AML cells, which was consistent with the results of genomic and metabolomic multi-omics studies. The above results indicate that single-cell Raman spectroscopy has the potential for early identification of AE-positive AML.
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Affiliation(s)
- Yang Wang
- Changchun Sci-Tech University, Shuangyang, Jilin Province, China
| | - Yimeng Wang
- National Science Library (Chengdu), Chinese Academy of Sciences, Chengdu, China
| | - Xing Chen
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, China
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Applied Optics, Changchun, Jilin, China
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun, Jilin, China
| | - Mingyao Zhu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, China
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Applied Optics, Changchun, Jilin, China
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun, Jilin, China
| | - Yang Xu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, China
| | - Yihui Wu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, China
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Applied Optics, Changchun, Jilin, China
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun, Jilin, China
| | - Sujun Gao
- Department of Hematology, The First Bethune Hospital, Jilin University, Changchun, China
| | - Ming Zhang
- Department of Hematology, The First Bethune Hospital, Jilin University, Changchun, China
| | - Long Su
- Department of Hematology, The First Bethune Hospital, Jilin University, Changchun, China
| | - Wei Han
- Department of Hematology, The First Bethune Hospital, Jilin University, Changchun, China
| | - Mingbo Chi
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, China
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Applied Optics, Changchun, Jilin, China
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun, Jilin, China
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5
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Barkur S, Boitor RA, Mihai R, Gopal NSR, Leeney S, Koloydenko AA, Khout H, Rakha E, Notingher I. Intraoperative spectroscopic evaluation of sentinel lymph nodes in breast cancer surgery. Breast Cancer Res Treat 2024:10.1007/s10549-024-07349-z. [PMID: 38769222 DOI: 10.1007/s10549-024-07349-z] [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: 11/27/2023] [Accepted: 04/17/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND AND OBJECTIVES Sentinel lymph node (SLN) biopsy is a standard procedure for patients with breast cancer and normal axilla on imaging. Positive SLNs on histological examination can lead to a subsequent surgery for axillary lymph node clearance (ALNC). Here we report a non-destructive technique based on autofluorescence (AF) imaging and Raman spectroscopy for intra-operative assessment of SLNs excised in breast cancer surgery. METHODS A microscope integrating AF imaging and Raman spectroscopy modules was built to allow scanning of lymph node biopsy samples. During AF-Raman measurements, AF imaging determined optimal sampling locations for Raman spectroscopy measurements. After optimisation of the AF image analysis and training of classification models based on data from 85 samples, the AF-Raman technique was tested on an independent set of 81 lymph nodes comprising 58 fixed and 23 fresh specimens. The sensitivity and specificity of AF-Raman were calculated using post-operative histology as a standard of reference. RESULTS The independent test set contained 66 negative lymph nodes and 15 positive lymph nodes according to the reference standard, collected from 78 patients. For this set of specimens, the area under the receiver operating characteristic (ROC) curve for the AF-Raman technique was 0.93 [0.83-0.98]. AF-Raman was then operated in a regime that maximised detection specificity, producing a 94% detection accuracy: 80% sensitivity and 97% specificity. The main confounders for SLN metastasis were areas rich in histiocytes clusters, for which only few Raman spectra had been included in the training dataset. DISCUSSION This preliminary study indicates that with further development and extension of the training dataset by inclusion of additional Raman spectra of histiocytes clusters and capsule, the AF-Raman may become a promising technique for intra-operative assessment of SLNs. Intra-operative detection of positive biopsies could avoid second surgery for axillary clearance.
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Affiliation(s)
- Surekha Barkur
- School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Radu A Boitor
- School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Raluca Mihai
- Department of Pathology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | | | - Samuel Leeney
- School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | | | - Hazem Khout
- Breast Institute, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Emad Rakha
- Department of Pathology, Nottingham University Hospitals NHS Trust, Nottingham, UK.
| | - Ioan Notingher
- School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
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6
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Troncoso-Afonso L, Vinnacombe-Willson GA, García-Astrain C, Liz-Márzan LM. SERS in 3D cell models: a powerful tool in cancer research. Chem Soc Rev 2024; 53:5118-5148. [PMID: 38607302 PMCID: PMC11104264 DOI: 10.1039/d3cs01049j] [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: 11/29/2023] [Indexed: 04/13/2024]
Abstract
Unraveling the cellular and molecular mechanisms underlying tumoral processes is fundamental for the diagnosis and treatment of cancer. In this regard, three-dimensional (3D) cancer cell models more realistically mimic tumors compared to conventional 2D cell cultures and are more attractive for performing such studies. Nonetheless, the analysis of such architectures is challenging because most available techniques are destructive, resulting in the loss of biochemical information. On the contrary, surface-enhanced Raman spectroscopy (SERS) is a non-invasive analytical tool that can record the structural fingerprint of molecules present in complex biological environments. The implementation of SERS in 3D cancer models can be leveraged to track therapeutics, the production of cancer-related metabolites, different signaling and communication pathways, and to image the different cellular components and structural features. In this review, we highlight recent progress in the use of SERS for the evaluation of cancer diagnosis and therapy in 3D tumoral models. We outline strategies for the delivery and design of SERS tags and shed light on the possibilities this technique offers for studying different cellular processes, through either biosensing or bioimaging modalities. Finally, we address current challenges and future directions, such as overcoming the limitations of SERS and the need for the development of user-friendly and robust data analysis methods. Continued development of SERS 3D bioimaging and biosensing systems, techniques, and analytical strategies, can provide significant contributions for early disease detection, novel cancer therapies, and the realization of patient-tailored medicine.
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Affiliation(s)
- Lara Troncoso-Afonso
- BioNanoPlasmonics Laboratory, CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain.
- Department of Applied Chemistry, University of the Basque Country, 20018 Donostia-San Sebastián, Gipuzkoa, Spain
| | - Gail A Vinnacombe-Willson
- BioNanoPlasmonics Laboratory, CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain.
| | - Clara García-Astrain
- BioNanoPlasmonics Laboratory, CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain.
- Centro de Investigación Biomédica en Red de Bioingeniería Biomateriales, y Nanomedicina (CIBER-BBN), Paseo de Miramón 182, 20014 Donostia-San Sebastián, Spain
| | - Luis M Liz-Márzan
- BioNanoPlasmonics Laboratory, CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain.
- Centro de Investigación Biomédica en Red de Bioingeniería Biomateriales, y Nanomedicina (CIBER-BBN), Paseo de Miramón 182, 20014 Donostia-San Sebastián, Spain
- Ikerbasque Basque Foundation for Science, 48013 Bilbao, Spain
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7
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Vyas B, Khatiashvili A, Galati L, Ngo K, Gildener-Leapman N, Larsen M, Lednev IK. Raman hyperspectroscopy of saliva and machine learning for Sjögren's disease diagnostics. Sci Rep 2024; 14:11135. [PMID: 38750168 PMCID: PMC11096345 DOI: 10.1038/s41598-024-59850-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Sjögren's disease is an autoimmune disorder affecting exocrine glands, causing dry eyes and mouth and other morbidities. Polypharmacy or a history of radiation to the head and neck can also lead to dry mouth. Sjogren's disease is often underdiagnosed due to its non-specific symptoms, limited awareness among healthcare professionals, and the complexity of diagnostic criteria, limiting the ability to provide therapy early. Current diagnostic methods suffer from limitations including the variation in individuals, the absence of a single diagnostic marker, and the low sensitivity and specificity, high cost, complexity, and invasiveness of current procedures. Here we utilized Raman hyperspectroscopy combined with machine learning to develop a novel screening test for Sjögren's disease. The method effectively distinguished Sjögren's disease patients from healthy controls and radiation patients. This technique shows potential for development of a single non-invasive, efficient, rapid, and inexpensive medical screening test for Sjögren's disease using a Raman hyper-spectral signature.
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Affiliation(s)
- Bhavik Vyas
- Department of Chemistry, University at Albany, SUNY, Albany, NY, 12222, USA
| | - Ana Khatiashvili
- Division of Otolaryngology Head and Neck Surgery, Albany Medical College, Albany, NY, 12208, USA
| | - Lisa Galati
- Division of Otolaryngology Head and Neck Surgery, Albany Medical College, Albany, NY, 12208, USA
| | - Khoa Ngo
- Division of Otolaryngology Head and Neck Surgery, Albany Medical College, Albany, NY, 12208, USA
| | - Neil Gildener-Leapman
- Division of Otolaryngology Head and Neck Surgery, Albany Medical College, Albany, NY, 12208, USA
| | - Melinda Larsen
- Department of Biology and The RNA Institute, University at Albany, SUNY, Albany, NY, 12222, USA
| | - Igor K Lednev
- Department of Chemistry, University at Albany, SUNY, Albany, NY, 12222, USA.
- Department of Biology and The RNA Institute, University at Albany, SUNY, Albany, NY, 12222, USA.
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8
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Zhang Q, Ren T, Cao K, Xu Z. Advances of machine learning-assisted small extracellular vesicles detection strategy. Biosens Bioelectron 2024; 251:116076. [PMID: 38340580 DOI: 10.1016/j.bios.2024.116076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024]
Abstract
Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.
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Affiliation(s)
- Qi Zhang
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China
| | - Tingju Ren
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China
| | - Ke Cao
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China
| | - Zhangrun Xu
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, PR China.
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9
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Jiang M, Wu P, Zhang Y, Wang M, Zhang M, Ye Z, Zhang X, Zhang C. Artificial Intelligence-Driven Platform: Unveiling Critical Hepatic Molecular Alterations in Hepatocellular Carcinoma Development. Adv Healthc Mater 2024:e2400291. [PMID: 38657582 DOI: 10.1002/adhm.202400291] [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: 01/26/2024] [Revised: 04/19/2024] [Indexed: 04/26/2024]
Abstract
Since most Hepatocellular Carcinoma (HCC) typically arises as a consequence of long-term liver damage, the hepatic molecular characteristics are closely related to the occurrence of HCC. Gaining comprehensive information about the location, morphology, and hepatic molecular alterations related to HCC is essential for accurate diagnosis. However, there is a dearth of technological advancements capable of concurrently providing precise HCC diagnosis and discerning the accompanying hepatic molecular alterations. In this study, an integrated information system is developed for the pathological-level diagnosis of HCC and the revelation of critical molecular alterations in the liver. This system utilizes computed tomography/Surface-enhanced Raman scattering combined with an artificial intelligence strategy to establish connections between the occurrence of HCC and alterations in hepatic biomolecules. Employing artificial intelligence techniques, the SERS spectra from both healthy and HCC groups are successfully classified into two distinct categories with a remarkable accuracy rate of 91.38%. Based on molecular profiling, it is identified that the nucleotide-to-lipid signal ratio holds significant potential as a reliable indicator for the occurrence of HCC, thereby serving as a promising tool for prevention and therapeutic surveillance.
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Affiliation(s)
- Miao Jiang
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300203, China
| | - Pengyun Wu
- Department of Nuclear Medicine, Tianjin Medical University General Hospital, 154 Anshan Ave, Heping, 300052, China
| | - Yuwei Zhang
- Department of Radiology, National Clinical Research Centre of Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Mengling Wang
- Department of Radiology, National Clinical Research Centre of Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Mingjie Zhang
- Department of Radiology, National Clinical Research Centre of Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Zhaoxiang Ye
- Department of Radiology, National Clinical Research Centre of Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Xuejun Zhang
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300203, China
| | - Cai Zhang
- Department of Radiology, National Clinical Research Centre of Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
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10
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Li M, Wang R, Bao Q. Hyper-spectra imaging analysis of PLGA microspheres via machine learning enhanced Raman spectroscopy. J Control Release 2024; 367:676-686. [PMID: 38309305 DOI: 10.1016/j.jconrel.2024.01.071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/05/2024]
Abstract
Long-acting injectables (LAI) offer a cost-effective and patient-centric approach by reducing pill burden and improving compliance, leading to better treatment outcomes. Among various types of long-acting injectables, poly (lactic-co-glycolic acid) (PLGA) microspheres have been extensively investigated and reported in the literature. However, microsphere formulation development is still challenging due to the complexity of PLGA polymer, formulation screening, and processing, as well as time-consuming and cumbersome physicochemical characterization. A further challenge is the limited availability of drug substances in early formulation development. Therefore, there is a need to develop novel and advanced tools that can accelerate the early formulation development. In this manuscript, a novel comprehensive physicochemical characterization approach was developed by integrating Raman microscopy and the machine learning process. The physicochemical properties such as drug loading, particle size and size distribution, content uniformity/heterogeneity, and drug polymorphism of the microspheres can be obtained in a single run, without requiring separate methods for each attribute (e.g., liquid chromatography, particle size analyzer, thermal analysis, X-ray powder diffraction). This approach is non-destructive and can significantly reduce material consumption, sample preparation, labor work, and analysis time/cost, which will greatly facilitate the formulation development of PLGA microsphere products. In addition, the approach will potentially be beneficial in enabling automated high throughput screening of microsphere formulations.
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Affiliation(s)
- Minghe Li
- Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT 06877, USA
| | - Ruifeng Wang
- Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT 06877, USA; Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269, USA
| | - Quanying Bao
- Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT 06877, USA.
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Lopes DF, Silverio A, Schmidt AKA, Picca GB, Silveira L. Characterization of biomarkers in blood serum for cancer diagnosis in dogs using Raman spectroscopy. JOURNAL OF BIOPHOTONICS 2024; 17:e202300338. [PMID: 38100121 DOI: 10.1002/jbio.202300338] [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: 08/23/2023] [Revised: 10/25/2023] [Accepted: 12/03/2023] [Indexed: 03/26/2024]
Abstract
Biomarkers of cancer in sera of domestic dogs were detected through Raman spectroscopy with 830 nm excitation. Raman spectra of sera from 61 dogs (31 healthy and 30 with cancer, resulting in 154 and 200 spectra, respectively) were submitted to principal component analysis (PCA) for feature extraction and partial least squares (PLS) regression for discrimination between Healthy and Cancer groups. In the PCA, the peaks at 1132, 1342, 1368, and 1453 cm-1 (albumin and phenylalanine) were higher for the Cancer group. The "redshift" of the peaks at 621, 1003, and 1032 cm-1 (conformational change in proteins and/or bonds at sites close to the aromatic ring of amino acids) occurred in the Cancer group, and the peaks at 451 cm-1 (tryptophan) and 1441 cm-1 (lipids) were higher for the Healthy group. The PLS-DA classified the serum spectra in Healthy and Cancer groups with high accuracy (78%).
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Affiliation(s)
| | | | | | | | - Landulfo Silveira
- Universidade Anhembi Morumbi-UAM, São Paulo, Brazil
- Center for Innovation, Technology and Education-CITÉ, Parque Tecnológico de São José dos Campos, São José dos Campos, São Paulo, Brazil
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12
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Mayorova OA, Saveleva MS, Bratashov DN, Prikhozhdenko ES. Combination of Machine Learning and Raman Spectroscopy for Determination of the Complex of Whey Protein Isolate with Hyaluronic Acid. Polymers (Basel) 2024; 16:666. [PMID: 38475349 DOI: 10.3390/polym16050666] [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: 12/29/2023] [Revised: 02/22/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
Macromolecules and their complexes remain interesting topics in various fields, such as targeted drug delivery and tissue regeneration. The complex chemical structure of such substances can be studied with a combination of Raman spectroscopy and machine learning. The complex of whey protein isolate (WPI) and hyaluronic acid (HA) is beneficial in terms of drug delivery. It provides HA properties with the stability obtained from WPI. However, differences between WPI-HA and WPI solutions can be difficult to detect by Raman spectroscopy. Especially when the low HA (0.1, 0.25, 0.5% w/v) and the constant WPI (5% w/v) concentrations are used. Before applying the machine learning techniques, all the collected data were divided into training and test sets in a ratio of 3:1. The performances of two ensemble methods, random forest (RF) and gradient boosting (GB), were evaluated on the Raman data, depending on the type of problem (regression or classification). The impact of noise reduction using principal component analysis (PCA) on the performance of the two machine learning methods was assessed. This procedure allowed us to reduce the number of features while retaining 95% of the explained variance in the data. Another application of these machine learning methods was to identify the WPI Raman bands that changed the most with the addition of HA. Both the RF and GB could provide feature importance data that could be plotted in conjunction with the actual Raman spectra of the samples. The results show that the addition of HA to WPI led to changes mainly around 1003 cm-1 (correspond to ring breath of phenylalanine) and 1400 cm-1, as demonstrated by the regression and classification models. For selected Raman bands, where the feature importance was greater than 1%, a direct evaluation of the effect of the amount of HA on the Raman intensities was performed but was found not to be informative. Thus, applying the RF or GB estimators to the Raman data with feature importance evaluation could detect and highlight small differences in the spectra of substances that arose from changes in the chemical structure; using PCA to filter out noise in the Raman data could improve the performance of both the RF and GB. The demonstrated results will make it possible to analyze changes in chemical bonds during various processes, for example, conjugation, to study complex mixtures of substances, even with small additions of the components of interest.
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Affiliation(s)
- Oksana A Mayorova
- Science Medical Center, Saratov State University, 83 Astrakhanskaya Str., 410012 Saratov, Russia
| | - Mariia S Saveleva
- Science Medical Center, Saratov State University, 83 Astrakhanskaya Str., 410012 Saratov, Russia
| | - Daniil N Bratashov
- Science Medical Center, Saratov State University, 83 Astrakhanskaya Str., 410012 Saratov, Russia
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13
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Jiang J, Li L, Yin G, Luo H, Li J. A Molecular Typing Method for Invasive Breast Cancer by Serum Raman Spectroscopy. Clin Breast Cancer 2024:S1526-8209(24)00048-X. [PMID: 38492997 DOI: 10.1016/j.clbc.2024.02.008] [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: 11/16/2023] [Revised: 01/17/2024] [Accepted: 02/12/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND The incidence of breast cancer ranks highest among cancers and is exceedingly heterogeneous. Immunohistochemical staining is commonly used clinically to identify the molecular subtype for subsequent treatment and prognosis. PURPOSE Raman spectroscopy and support vector machine (SVM) learning algorithm were utilized to identify blood samples from breast cancer patients in order to investigate a novel molecular typing approach. METHOD Tumor tissue coarse needle aspiration biopsy samples, and peripheral venous blood samples were gathered from 459 invasive breast cancer patients admitted to the breast department of Sichuan Cancer Hospital between June 2021 and September 2022. Immunohistochemical staining and in situ hybridization were performed on the coarse needle aspiration biopsy tissues to obtain their molecular typing pathological labels, including: 70 cases of Luminal A, 167 cases of Luminal B (HER2-positive), 57 cases of Luminal B (HER2-negative), 84 cases of HER2-positive, and 81 cases of triple-negative. Blood samples were processed to obtained Raman spectra taken for SVM classification models establishment with machine algorithms (using 80% of the sample data as the training set), and then the performance of the SVM classification models was evaluated by the independent validation set (20% of the sample data). RESULTS The AUC values of SVM classification models remained above 0.85, demonstrating outstanding model performance and excellent subtype discrimination of breast cancer molecular subtypes. CONCLUSION Raman spectroscopy of serum samples can promptly and precisely detect the molecular subtype of invasive breast cancer, which has the potential for clinical value.
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Affiliation(s)
- Jun Jiang
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; Department of Breast Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Lintao Li
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Gang Yin
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Huaichao Luo
- Department of Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Junjie Li
- Department of Breast Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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14
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Zhang K, Liu R, Wei X, Wang Z, Huang P. Use of Raman spectroscopy to study rat lung tissues for distinguishing asphyxia from sudden cardiac death. RSC Adv 2024; 14:5665-5674. [PMID: 38357034 PMCID: PMC10865087 DOI: 10.1039/d3ra07684a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/24/2024] [Indexed: 02/16/2024] Open
Abstract
Determining asphyxia as the cause of death is crucial but is based on an exclusive strategy because it lacks sensitive and specific morphological characteristics in forensic practice. In some cases where the deceased has underlying heart disease, differentiation between asphyxia and sudden cardiac death (SCD) as the primary cause of death can be challenging. Herein, Raman spectroscopy was employed to detect pulmonary biochemical differences to discriminate asphyxia from SCD in rat models. Thirty-two rats were used to build asphyxia and SCD models, with lung samples collected immediately or 24 h after death. Twenty Raman spectra were collected for each lung sample, and 640 spectra were obtained for further data preprocessing and analysis. The results showed that different biochemical alterations existed in the lung tissues of the rats that died from asphyxia and SCD and could be used to distinguish between the two causes of death. Moreover, we screened and used 8 of the 11 main differential spectral features that maintained their significant differences at 24 h after death to successfully determine the cause of death, even with decomposition and autolysis. Eventually, seven prevalent machine learning classification algorithms were employed to establish classification models, among which the support vector machine exhibited the best performance, with an area under the curve value of 0.9851 in external validation. This study shows the promise of Raman spectroscopy combined with machine learning algorithms to investigate differential biochemical alterations originating from different deaths to aid determining the cause of death in forensic practice.
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Affiliation(s)
- Kai Zhang
- Shanghai Key Lab of Forensic Medicine, Key Lab of Forensic Science, Ministry of Justice, China, Academy of Forensic Science Shanghai People's Republic of China
- Department of Forensic Pathology, College of Forensic Medicine, NHC Key Laboratory of Forensic Science, Xi'an Jiaotong University Xi'an People's Republic of China
| | - Ruina Liu
- Center for Translational Medicine, The First Affiliated Hospital of Xi'an Jiaotong University Xi'an People's Republic of China
| | - Xin Wei
- Department of Forensic Pathology, College of Forensic Medicine, NHC Key Laboratory of Forensic Science, Xi'an Jiaotong University Xi'an People's Republic of China
| | - Zhenyuan Wang
- Department of Forensic Pathology, College of Forensic Medicine, NHC Key Laboratory of Forensic Science, Xi'an Jiaotong University Xi'an People's Republic of China
| | - Ping Huang
- Shanghai Key Lab of Forensic Medicine, Key Lab of Forensic Science, Ministry of Justice, China, Academy of Forensic Science Shanghai People's Republic of China
- Institute of Forensic Science, Fudan University Shanghai People's Republic of China
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15
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Espinosa-Garavito AC, Quiroz EN, Galán-Freyle NJ, Aroca-Martinez G, Hernández-Rivera SP, Villa-Medina J, Méndez-López M, Gomez-Escorcia L, Acosta-Hoyos A, Pacheco-Lugo L, Espitia-Almeida F, Pacheco-Londoño LC. Surface-enhanced Raman Spectroscopy in urinalysis of hypertension patients with kidney disease. Sci Rep 2024; 14:3035. [PMID: 38321263 PMCID: PMC10847430 DOI: 10.1038/s41598-024-53679-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 02/03/2024] [Indexed: 02/08/2024] Open
Abstract
Arterial hypertension (AH) is a multifactorial and asymptomatic disease that affects vital organs such as the kidneys and heart. Considering its prevalence and the associated severe health repercussions, hypertension has become a disease of great relevance for public health across the globe. Conventionally, the classification of an individual as hypertensive or non-hypertensive is conducted through ambulatory blood pressure monitoring over a 24-h period. Although this method provides a reliable diagnosis, it has notable limitations, such as additional costs, intolerance experienced by some patients, and interferences derived from physical activities. Moreover, some patients with significant renal impairment may not present proteinuria. Accordingly, alternative methodologies are applied for the classification of individuals as hypertensive or non-hypertensive, such as the detection of metabolites in urine samples through liquid chromatography or mass spectrometry. However, the high cost of these techniques limits their applicability for clinical use. Consequently, an alternative methodology was developed for the detection of molecular patterns in urine collected from hypertension patients. This study generated a direct discrimination model for hypertensive and non-hypertensive individuals through the amplification of Raman signals in urine samples based on gold nanoparticles and supported by chemometric techniques such as partial least squares-discriminant analysis (PLS-DA). Specifically, 162 patient urine samples were used to create a PLS-DA model. These samples included 87 urine samples from patients diagnosed with hypertension and 75 samples from non-hypertensive volunteers. In the AH group, 35 patients were diagnosed with kidney damage and were further classified into a subgroup termed (RAH). The PLS-DA model with 4 latent variables (LV) was used to classify the hypertensive patients with external validation prediction (P) sensitivity of 86.4%, P specificity of 77.8%, and P accuracy of 82.5%. This study demonstrates the ability of surface-enhanced Raman spectroscopy to differentiate between hypertensive and non-hypertensive patients through urine samples, representing a significant advance in the detection and management of AH. Additionally, the same model was then used to discriminate only patients diagnosed with renal damage and controls with a P sensitivity of 100%, P specificity of 77.8%, and P accuracy of 82.5%.
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Affiliation(s)
- Alberto C Espinosa-Garavito
- Centro de Investigaciones en Ciencias de la Vida, Facultad de Ciencias Básicas y Biomédicas, Universidad Simón Bolívar, 080002, Barranquilla, Atlántico, Colombia
| | - Elkin Navarro Quiroz
- Centro de Investigaciones en Ciencias de la Vida, Facultad de Ciencias Básicas y Biomédicas, Universidad Simón Bolívar, 080002, Barranquilla, Atlántico, Colombia
| | - Nataly J Galán-Freyle
- Centro de Investigaciones en Ciencias de la Vida, Facultad de Ciencias Básicas y Biomédicas, Universidad Simón Bolívar, 080002, Barranquilla, Atlántico, Colombia
| | | | - Samuel P Hernández-Rivera
- Center for Chemical Sensors, DHS SENTRY COE, University of Puerto Rico-Mayaguez, Mayaguez, PR, 00681, USA
| | - Joe Villa-Medina
- Center of Pharmaceutical Research, Procaps Laboratories, 080002, Barranquilla, Colombia
| | - Maximiliano Méndez-López
- Grupo de Química y Biología, Departamento de Química y Biología, Universidad del Norte, Km 5 Vía Puerto Colombia, 080001, Barranquilla, Colombia
| | | | - Antonio Acosta-Hoyos
- Centro de Investigaciones en Ciencias de la Vida, Facultad de Ciencias Básicas y Biomédicas, Universidad Simón Bolívar, 080002, Barranquilla, Atlántico, Colombia
| | - Lisandro Pacheco-Lugo
- Centro de Investigaciones en Ciencias de la Vida, Facultad de Ciencias Básicas y Biomédicas, Universidad Simón Bolívar, 080002, Barranquilla, Atlántico, Colombia
| | - Fabián Espitia-Almeida
- Centro de Investigaciones en Ciencias de la Vida, Facultad de Ciencias Básicas y Biomédicas, Universidad Simón Bolívar, 080002, Barranquilla, Atlántico, Colombia
| | - Leonardo C Pacheco-Londoño
- Centro de Investigaciones en Ciencias de la Vida, Facultad de Ciencias Básicas y Biomédicas, Universidad Simón Bolívar, 080002, Barranquilla, Atlántico, Colombia.
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16
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Chen C, Qi J, Li Y, Li D, Wu L, Li R, Chen Q, Sun N. Applications of Raman spectroscopy in the diagnosis and monitoring of neurodegenerative diseases. Front Neurosci 2024; 18:1301107. [PMID: 38370434 PMCID: PMC10869569 DOI: 10.3389/fnins.2024.1301107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 01/17/2024] [Indexed: 02/20/2024] Open
Abstract
Raman scattering is an inelastic light scattering that occurs in a manner reflective of the molecular vibrations of molecular structures and chemical conditions in a given sample of interest. Energy changes in the scattered light can be assessed to determine the vibration mode and associated molecular and chemical conditions within the sample, providing a molecular fingerprint suitable for sample identification and characterization. Raman spectroscopy represents a particularly promising approach to the molecular analysis of many diseases owing to clinical advantages including its instantaneous nature and associated high degree of stability, as well as its ability to yield signal outputs corresponding to a single molecule type without any interference from other molecules as a result of its narrow peak width. This technology is thus ideally suited to the simultaneous assessment of multiple analytes. Neurodegenerative diseases represent an increasingly significant threat to global public health owing to progressive population aging, imposing a severe physical and social burden on affected patients who tend to develop cognitive and/or motor deficits beginning between the ages of 50 and 70. Owing to a relatively limited understanding of the etiological basis for these diseases, treatments are lacking for the most common neurodegenerative diseases, which include Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. The present review was formulated with the goal of briefly explaining the principle of Raman spectroscopy and discussing its potential applications in the diagnosis and evaluation of neurodegenerative diseases, with a particular emphasis on the research prospects of this novel technological platform.
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Affiliation(s)
- Chao Chen
- Central Laboratory, Liaocheng People’s Hospital and Liaocheng School of Clinical Medicine, Shandong First Medical University, Liaocheng, China
| | - Jinfeng Qi
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Ying Li
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Ding Li
- Department of Clinical Laboratory, Liaocheng People’s Hospital and Liaocheng School of Clinical Medicine, Shandong First Medical University, Liaocheng, China
| | - Lihong Wu
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Ruihua Li
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Qingfa Chen
- Institute of Tissue Engineering and Regenerative Medicine, Liaocheng People’s Hospital and Liaocheng School of Clinical Medicine, Shandong First Medical University, Liaocheng, China
- Research Center of Basic Medicine, Jinan Central Hospital, Jinan, China
| | - Ning Sun
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
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17
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Simeral ML, Demers SME, Sheth K, Hafner JH. A Raman spectral marker for the iso-octyl chain structure of cholesterol. ANALYTICAL SCIENCE ADVANCES 2024; 5:2300057. [PMID: 38828085 PMCID: PMC11142391 DOI: 10.1002/ansa.202300057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 11/17/2023] [Indexed: 06/05/2024]
Abstract
Raman spectroscopy provides label-free, specific analysis of biomolecular structure and interactions. It could have a greater impact with improved characterization of complex fingerprint vibrations. Many Raman peaks have been assigned to cholesterol, for example, but the molecular vibrations associated with those peaks are not known. In this report, time-dependent density functional theory calculations of the Raman spectrum of cholesterol are compared to measurements on microcrystalline powder to identify 23 peaks in the Raman spectrum. Among them, a band of six peaks is found to be sensitive to the conformational structure of cholesterol's iso-octyl chain. Calculations on 10 conformers in this spectral band are fit to experimental spectra to probe the cholesterol chain structure in purified powder and in phospholipid vesicles. In vesicles, the chain is found to bend perpendicular to the steroid rings, supporting the case that the chain is a dynamic structure that contributes to lipid condensation and other effects of cholesterol in biomembranes. Statement of Significance: Here we use density functional theory to identify a band of six peaks in cholesterol's Raman spectrum that is sensitive to the conformational structure of cholesterol's chain. Raman spectra were analyzed to show that in fluid-phase lipid membranes, about half of the cholesterol chains point perpendicular to the steroid rings. This new method of label-free structural analysis could make significant contributions to our understanding of cholesterol's critical role in biomembrane structure and function. More broadly, the results show that computational quantum chemistry Raman spectroscopy can make significant new contributions to molecular structure when spectra are interpreted with computational quantum chemistry.
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Affiliation(s)
| | | | - Kyle Sheth
- Department of Physics and AstronomyRice UniversityHoustonTexasUSA
| | - Jason H. Hafner
- Department of Physics and AstronomyRice UniversityHoustonTexasUSA
- Department of ChemistryRice UniversityHoustonTexasUSA
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18
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Jayaprakash V, You JB, Kanike C, Liu J, McCallum C, Zhang X. Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 38272008 DOI: 10.1021/acs.est.3c06447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has been well explored as a highly effective characterization technique that is capable of chemical pollutant detection and identification at very low concentrations. Machine learning has been previously used to identify compounds based on SERS spectral data. However, utilization of SERS to quantify concentrations, with or without machine learning, has been difficult due to the spectral intensity being sensitive to confounding factors such as the substrate parameters, orientation of the analyte, and sample preparation technique. Here, we demonstrate an approach for predicting the concentration of sample pollutants from SERS spectra using machine learning. Frequency domain transform methods, including the Fourier and Walsh-Hadamard transforms, are applied to spectral data sets of three analytes (rhodamine 6G, chlorpyrifos, and triclosan), which are then used to train machine learning algorithms. Using standard machine learning models, the concentration of the sample pollutants is predicted with >80% cross-validation accuracy from raw SERS data. A cross-validation accuracy of 85% was achieved using deep learning for a moderately sized data set (∼100 spectra), and 70-80% was achieved for small data sets (∼50 spectra). Performance can be maintained within this range even when combining various sample preparation techniques and environmental media interference. Additionally, as a spectral pretreatment, the Fourier and Hadamard transforms are shown to consistently improve prediction accuracy across multiple data sets. Finally, standard models were shown to accurately identify characteristic peaks of compounds via analysis of their importance scores, further verifying their predictive value.
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Affiliation(s)
- Vishnu Jayaprakash
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
| | - Jae Bem You
- Department of Chemical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Chiranjeevi Kanike
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
| | - Jinfeng Liu
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
| | | | - Xuehua Zhang
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
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19
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Zhao Y, Kumar A, Yang Y. Unveiling practical considerations for reliable and standardized SERS measurements: lessons from a comprehensive review of oblique angle deposition-fabricated silver nanorod array substrates. Chem Soc Rev 2024; 53:1004-1057. [PMID: 38116610 DOI: 10.1039/d3cs00540b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Recently, there has been an exponential growth in the number of publications focusing on surface-enhanced Raman scattering (SERS), primarily driven by advancements in nanotechnology and the increasing demand for chemical and biological detection. While many of these publications have focused on the development of new substrates and detection-based applications, there is a noticeable lack of attention given to various practical issues related to SERS measurements and detection. This review aims to fill this gap by utilizing silver nanorod (AgNR) SERS substrates fabricated through the oblique angle deposition method as an illustrative example. The review highlights and addresses a range of practical issues associated with SERS measurements and detection. These include the optimization of SERS substrates in terms of morphology and structural design, considerations for measurement configurations such as polarization and the incident angle of the excitation laser, and exploration of enhancement mechanisms encompassing both intrinsic properties induced by the structure and materials, as well as extrinsic factors arising from wetting/dewetting phenomena and analyte size. The manufacturing and storage aspects of SERS substrates, including scalable fabrication techniques, contamination control, cleaning procedures, and appropriate storage methods, are also discussed. Furthermore, the review delves into device design considerations, such as well arrays, flow cells, and fiber probes, and explores various sample preparation methods such as drop-cast and immersion. Measurement issues, including the effect of excitation laser wavelength and power, as well as the influence of buffer, are thoroughly examined. Additionally, the review discusses spectral analysis techniques, encompassing baseline removal, chemometric analysis, and machine learning approaches. The wide range of AgNR-based applications of SERS, across various fields, is also explored. Throughout the comprehensive review, key lessons learned from collective findings are outlined and analyzed, particularly in the context of detailed SERS measurements and standardization. The review also provides insights into future challenges and perspectives in the field of SERS. It is our hope that this comprehensive review will serve as a valuable reference for researchers seeking to embark on in-depth studies and applications involving their own SERS substrates.
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Affiliation(s)
- Yiping Zhao
- Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA.
| | - Amit Kumar
- Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA.
| | - Yanjun Yang
- School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA.
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20
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Zhang S, Wu SQY, Hum M, Perumal J, Tan EY, Lee ASG, Teng J, Dinish US, Olivo M. Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis. RSC Adv 2024; 14:3599-3610. [PMID: 38264270 PMCID: PMC10804230 DOI: 10.1039/d3ra05723b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/17/2023] [Indexed: 01/25/2024] Open
Abstract
Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.
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Affiliation(s)
- Shuyan Zhang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - Steve Qing Yang Wu
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - Melissa Hum
- Division of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre Singapore (NCCS) 30 Hospital Boulevard Singapore 168583 Republic of Singapore
| | - Jayakumar Perumal
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - Ern Yu Tan
- Breast & Endocrine Surgery, Tan Tock Seng Hospital (TTSH) 11 Jln Tan Tock Seng Singapore 308433 Republic of Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Republic of Singapore
| | - Ann Siew Gek Lee
- Division of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre Singapore (NCCS) 30 Hospital Boulevard Singapore 168583 Republic of Singapore
- SingHealth Duke-NUS Oncology Academic Clinical Programme (ONCO ACP), Duke-NUS Medical School Singapore 169857 Republic of Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore Singapore 117593 Republic of Singapore
| | - Jinghua Teng
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - U S Dinish
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
| | - Malini Olivo
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR) 2 Fusionopolis Way, Innovis #08-03 Singapore 138634 Republic of Singapore
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21
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Chang M, He C, Du Y, Qiu Y, Wang L, Chen H. RaT: Raman Transformer for highly accurate melanoma detection with critical features visualization. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123475. [PMID: 37806238 DOI: 10.1016/j.saa.2023.123475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/27/2023] [Accepted: 09/29/2023] [Indexed: 10/10/2023]
Abstract
Melanoma is an important cause of death from skin cancer. Early and accurate diagnosis can effectively reduce mortality. But the current diagnosis relies on the experience of pathologists, increasing the rate of misdiagnosis. In this paper, Raman Transformer (RaT) model is proposed by combining Raman spectroscopy and a Transformer encoder to distinguish the Raman spectra of melanoma and normal tissue. To make the spectral data more suitable for the Transformer encoder, we split the Raman spectrum into segments and map them into block vectors, which are then input into the Transformer encoder and classified using the multi-head self-attention mechanism and the Multilayer Perceptron (MLP). The RaT model achieves 99.69% accuracy, 99.61% sensitivity, and 99.82% specificity, which is higher than the classical principal component analysis with the neural network (PCA + NNET) method. In addition, we visualize and explain the fingerprint peaks found by the RaT model and their corresponding biological information. Our proposed RaT model provides a novel and reliable method for processing Raman spectral data, which is expected to help distinguish melanoma from normal cells, diagnose other diseases, and save human lives.
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Affiliation(s)
- Min Chang
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Chen He
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yi Du
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Yemin Qiu
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Luyao Wang
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Hui Chen
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China
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22
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Szymborski TR, Berus SM, Nowicka AB, Słowiński G, Kamińska A. Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy. Biomedicines 2024; 12:167. [PMID: 38255271 PMCID: PMC10813688 DOI: 10.3390/biomedicines12010167] [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: 11/23/2023] [Revised: 12/23/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024] Open
Abstract
The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.
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Affiliation(s)
- Tomasz R. Szymborski
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland;
| | - Sylwia M. Berus
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland;
| | - Ariadna B. Nowicka
- Institute for Materials Research and Quantum Engineering, Poznan University of Technology, Piotrowo 3, 60-965 Poznan, Poland;
| | - Grzegorz Słowiński
- Department of Software Engineering, Warsaw School of Computer Science, Lewartowskiego 17, 00-169 Warsaw, Poland;
| | - Agnieszka Kamińska
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland;
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23
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Dhillon AK, Sharma A, Yadav V, Singh R, Ahuja T, Barman S, Siddhanta S. Raman spectroscopy and its plasmon-enhanced counterparts: A toolbox to probe protein dynamics and aggregation. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2024; 16:e1917. [PMID: 37518952 DOI: 10.1002/wnan.1917] [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: 06/29/2022] [Revised: 06/22/2023] [Accepted: 07/06/2023] [Indexed: 08/01/2023]
Abstract
Protein unfolding and aggregation are often correlated with numerous diseases such as Alzheimer's, Parkinson's, Huntington's, and other debilitating neurological disorders. Such adverse events consist of a plethora of competing mechanisms, particularly interactions that control the stability and cooperativity of the process. However, it remains challenging to probe the molecular mechanism of protein dynamics such as aggregation, and monitor them in real-time under physiological conditions. Recently, Raman spectroscopy and its plasmon-enhanced counterparts, such as surface-enhanced Raman spectroscopy (SERS) and tip-enhanced Raman spectroscopy (TERS), have emerged as sensitive analytical tools that have the potential to perform molecular studies of functional groups and are showing significant promise in probing events related to protein aggregation. We summarize the fundamental working principles of Raman, SERS, and TERS as nondestructive, easy-to-perform, and fast tools for probing protein dynamics and aggregation. Finally, we highlight the utility of these techniques for the analysis of vibrational spectra of aggregation of proteins from various sources such as tissues, pathogens, food, biopharmaceuticals, and lastly, biological fouling to retrieve precise chemical information, which can be potentially translated to practical applications and point-of-care (PoC) devices. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > Diagnostic Nanodevices Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.
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Affiliation(s)
| | - Arti Sharma
- Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, India
| | - Vikas Yadav
- Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, India
| | - Ruchi Singh
- Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, India
| | - Tripti Ahuja
- Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, India
| | - Sanmitra Barman
- Center for Advanced Materials and Devices (CAMD), BML Munjal University, Haryana, India
| | - Soumik Siddhanta
- Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, India
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24
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Akdeniz M, Al-Shaebi Z, Altunbek M, Bayraktar C, Kayabolen A, Bagci-Onder T, Aydin O. Characterization and discrimination of spike protein in SARS-CoV-2 virus-like particles via surface-enhanced Raman spectroscopy. Biotechnol J 2024; 19:e2300191. [PMID: 37750467 DOI: 10.1002/biot.202300191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 09/11/2023] [Accepted: 09/22/2023] [Indexed: 09/27/2023]
Abstract
Non-infectious virus-like particles (VLPs) are excellent structures for development of many biomedical applications such as drug delivery systems, vaccine production platforms, and detection techniques for infectious diseases including SARS-CoV-2 VLPs. The characterization of biochemical and biophysical properties of purified VLPs is crucial for development of detection methods and therapeutics. The presence of spike (S) protein in their structure is especially important since S protein induces immunological response. In this study, development of a rapid, low-cost, and easy-to-use technique for both characterization and detection of S protein in the two VLPs, which are SARS-CoV-2 VLPs and HIV-based VLPs was achieved using surface-enhanced Raman spectroscopy (SERS). To analyze and classify datasets of SERS spectra obtained from the VLP groups, machine learning classification techniques including support vector machine (SVM), k-nearest neighbors (kNN), and random forest (RF) were utilized. Among them, the SVM classification algorithm demonstrated the best classification performance for SARS-CoV-2 VLPs and HIV-based VLPs groups with 87.5% and 92.5% accuracy, respectively. This study could be valuable for the rapid characterization of VLPs for the development of novel therapeutics or detection of structural proteins of viruses leading to a variety of infectious diseases.
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Affiliation(s)
- Munevver Akdeniz
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
- Nanothera Lab, Drug Application and Research Center (ERFARMA), Erciyes University, Kayseri, Turkey
| | - Zakarya Al-Shaebi
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
- Nanothera Lab, Drug Application and Research Center (ERFARMA), Erciyes University, Kayseri, Turkey
| | - Mine Altunbek
- Department of Chemical Engineering, University of Massachusetts, Lowell, Massachusetts, USA
| | - Canan Bayraktar
- Koç University Research Center for Translational Medicine (KUTTAM), Koç University, Istanbul, Turkey
| | - Alisan Kayabolen
- Koç University Research Center for Translational Medicine (KUTTAM), Koç University, Istanbul, Turkey
- McGovern Institute for Brain Research at MIT, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Tugba Bagci-Onder
- Koç University Research Center for Translational Medicine (KUTTAM), Koç University, Istanbul, Turkey
| | - Omer Aydin
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
- Nanothera Lab, Drug Application and Research Center (ERFARMA), Erciyes University, Kayseri, Turkey
- Clinical Engineering Research and Implementation Center (ERKAM), Erciyes University, Kayseri, Turkey
- Nanotechnology Research and Application Center (ERNAM), Erciyes University, Kayseri, Turkey
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25
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Sarma D, Medhi A, Mohanta D, Nath P. Electrochemically deposited bimetallic SERS substrate for trace sensing of antibiotics. Mikrochim Acta 2023; 191:14. [PMID: 38087069 DOI: 10.1007/s00604-023-06075-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 10/26/2023] [Indexed: 12/18/2023]
Abstract
Electrochemically deposited bimetallic copper-gold nanoparticles on indium tin oxide (Cu-AuNPs on ITO) glass are demonstrated to be a sensitive and reproducible surface-enhanced Raman scattering (SERS) platform. An optimal signal enhancement with reasonably good degree of homogeneity was obtained by tuning the deposition parameters of the electrochemical setup. For Raman active analytes such as malachite green (MG) and rhodamine 6G (R6G), the developed SERS platform yields a limit of detection (LOD) of 0.75 nM. The usability of the proposed SERS platform has been realized through detection of two important antibiotics namely sulfamethoxazole (SFZ) and tetracycline hydrochloride (TCH) commonly used in egg farms. Furthermore, a machine learning (ML)-based model coupled with a dimensionality reduction technique-principal component analysis (PCA)-has been implemented to classify the targeted analytes in egg samples.
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Affiliation(s)
- Dipjyoti Sarma
- Applied Photonics and Nanophotonics Laboratory, Department of Physics, Tezpur University, Napaam, Tezpur, Assam, 784028, India
| | - Ankush Medhi
- Nanoscience and Soft-Matter Laboratory, Department of Physics, Tezpur University, Napaam, Tezpur, Assam, 784028, India
| | - Dambarudhar Mohanta
- Nanoscience and Soft-Matter Laboratory, Department of Physics, Tezpur University, Napaam, Tezpur, Assam, 784028, India
| | - Pabitra Nath
- Applied Photonics and Nanophotonics Laboratory, Department of Physics, Tezpur University, Napaam, Tezpur, Assam, 784028, India.
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26
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Zhou QQ, Guo J, Wang Z, Li J, Chen M, Xu Q, Zhu L, Xu Q, Wang Q, Pan H, Pan J, Zhu Y, Song M, Liu X, Wang J, Zhang Z, Zhang L, Wang Y, Cai H, Chen X, Lu G. Rapid visualization of PD-L1 expression level in glioblastoma immune microenvironment via machine learning cascade-based Raman histopathology. J Adv Res 2023:S2090-1232(23)00377-6. [PMID: 38072311 DOI: 10.1016/j.jare.2023.12.002] [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: 08/04/2023] [Revised: 11/29/2023] [Accepted: 12/01/2023] [Indexed: 02/13/2024] Open
Abstract
INTRODUCTION Combination immunotherapy holds promise for improving survival in responsive glioblastoma (GBM) patients. Programmed death-ligand 1 (PD-L1) expression in immune microenvironment (IME) is the most important predictive biomarker for immunotherapy. Due to the heterogeneous distribution of PD-L1, post-operative histopathology fails to accurately capture its expression in residual tumors, making intra-operative diagnosis crucial for GBM treatment strategies. However, the current methods for evaluating the expression of PD-L1 are still time-consuming. OBJECTIVE To overcome the PD-L1 heterogeneity and enable rapid, accurate, and label-free imaging of PD-L1 expression level in GBM IME at the tissue level. METHODS We proposed a novel intra-operative diagnostic method, Machine Learning Cascade (MLC)-based Raman histopathology, which uses a coordinate localization system (CLS), hierarchical clustering analysis (HCA), support vector machine (SVM), and similarity analysis (SA). This method enables visualization of PD-L1 expression in glioma cells, CD8+ T cells, macrophages, and normal cells in addition to the tumor/normal boundary. The study quantified PD-L1 expression levels using the tumor proportion, combined positive, and cellular composition scores (TPS, CPS, and CCS, respectively) based on Raman data. Furthermore, the association between Raman spectral features and biomolecules was examined biochemically. RESULTS The entire process from signal collection to visualization could be completed within 30 min. In an orthotopic glioma mouse model, the MLC-based Raman histopathology demonstrated a high average accuracy (0.990) for identifying different cells and exhibited strong concordance with multiplex immunofluorescence (84.31 %) and traditional pathologists' scoring (R2 ≥ 0.9). Moreover, the peak intensities at 837 and 874 cm-1 showed a positive linear correlation with PD-L1 expression level. CONCLUSIONS This study introduced a new and extendable diagnostic method to achieve rapid and accurate visualization of PD-L1 expression in GBM IMB at the tissular level, leading to great potential in GBM intraoperative diagnosis for guiding surgery and post-operative immunotherapy.
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Affiliation(s)
- Qing-Qing Zhou
- Department of Radiology, Jinling Hospital, Affiliated Nanjing Medical University, Nanjing, China; Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, China
| | - Jingxing Guo
- School of Chemistry, Chemical Engineering and Life Sciences, Wuhan University of Technology, Wuhan, China.
| | - Ziyang Wang
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China; Nanjing Nuoyuan Medical Devices Co. Ltd, Nanjing, China
| | - Jianrui Li
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Meng Chen
- Nanjing Nuoyuan Medical Devices Co. Ltd, Nanjing, China
| | - Qiang Xu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Lijun Zhu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Qing Xu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Qiang Wang
- Department of Neurosurgery, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Hao Pan
- Department of Neurosurgery, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Jing Pan
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yong Zhu
- School of Science, China Pharmaceutical University, Nanjing, China
| | - Ming Song
- Department of Mathmatical Sciences, The University of Texas at Dallas, Richardson, USA
| | - Xiaoxue Liu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jiandong Wang
- Department of Pathology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhiqiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yiqing Wang
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China
| | - Huiming Cai
- Department of Biomedical Engineering, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China; Nanjing Nuoyuan Medical Devices Co. Ltd, Nanjing, China
| | - Xiaoyuan Chen
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore; Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Guangming Lu
- Department of Radiology, Jinling Hospital, Affiliated Nanjing Medical University, Nanjing, China; Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, China.
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27
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Yan C, Luo S, Cao L, Cheng Z, Zhang H. Tensor product based 2-D correlation data preprocessing methods for Raman spectroscopy of Chinese handmade paper. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123033. [PMID: 37356393 DOI: 10.1016/j.saa.2023.123033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 06/27/2023]
Abstract
The paper introduces two new methods, namely the cross correlation method (CCM) and two-dimensional correlation method (TDCM), for preprocessing Raman spectroscopy data for analyzing Chinese handmade paper samples. CCM expands the spectral dimension from 1×N to 1×2N-1 by taking cross-correlation between two spectral data of the same category. TDCM includes two-dimensional synchronous correlation method (TDSCM) and two-dimensional asynchronous correlation method (TDACM), which expand the spectral dimension from 1×N to N×N by taking tensor products between two spectral data and between one spectral data and the Hilbert transformation of the other spectral data of the same category, respectively. The experimental data were preprocessed using baseline removal, CCM, TDSCM, and TDACM methods. Four machine learning models were employed to evaluate the effects of these methods: principal component analysis (PCA) combined with linear regression (LR), support vector machine (SVM) combined with LR, k-Nearest Neighbors (KNN), and random forest (RF). The results show that the R-squared values for the PCA model were nearly 1 for all types of data, indicating high accuracy. However, for SVM-LR, KNN, and RF models, the R-squared values were sorted in the order of raw data, baseline removal data, CCM, TDSCM, and TDACM preprocessed data. The R-squared values of KNN and RF machine learning models for TDACM preprocessed data were approaching 1, indicating that the accuracy of machine learning was significantly improved by nearly 100%. This has led to a remarkable improvement in the accuracy of supervised models such as KNN and RF, bringing them closer to the level of unsupervised models such as PCA.
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Affiliation(s)
- Chunsheng Yan
- Zhejiang University Library, Hangzhou 310058, China; State Key Laboratory of Modern Optical Instrumentation, Hangzhou 310058, China.
| | - Si Luo
- Hangzhou Institute of Advanced Studies, Zhejiang Normal University, Hangzhou 311231, China
| | - Linquan Cao
- School of Art and Archaeology, Zhejiang University, Hangzhou, China; Laboratory for Art and Archaeology Image of Ministry of Education, Zhejiang University, Hangzhou, China
| | - Zhongyi Cheng
- Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Hangzhou 310058, China
| | - Hui Zhang
- School of Art and Archaeology, Zhejiang University, Hangzhou, China; Laboratory for Art and Archaeology Image of Ministry of Education, Zhejiang University, Hangzhou, China.
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28
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Alix JJP, Plesia M, Shaw PJ, Mead RJ, Day JCC. Combining electromyography and Raman spectroscopy: optical EMG. Muscle Nerve 2023; 68:464-470. [PMID: 37477391 PMCID: PMC10952815 DOI: 10.1002/mus.27937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 06/21/2023] [Accepted: 06/25/2023] [Indexed: 07/22/2023]
Abstract
INTRODUCTION/AIMS Electromyography (EMG) remains a key component of the diagnostic work-up for suspected neuromuscular disease, but it does not provide insight into the molecular composition of muscle which can provide diagnostic information. Raman spectroscopy is an emerging neuromuscular biomarker capable of generating highly specific, molecular fingerprints of tissue. Here, we present "optical EMG," a combination of EMG and Raman spectroscopy, achieved using a single needle. METHODS An optical EMG needle was created to collect electrophysiological and Raman spectroscopic data during a single insertion. We tested functionality with in vivo recordings in the SOD1G93A mouse model of amyotrophic lateral sclerosis (ALS), using both transgenic (n = 10) and non-transgenic (NTg, n = 7) mice. Under anesthesia, compound muscle action potentials (CMAPs), spontaneous EMG activity and Raman spectra were recorded from both gastrocnemius muscles with the optical EMG needle. Standard concentric EMG needle recordings were also undertaken. Electrophysiological data were analyzed with standard univariate statistics, Raman data with both univariate and multivariate analyses. RESULTS A significant difference in CMAP amplitude was observed between SOD1G93A and NTg mice with optical EMG and standard concentric needles (p = .015 and p = .011, respectively). Spontaneous EMG activity (positive sharp waves) was detected in transgenic SOD1G93A mice only. Raman spectra demonstrated peaks associated with key muscle components. Significant differences in molecular composition between SOD1G93A and NTg muscle were identified through the Raman spectra. DISCUSSION Optical EMG can provide standard electrophysiological data and molecular Raman data during a single needle insertion and represents a potential biomarker for neuromuscular disease.
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Affiliation(s)
- James J. P. Alix
- Sheffield Institute for Translational NeuroscienceUniversity of SheffieldSheffieldUK
- Cross‐Faculty Neuroscience InstituteUniversity of SheffieldSheffieldUK
| | - Maria Plesia
- Sheffield Institute for Translational NeuroscienceUniversity of SheffieldSheffieldUK
| | - Pamela J. Shaw
- Sheffield Institute for Translational NeuroscienceUniversity of SheffieldSheffieldUK
- Cross‐Faculty Neuroscience InstituteUniversity of SheffieldSheffieldUK
| | - Richard J. Mead
- Sheffield Institute for Translational NeuroscienceUniversity of SheffieldSheffieldUK
- Cross‐Faculty Neuroscience InstituteUniversity of SheffieldSheffieldUK
| | - John C. C. Day
- Interface Analysis Centre, School of PhysicsUniversity of BristolBristolUK
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29
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Dai T, Xiao Z, Shan D, Moreno A, Li H, Prakash M, Banaei N, Rao J. Culture-Independent Multiplexed Detection of Drug-Resistant Bacteria Using Surface-Enhanced Raman Scattering. ACS Sens 2023; 8:3264-3271. [PMID: 37506677 DOI: 10.1021/acssensors.3c01345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
Abstract
The rapid and accurate detection of bacteria resistance to β-lactam antibiotics is critical to inform optimal treatment and prevent overprescription of potent antibiotics. Here, we present a fast, culture-independent method for the detection of extended-spectrum β-lactamases (ESBLs) using surface-enhanced Raman scattering (SERS). The method uses Raman probes that release sulfur-based Raman active molecules in the presence of β-lactamases. The released thiol molecules can be captured by gold nanoparticles, leading to amplified Raman signals. A broad-spectrum cephalosporin probe R1G and an ESBL-specific probe R3G are designed to enable duplex detection of bacteria expressing broad-spectrum β-lactamases or ESBLs with a detection limit of 103 cfu/mL in 1 h incubation. Combined with a portable Raman microscope, our culturing-free SERS assay has reduced screening time to 1.5 h without compromising sensitivity and specificity.
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Affiliation(s)
- Tingting Dai
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
| | - Zhen Xiao
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, California 94305, United States
| | - Dingying Shan
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, California 94305, United States
| | - Angel Moreno
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, United States
| | - Hongquan Li
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, United States
| | - Manu Prakash
- Department of Bioengineering, Stanford University, Stanford, California 94305, United States
| | - Niaz Banaei
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, United States
- Clinical Microbiology Laboratory, Stanford University Medical Center, Palo Alto, California 94304, United States
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, United States
| | - Jianghong Rao
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, California 94305, United States
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30
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Sharaha U, Hania D, Lapidot I, Salman A, Huleihel M. Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning. Cells 2023; 12:1909. [PMID: 37508572 PMCID: PMC10378363 DOI: 10.3390/cells12141909] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/06/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society. Currently used methods for the diagnosis of cancer are time-consuming, producing discomfort and anxiety for patients and significant medical waste. The main goal of this study is to evaluate the potential of Raman spectroscopy-based machine learning for the identification and characterization of precancerous and cancerous cells. As a representative model, normal mouse primary fibroblast cells (NFC) as healthy cells; a mouse fibroblast cell line (NIH/3T3), as precancerous cells; and fully malignant mouse fibroblasts (MBM-T) as cancerous cells were used. Raman spectra were measured from three different sites of each of the 457 investigated cells and analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA). Our results showed that it was possible to distinguish between the normal and abnormal (precancerous and cancerous) cells with a success rate of 93.1%; this value was 93.7% when distinguishing between normal and precancerous cells and 80.2% between precancerous and cancerous cells. Moreover, there was no influence of the measurement site on the differentiation between the different examined biological systems.
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Affiliation(s)
- Uraib Sharaha
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
- Department of Biology, Science and Technology College, Hebron University, Hebron P760, Palestine
| | - Daniel Hania
- Department of Green Engineering, SCE-Shamoon College of Engineering, Beer-Sheva 84100, Israel
| | - Itshak Lapidot
- Department of Electrical and Electronics Engineering, ACLP-Afeka Center for Language Processing, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv 69107, Israel
- Laboratoire Informatique d'Avignon (LIA), Avignon Université, 339 Chemin des Meinajaries, 84000 Avignon, France
| | - Ahmad Salman
- Department of Physics, SCE-Shamoon College of Engineering, Beer-Sheva 84100, Israel
| | - Mahmoud Huleihel
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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31
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Ralbovsky NM, Smith JP. Machine Learning for Prediction, Classification, and Identification of Immobilized Enzymes for Biocatalysis. Pharm Res 2023; 40:1479-1490. [PMID: 36653518 DOI: 10.1007/s11095-022-03457-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 12/01/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND Enzyme immobilization is a beneficial component involved in biocatalytic strategies. Understanding and evaluating the enzyme immobilization system plays an important role in the successful development and implementation of the biocatalysis route. Ensuring the implementation of a successful enzyme immobilization process is vital for realizing a highly functioning and well suited biocatalytic process within pharmaceutical development. AIM To develop a method which can accurately and objectively identify and classify differences within enzyme immobilization systems, sample preparation methods, and data collection parameters. METHODS Raman hyperspectral imaging was used to obtain a total of eight spectral data sets from enzyme immobilization samples. Partial least squares discriminant analysis (PLS-DA) was used to classify and identify the samples based on their differences. RESULTS Several two-class, four-class, and eight-class PLS-DA models were built to classify the different sample data sets. All models reached between 92-100% accuracy after cross-validation and external validation, illustrating great success of the models for identifying differences between the samples. CONCLUSION Raman hyperspectral imaging with machine learning can be used to investigate, interpret, and classify different data collection parameters, sample preparation methods, and enzyme immobilization supports, providing crucial insight into enzyme immobilization process development.
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Affiliation(s)
- Nicole M Ralbovsky
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA, 19486, USA.
| | - Joseph P Smith
- Analytical Research & Development, MRL, Merck & Co., Inc., West Point, PA, 19486, USA.
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32
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Chen Q, Shi T, Du D, Wang B, Zhao S, Gao Y, Wang S, Zhang Z. Non-destructive diagnostic testing of cardiac myxoma by serum confocal Raman microspectroscopy combined with multivariate analysis. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:2578-2587. [PMID: 37114381 DOI: 10.1039/d3ay00180f] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The symptoms of cardiac myxoma (CM) mainly occur when the tumor is growing, and the diagnosis is determined by clinical presentation. Unfortunately, there is no evidence that specific blood tests are useful in CM diagnosis. Raman spectroscopy (RS) has emerged as a promising auxiliary diagnostic tool because of its ability to simultaneously detect multiple molecular features without labelling. The objective of this study was to identify spectral markers for CM, one of the most common benign cardiac tumors with insidious onset and rapid progression. In this study, a preliminary analysis was conducted based on serum Raman spectra to obtain the spectral differences between CM patients (CM group) and healthy control subjects (normal group). Principal component analysis-linear discriminant analysis (PCA-LDA) was constructed to highlight the differences in the distribution of biochemical components among the groups according to the obtained spectral information. Principal component analysis was combined with a support vector machine model (PCA-SVM) based on three different kernel functions (linear, polynomial, and Gaussian radial basis function (RBF)) to resolve spectral variations between all study groups. The results showed that CM patients had lower serum levels of phenylalanine and carotenoid than those in the normal group, and increased levels of fatty acids. The resulting Raman data was used in a multivariate analysis to determine the Raman range that could be used for CM diagnosis. Also, the chemical interpretation of the spectral results obtained is further presented in the discussion section based on the multivariate curve resolution-alternating least squares (MCR-ALS) method. These results suggest that RS can be used as an adjunct and promising tool for CM diagnosis, and that vibrations in the fingerprint region can be used as spectral markers for the disease under study.
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Affiliation(s)
- Qiang Chen
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Tao Shi
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Dan Du
- Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | - Bo Wang
- Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | - Sha Zhao
- Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | - Yang Gao
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Shuang Wang
- Institute of Photonics and Photon-Technology, Northwest University, Xi'an, China
| | - Zhanqin Zhang
- Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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33
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Luo H, Zu R, Li L, Deng Y, He S, Yin X, Zhang K, He Q, Yin Y, Yin G, Yao D, Wang D. Serum laser Raman spectroscopy as a potential diagnostic tool to discriminate the benignancy or malignancy of pulmonary nodules. iScience 2023; 26:106693. [PMID: 37197326 PMCID: PMC10183669 DOI: 10.1016/j.isci.2023.106693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/23/2023] [Accepted: 04/13/2023] [Indexed: 05/19/2023] Open
Abstract
It has been proved that Raman spectral intensities could be used to diagnose lung cancer patients. However, the application of Raman spectroscopy in identifying the patients with pulmonary nodules was barely studied. In this study, we revealed that Raman spectra of serum samples from healthy participants and patients with benign and malignant pulmonary nodules were significantly different. A support vector machine (SVM) model was developed for the classification of Raman spectra with wave points, according to ANOVA test results. It got a good performance with a median area under the curve (AUC) of 0.89, when the SVM model was applied in discriminating benign from malignant individuals. Compared with three common clinical models, the SVM model showed a better discriminative ability and added more net benefits to participants, which were also excellent in the small-size nodules. Thus, the Raman spectroscopy could be a less-invasive and low-costly liquid biopsy.
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Affiliation(s)
- Huaichao Luo
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Corresponding author
| | - Ruiling Zu
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Lintao Li
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yao Deng
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Shuya He
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Xing Yin
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Kaijiong Zhang
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Qiao He
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yu Yin
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Gang Yin
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Dongsheng Wang
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Corresponding author
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Karnachoriti M, Stathopoulos I, Kouri M, Spyratou E, Orfanoudakis S, Lykidis D, Lambropoulou Μ, Danias N, Arkadopoulos N, Efstathopoulos EP, Raptis YS, Seimenis I, Kontos AG. Biochemical differentiation between cancerous and normal human colorectal tissues by micro-Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 299:122852. [PMID: 37216817 DOI: 10.1016/j.saa.2023.122852] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/29/2023] [Accepted: 05/08/2023] [Indexed: 05/24/2023]
Abstract
Human colorectal tissues obtained by ten cancer patients have been examined by multiple micro-Raman spectroscopic measurements in the 500-3200 cm-1 range under 785 nm excitation. Distinct spectral profiles are recorded from different spots on the samples: a predominant 'typical' profile of colorectal tissue, as well as those from tissue topologies with high lipid, blood or collagen content. Principal component analysis identified several Raman bands of amino acids, proteins and lipids which allow the efficient discrimination of normal from cancer tissues, the first presenting plurality of Raman spectral profiles while the last showing off quite uniform spectroscopic characteristics. Tree-based machine learning experiment was further applied on all data as well as on filtered data keeping only those spectra which characterize the largely inseparable data clusters of 'typical' and 'collagen-rich' spectra. This purposive sampling evidences statistically the most significant spectroscopic features regarding the correct identification of cancer tissues and allows matching spectroscopic results with the biochemical changes induced in the malignant tissues.
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Affiliation(s)
- M Karnachoriti
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece; Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - I Stathopoulos
- 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - M Kouri
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece; Medical Physics Program, University of Massachusetts Lowell, MA 01854, United States
| | - E Spyratou
- Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - S Orfanoudakis
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece; Alpha Information Technology S.A., Software & System Development, 68131 Alexandroupolis, Greece
| | - D Lykidis
- Laboratory of Histology-Embryology, Medical Department, Democritus University of Thrace, Alexandroupolis, Greece
| | - Μ Lambropoulou
- Laboratory of Histology-Embryology, Medical Department, Democritus University of Thrace, Alexandroupolis, Greece
| | - N Danias
- 4(th) Department of Surgery, School of Medicine, Attikon University Hospital, Univ. of Athens, 12462 Athens, Greece
| | - N Arkadopoulos
- 4(th) Department of Surgery, School of Medicine, Attikon University Hospital, Univ. of Athens, 12462 Athens, Greece
| | - E P Efstathopoulos
- 2(nd) Department of Radiology, Medical School, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Y S Raptis
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece
| | - I Seimenis
- Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - A G Kontos
- School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Zografou, Athens, Greece.
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35
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Dawuti W, Dou J, Zheng X, Lü X, Zhao H, Yang L, Lin R, Lü G. Rapid and accurate screening of cystic echinococcosis in sheep based on serum Fourier-transform infrared spectroscopy combined with machine learning algorithms. JOURNAL OF BIOPHOTONICS 2023; 16:e202200320. [PMID: 36707914 DOI: 10.1002/jbio.202200320] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 01/20/2023] [Accepted: 01/25/2023] [Indexed: 05/17/2023]
Abstract
Cystic echinococcosis (CE) in sheep is a serious zoonotic parasitic disease caused by Echinococcus granulosus sensu stricto (s.s.). Presently, the screening technology for CE in sheep is time-consuming and inaccurate, and novel screening technology is urgently needed. In this work, we combined machine-learning algorithms with Fourier transform infrared (FT-IR) spectroscopy of serum to establish a quick and accurate screening approach for CE in sheep. Serum samples from 77 E. granulosus s.s.-infected sheep to 121 healthy control sheep were measured by FT-IR spectrometer. To optimize the classification accuracy of the serum FI-TR method for the E. granulosus s.s.-infected sheep and healthy control sheep, principal component analysis (PCA), linear discriminant analysis, and support vector machine (SVM) algorithms were used to analyze the data. Among all the bands, 1500-1700 cm-1 band has the best classification effect; its diagnostic sensitivity, specificity, and accuracy of PCA-SVM were 100%, 95.74%, and 96.66%, respectively. The study showed that serum FT-IR spectroscopy combined with machine learning algorithms has great potential for rapid and accurate screening methods for the CE in sheep.
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Affiliation(s)
- Wubulitalifu Dawuti
- School of Public Health, Xinjiang Medical University, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Jingrui Dou
- School of Public Health, Xinjiang Medical University, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiangxiang Zheng
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaoyi Lü
- College of Software, Xinjiang University, Urumqi, China
| | - Hui Zhao
- Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Lingfei Yang
- Department of Abdominal Ultrasound Diagnosis, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Renyong Lin
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Guodong Lü
- School of Public Health, Xinjiang Medical University, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
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36
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Jain S, Naicker D, Raj R, Patel V, Hu YC, Srinivasan K, Jen CP. Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials. Diagnostics (Basel) 2023; 13:diagnostics13091563. [PMID: 37174954 PMCID: PMC10178016 DOI: 10.3390/diagnostics13091563] [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: 03/04/2023] [Revised: 04/16/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body's interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed.
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Affiliation(s)
- Somit Jain
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Dharmik Naicker
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Ritu Raj
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Vedanshu Patel
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Yuh-Chung Hu
- Department of Mechanical and Electromechanical Engineering, National ILan University, Yilan 26047, Taiwan
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Chun-Ping Jen
- School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Department of Mechanical Engineering and Advanced Institute of Manufacturing for High-Tech Innovations, National Chung Cheng University, Chia-Yi 62102, Taiwan
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37
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Alkhuder K. Raman Scattering-Based Optical Sensing Of Chronic Liver Diseases. Photodiagnosis Photodyn Ther 2023; 42:103505. [PMID: 36965755 DOI: 10.1016/j.pdpdt.2023.103505] [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: 11/17/2022] [Revised: 02/26/2023] [Accepted: 03/07/2023] [Indexed: 03/27/2023]
Abstract
Chronic liver diseases (CLDs) are a major public health problem. Despite the progress achieved in fighting against viral hepatitis, the emergence of non-alcoholic fatty liver disease might pose a serious challenge to the public's health in the coming decades. Medical management of CLDs represents a substantial burden on the public health infrastructures. The health care cost of these diseases is an additional burden that weighs heavily on the economies of developing countries. Effective management of CLDs requires the adoption of reliable and cost-effective screening and diagnosing methods to ensure early detection and accurate clinical assessment of these diseases. Vibrational spectroscopies have emerged as universal analytical methods with promising applications in various industrial and biomedical fields. These revolutionary analytical techniques rely on analyzing the interaction between a light beam and the test sample to generate a spectral fingerprint. This latter is defined by the analyte's chemical structure and the molecular vibrations of its functional groups. Raman spectroscopy and surface-enhanced Raman spectroscopy have been used in combination with various chemometric tests to diagnose a wide range of malignant, metabolic and infectious diseases. The aim of the current review is to cast light on the use of these optical sensing methods in the diagnosis of CLDs. The vast majority of research works that investigated the potential application of these spectroscopic techniques in screening and detecting CLDs were discussed here. The advantages and limitations of these modern analytical methods, as compared with the routine and gold standard diagnostic approaches, were also reviewed in details.
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Chen R, Liu F, Zhang C, Wang W, Yang R, Zhao Y, Peng J, Kong W, Huang J. Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy. FRONTIERS IN PLANT SCIENCE 2023; 14:1128300. [PMID: 37025139 PMCID: PMC10072231 DOI: 10.3389/fpls.2023.1128300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.
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Affiliation(s)
- Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Wei Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Rui Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yiying Zhao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Jiyu Peng
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Wenwen Kong
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, China
| | - Jing Huang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
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39
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Bikku T, Fritz RA, Colón YJ, Herrera F. Machine Learning Identification of Organic Compounds Using Visible Light. J Phys Chem A 2023; 127:2407-2414. [PMID: 36876889 DOI: 10.1021/acs.jpca.2c07955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols and applications.
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Affiliation(s)
- Thulasi Bikku
- Department of Physics, Universidad de Santiago de Chile, Av. Victor Jara 3493, Santiago, Chile.,Computer Science and Engineering, Vignan's Nirula Institute of Technology and Science for Women, Guntur, Andhra Pradesh 522009, India
| | - Rubén A Fritz
- Department of Physics, Universidad de Santiago de Chile, Av. Victor Jara 3493, Santiago, Chile
| | - Yamil J Colón
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Felipe Herrera
- Department of Physics, Universidad de Santiago de Chile, Av. Victor Jara 3493, Santiago, Chile.,Millennium Institute for Research in Optics, Esteban Iturra s/n 4070386, Concepción , Chile
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40
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Yang H, Li X, Zhang S, Li Y, Zhu Z, Shen J, Dai N, Zhou F. A one-dimensional convolutional neural network based deep learning for high accuracy classification of transformation stages in esophageal squamous cell carcinoma tissue using micro-FTIR. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 289:122210. [PMID: 36508904 DOI: 10.1016/j.saa.2022.122210] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 11/08/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
Among the most frequently diagnosed cancers in developing countries, esophageal squamous cell carcinoma (ESCC) ranks among the top six causes of death. It would be beneficial if a rapid, accurate, and automatic ESCC diagnostic method could be developed to reduce the workload of pathologists and improve the effectiveness of cancer treatments. Using micro-FTIR spectroscopy, this study classified the transformation stages of ESCC tissues. Based on 6,352 raw micro-FTIR spectra, a one-dimensional convolutional neural network (1D-CNN) model was constructed to classify-five stages. Based on the established model, more than 93% accuracy was achieved at each stage, and the accuracy of identifying proliferation, low grade neoplasia, and ESCC cancer groups was achieved 99% for the test dataset. In this proof-of-concept study, the developed method can be applied to other diseases in order to promote the use of FTIR spectroscopy in cancer pathology.
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Affiliation(s)
- Haijun Yang
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China
| | - Xianchang Li
- Huzhou College, Huzhou 313000, Zhejiang Province, China; Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China.
| | - Shiding Zhang
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China
| | - Yuan Li
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China
| | - Zunwei Zhu
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China
| | - Jingwei Shen
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China
| | - Ningtao Dai
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China
| | - Fuyou Zhou
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China.
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41
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Dawuti W, Dou J, Li J, Zhang R, Zhou J, Maimaitiaili M, Zhou R, Lin R, Lü G. Label-free surface-enhanced Raman spectroscopy of serum with machine-learning algorithms for gallbladder cancer diagnosis. Photodiagnosis Photodyn Ther 2023; 42:103544. [PMID: 37004836 DOI: 10.1016/j.pdpdt.2023.103544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/24/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023]
Abstract
Gallbladder cancer (GBC) is a rare but frequently fatal biliary tract malignancy that is typically only discovered when it is already advanced. In the search of an efficient diagnosis method. Therefore, in this study, we investigated a novel technique for the quick and non-invasive diagnosis of GBC based on serum surface-enhanced Raman spectroscopy (SERS). SERS spectra of serum from 41 patients with GBC and 72 normal subjects were recorded. Principal component analysis-linear discriminant analysis (PCA-LDA), and PCA-support vector machine (PCA-SVM), Linear SVM and Gaussian radial basis function-SVM (RBF-SVM) algorithms were used to establish the classification models, respectively. When the Linear SVM was used, the overall diagnostic accuracy for classifying the two groups could achieve 97.1%, and when RBF-SVM was used, the diagnostic sensitivity of GBC was 100%. The results demonstrated that SERS in combination with a machine learning algorithm is a promising candidate to be one of the diagnostic tools for GBC in the future.
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Affiliation(s)
- Wubulitalifu Dawuti
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; School of Public Health, Xinjiang Medical University, Urumqi 830054, China
| | - Jingrui Dou
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; School of Public Health, Xinjiang Medical University, Urumqi 830054, China
| | - Jintian Li
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; School of Public Health, Xinjiang Medical University, Urumqi 830054, China
| | - Rui Zhang
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; School of Public Health, Xinjiang Medical University, Urumqi 830054, China
| | - Jing Zhou
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; College of Pharmacy, Xinjiang Medical University, Urumqi 830054, China
| | - Maierhaba Maimaitiaili
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; College of Pharmacy, Xinjiang Medical University, Urumqi 830054, China
| | - Run Zhou
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China; College of Pharmacy, Xinjiang Medical University, Urumqi 830054, China
| | - Renyong Lin
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
| | - Guodong Lü
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
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42
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He S, Ding L, Yuan H, Zhao G, Yang X, Wu Y. A review of sensors for classification and subtype discrimination of cancer: Insights into circulating tumor cells and tumor-derived extracellular vesicles. Anal Chim Acta 2023; 1244:340703. [PMID: 36737145 DOI: 10.1016/j.aca.2022.340703] [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: 07/23/2022] [Revised: 12/02/2022] [Accepted: 12/03/2022] [Indexed: 12/14/2022]
Abstract
Liquid biopsy can reflect the state of tumors in vivo non-invasively, thus providing a strong basis for the early diagnosis, individualized treatment monitoring and prognosis of tumors. Circulating tumor cells (CTCs) and tumor-derived extracellular vesicles (tdEVs) contain information-rich components, such as nucleic acids and proteins, and they are essential markers for liquid biopsies. Their capture and analysis are of great importance for the study of disease occurrence and development and, consequently, have been the subject of many reviews. However, both CTCs and tdEVs carry the biological characteristics of their original tissue, and few reviews have focused on their function in the staging and classification of cancer. In this review, we focus on state-of-the-art sensors based on the simultaneous detection of multiple biomarkers within CTCs and tdEVs, with clinical applications centered on cancer classification and subtyping. We also provide a thorough discussion of the current challenges and prospects for novel sensors with the ultimate goal of cancer classification and staging. It is hoped that these most advanced technologies will bring new insights into the clinical practice of cancer screening and diagnosis.
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Affiliation(s)
- Sitian He
- College of Public Health, Zhengzhou University, Zhengzhou, 450001, China
| | - Lihua Ding
- College of Public Health, Zhengzhou University, Zhengzhou, 450001, China
| | - Huijie Yuan
- College of Public Health, Zhengzhou University, Zhengzhou, 450001, China
| | - Gaofeng Zhao
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, China.
| | - Xiaonan Yang
- School of Information Engineering, Zhengzhou University, Zhengzhou, 450001, China.
| | - Yongjun Wu
- College of Public Health, Zhengzhou University, Zhengzhou, 450001, China.
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Beeram R, Vepa KR, Soma VR. Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques. BIOSENSORS 2023; 13:328. [PMID: 36979540 PMCID: PMC10046859 DOI: 10.3390/bios13030328] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/20/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.
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Zhang S, Chen S, Zhu R. Electroporation-Assisted Surface-Enhanced Raman Detection for Long-Term, Label-Free, and Noninvasive Molecular Profiling of Live Single Cells. ACS Sens 2023; 8:555-564. [PMID: 36399395 DOI: 10.1021/acssensors.2c01582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Molecule characterization of live single cells is greatly important in disease diagnoses and personalized treatments. Conventional molecule detection methods, such as mass spectrography, gene sequencing, or immunofluorescence, are usually destructive or labeled and unable to monitor the dynamic change of live cellular molecules. Herein, we propose an electroporation-assisted surface-enhanced Raman scattering (EP-SERS) method using a microchip to implement label-free, noninvasive, and continuous detections of the molecules of live single cells. The microchip containing microelectrodes with nanostructured EP-SERS probes has a multifunction of cell positioning, electroporation, and SERS detection. The EP-SERS method capably detects both the intracellular and extracellular molecules of live single cells without losing cell viability so as to enable long-term monitoring of the molecular pathological process in situ. We detect the molecules of single cells for two breast cancer cell lines with different malignancies (MCF-7 and MDA-MB-231), one liver cancer cell line (Huh-7), and one normal cell line (293T) using the EP-SERS method and classify these cell types to achieve high accuracies of 91.4-98.3% using their SERS spectra. Furthermore, 24 h continuous monitoring of the heterogeneous molecular responses of different cancer cell lines under doxorubicin treatment is successfully implemented using the EP-SERS method. This work provides a long-term, label-free, and biocompatible approach to simultaneously detect intracellular and extracellular molecules of live single cells on a chip, which would facilitate research and applications of cancer diagnoses and personalized treatments.
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Affiliation(s)
- Shengsen Zhang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| | - Shengjie Chen
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing100084, China
| | - Rong Zhu
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing100084, China
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Wei Q, Dong Q, Pu H. Multiplex Surface-Enhanced Raman Scattering: An Emerging Tool for Multicomponent Detection of Food Contaminants. BIOSENSORS 2023; 13:296. [PMID: 36832062 PMCID: PMC9954132 DOI: 10.3390/bios13020296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/31/2022] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
For survival and quality of human life, the search for better ways to ensure food safety is constant. However, food contaminants still threaten human health throughout the food chain. In particular, food systems are often polluted with multiple contaminants simultaneously, which can cause synergistic effects and greatly increase food toxicity. Therefore, the establishment of multiple food contaminant detection methods is significant in food safety control. The surface-enhanced Raman scattering (SERS) technique has emerged as a potent candidate for the detection of multicomponents simultaneously. The current review focuses on the SERS-based strategies in multicomponent detection, including the combination of chromatography methods, chemometrics, and microfluidic engineering with the SERS technique. Furthermore, recent applications of SERS in the detection of multiple foodborne bacteria, pesticides, veterinary drugs, food adulterants, mycotoxins and polycyclic aromatic hydrocarbons are summarized. Finally, challenges and future prospects for the SERS-based detection of multiple food contaminants are discussed to provide research orientation for further.
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Affiliation(s)
- Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Qirong Dong
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China
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Li C, Liu S, Zhang Q, Wan D, Shen R, Wang Z, Li Y, Hu B. Combining Raman spectroscopy and machine learning to assist early diagnosis of gastric cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 287:122049. [PMID: 36368293 DOI: 10.1016/j.saa.2022.122049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/20/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Gastric cancers, with gastric adenocarcinoma (GAC) as the most common histological type, cause quite a few of deaths. In order to improve the survival rate after GAC treatment, it is important to develop a method for early detection and therapy support of GAC. Raman spectroscopy is a potential tool for probing cancer cell due to its real-time and non-destructive measurements without any additional reagents. In this study, we use Raman spectroscopy to examine GAC samples, and distinguish cancerous gastric mucosa from normal gastric mucosa. Average Raman spectra of two groups show differences at 750 cm-1, 1004 cm-1, 1449 cm-1, 1089-1128 cm-1, 1311-1367 cm-1 and 1585-1665 cm-1, These peaks were assigned to cytochrome c, phenylalanine, phospholipid, collagen, lipid, and unsaturated fatty acid respectively. Furthermore, we build a SENet-LSTM model to realize the automatic classification of cancerous gastric mucosa and normal gastric mucosa, with all preprocessed Raman spectra in the range of 400-1800 cm-1 as input. An accuracy 96.20% was achieved. Besides, by using masking method, we found the Raman spectral features which determine the classification and explore the explainability of the classification model. The results are consistent with the conclusions obtained from the average spectrum. All results indicate it is potential for pre-cancerous screening to combine Raman spectroscopy and machine learning.
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Affiliation(s)
- Chenming Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Shasha Liu
- The first hospital of Lanzhou University, Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Qian Zhang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Dongdong Wan
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Rong Shen
- School of basic medical sciences, Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Zhong Wang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China.
| | - Yuee Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China.
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China.
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47
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Cao L, Zheng X, Han P, Ren L, Hu F, Li Z. Raman spectroscopy as a promising diagnostic method for rheumatoid arthritis. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:709-718. [PMID: 36598183 DOI: 10.1039/d2ay01904c] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Background: Diagnosis of rheumatoid arthritis (RA) basically relies on clinical symptoms and autoantibodies, especially anti-citrullinated protein antibodies (ACPAs) and rheumatoid factor (RF). However, the lack of autoantibodies is still a dilemma clinically in seronegative RA, especially in the early stage of the disease. This study aimed to provide a unique disease fingerprint with high diagnostic value to discriminate RA based on Raman spectroscopy. Methods: Raman spectroscopy provides a repertoire of biomolecules in serum from RA. Multivariate dimension-reducing methods and machine-learning algorithms were exploited to reveal the intrinsic differences and the potential discrimination power. The underlying differential biomolecules were retrieved by the assignment of Raman peaks. Moreover, the correlations between the spectral differences and RA patient's clinical and immunological manifestations were also analyzed. Results: RA patients exhibited unique Raman spectra characterized by biomolecular alterations during the disease progression. The discrimination power yielded 97.3% sensitivity and 94.8% specificity for RA diagnosis. In the recognition of ACPA-negative RA, the sensitivity and specificity also reached 95.6% and 92.8%, respectively. In particular, the differential Raman spectrum peaks of RA patients mainly represented lipids, amino acids, glycogen, and fatty acids. Further analysis showed that the different serum Raman spectra correlated with the clinical features of RA, including disease duration, RF, anticyclic citrullinated peptide antibodies (anti-CCPs), IgA, IgM, IgG, tender joint count, and swollen joint count (|rs| = 0.15-0.52, p < 0.05). Conclusions: Raman spectroscopy was revealed to be a promising diagnostic method for RA, especially for ACPA-negative patients.
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Affiliation(s)
- Lulu Cao
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, China.
| | - Xi Zheng
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, China.
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Peng Han
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, China.
| | - Limin Ren
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, China.
| | - Fanlei Hu
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, China.
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, China
- Department of Integration of Chinese and Western Medicine, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Zhanguo Li
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, China.
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, China
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48
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Puleio A, Rossi R, Gaudio P. Calibration of spectra in presence of non-stationary background using unsupervised physics-informed deep learning. Sci Rep 2023; 13:2156. [PMID: 36750596 PMCID: PMC9905576 DOI: 10.1038/s41598-023-29371-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/03/2023] [Indexed: 02/09/2023] Open
Abstract
Calibration is a key part of the development of a diagnostic. Standard approaches require the setting up of dedicated experiments under controlled conditions in order to find the calibration function that allows one to evaluate the desired information from the raw measurements. Sometimes, such controlled experiments are not possible to perform, and alternative approaches are required. Most of them aim at extracting information by looking at the theoretical expectations, requiring a lot of dedicated work and usually involving that the outputs are extremely dependent on some external factors, such as the scientist experience. This work presents a possible methodology to calibrate data or, more generally, to extract the information from the raw measurements by using a new unsupervised physics-informed deep learning methodology. The algorithm allows to automatically process the data and evaluate the searched information without the need for a supervised training by looking at the theoretical expectations. The method is examined in synthetic cases with increasing difficulties to test its potentialities, and it has been found that such an approach can also be used in very complex behaviours, where human-drive results may have huge uncertainties. Moreover, also an experimental test has been performed to validate its capabilities, but also highlight the limits of this method, which, of course, requires particular attention and a good knowledge of the analysed phenomena. The results are extremely interesting, and this methodology is believed to be applied to several cases where classic calibration and supervised approaches are not accessible.
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Affiliation(s)
- Alessandro Puleio
- grid.6530.00000 0001 2300 0941Department of Industrial Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, 00133 Rome, Italy
| | - Riccardo Rossi
- Department of Industrial Engineering, University of Rome "Tor Vergata", Via del Politecnico 1, 00133, Rome, Italy.
| | - Pasqualino Gaudio
- grid.6530.00000 0001 2300 0941Department of Industrial Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, 00133 Rome, Italy
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Sheehy G, Picot F, Dallaire F, Ember K, Nguyen T, Petrecca K, Trudel D, Leblond F. Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:025002. [PMID: 36825245 PMCID: PMC9941747 DOI: 10.1117/1.jbo.28.2.025002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/30/2023] [Indexed: 05/25/2023]
Abstract
SIGNIFICANCE Standardized data processing approaches are required in the field of bio-Raman spectroscopy to ensure information associated with spectral data acquired by different research groups, and with different systems, can be compared on an equal footing. AIM An open-sourced data processing software package was developed, implementing algorithms associated with all steps required to isolate the inelastic scattering component from signals acquired using Raman spectroscopy devices. The package includes a novel morphological baseline removal technique (BubbleFill) that provides increased adaptability to complex baseline shapes compared to current gold standard techniques. Also incorporated in the package is a versatile tool simulating spectroscopic data with varying levels of Raman signal-to-background ratios, baselines with different morphologies, and varying levels of stochastic noise. RESULTS Application of the BubbleFill technique to simulated data demonstrated superior baseline removal performance compared to standard algorithms, including iModPoly and MorphBR. The data processing workflow of the open-sourced package was validated in four independent in-human datasets, demonstrating it leads to inter-systems data compatibility. CONCLUSIONS A new open-sourced spectroscopic data pre-processing package was validated on simulated and real-world in-human data and is now available to researchers and clinicians for the development of new clinical applications using Raman spectroscopy.
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Affiliation(s)
- Guillaume Sheehy
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Fabien Picot
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Katherine Ember
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Tien Nguyen
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
| | - Kevin Petrecca
- McGill University, Montreal Neurological Institute-Hospital, Division of Neuropathology, Department of Pathology, Montreal, Quebec, Canada
| | - Dominique Trudel
- Institut du cancer de Montréal, Montreal, Quebec, Canada
- Université de Montréal, Department of Pathology and Cellular Biology, Montreal, Quebec, Canada
- Center Hospitalier de l’Université de Montréal, Department of Pathology, Montreal, Quebec, Canada
| | - Frédéric Leblond
- Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada
- Centre de recherche du Centre hospitalier de l’Université de Montréal, Montreal, Quebec, Canada
- Institut du cancer de Montréal, Montreal, Quebec, Canada
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50
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Zhao B, Zhai H, Shao H, Bi K, Zhu L. Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107295. [PMID: 36706562 PMCID: PMC9711896 DOI: 10.1016/j.cmpb.2022.107295] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/10/2022] [Accepted: 11/29/2022] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. METHODS Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. RESULTS For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. CONCLUSIONS Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.
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Affiliation(s)
- Bingqiang Zhao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Honglin Zhai
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China.
| | - Haiping Shao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Kexin Bi
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Ling Zhu
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
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