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Lin X, Zhu J, Shen J, Zhang Y, Zhu J. Advances in exosome plasmonic sensing: Device integration strategies and AI-aided diagnosis. Biosens Bioelectron 2024; 266:116718. [PMID: 39216205 DOI: 10.1016/j.bios.2024.116718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 08/11/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024]
Abstract
Exosomes, as next-generation biomarkers, has great potential in tracking cancer progression. They face many detection limitations in cancer diagnosis. Plasmonic biosensors have attracted considerable attention at the forefront of exosome detection, due to their label-free, real-time, and high-sensitivity features. Their advantages in multiplex immunoassays of minimal liquid samples establish the leading position in various diagnostic studies. This review delineates the application principles of plasmonic sensing technologies, highlighting the importance of exosomes-based spectrum and image signals in disease diagnostics. It also introduces advancements in miniaturizing plasmonic biosensing platforms of exosomes, which can facilitate point-of-care testing for future healthcare. Nowadays, inspired by the surge of artificial intelligence (AI) for science and technology, more and more AI algorithms are being adopted to process the exosome spectrum and image data from plasmonic detection. Using representative algorithms of machine learning has become a mainstream trend in plasmonic biosensing research for exosome liquid biopsy. Typically, these algorithms process complex exosome datasets efficiently and establish powerful predictive models for precise diagnosis. This review further discusses critical strategies of AI algorithm selection in exosome-based diagnosis. Particularly, we categorize the AI algorithms into the interpretable and uninterpretable groups for exosome plasmonic detection applications. The interpretable AI enhances the transparency and reliability of diagnosis by elucidating the decision-making process, while the uninterpretable AI provides high diagnostic accuracy with robust data processing by a "black-box" working mode. We believe that AI will continue to promote significant progress of exosome plasmonic detection and mobile healthcare in the near future.
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Affiliation(s)
- Xiangyujie Lin
- Institute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China; Shenzhen Research Institute of Xiamen University, Shenzhen, 518057, China
| | - Jiaheng Zhu
- Institute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China; Shenzhen Research Institute of Xiamen University, Shenzhen, 518057, China
| | - Jiaqing Shen
- Institute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China
| | - Youyu Zhang
- Institute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China; Shenzhen Research Institute of Xiamen University, Shenzhen, 518057, China.
| | - Jinfeng Zhu
- Institute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen University, Xiamen, 361005, China; Shenzhen Research Institute of Xiamen University, Shenzhen, 518057, China.
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2
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Lee S, Dang H, Moon JI, Kim K, Joung Y, Park S, Yu Q, Chen J, Lu M, Chen L, Joo SW, Choo J. SERS-based microdevices for use as in vitro diagnostic biosensors. Chem Soc Rev 2024; 53:5394-5427. [PMID: 38597213 DOI: 10.1039/d3cs01055d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Advances in surface-enhanced Raman scattering (SERS) detection have helped to overcome the limitations of traditional in vitro diagnostic methods, such as fluorescence and chemiluminescence, owing to its high sensitivity and multiplex detection capability. However, for the implementation of SERS detection technology in disease diagnosis, a SERS-based assay platform capable of analyzing clinical samples is essential. Moreover, infectious diseases like COVID-19 require the development of point-of-care (POC) diagnostic technologies that can rapidly and accurately determine infection status. As an effective assay platform, SERS-based bioassays utilize SERS nanotags labeled with protein or DNA receptors on Au or Ag nanoparticles, serving as highly sensitive optical probes. Additionally, a microdevice is necessary as an interface between the target biomolecules and SERS nanotags. This review aims to introduce various microdevices developed for SERS detection, available for POC diagnostics, including LFA strips, microfluidic chips, and microarray chips. Furthermore, the article presents research findings reported in the last 20 years for the SERS-based bioassay of various diseases, such as cancer, cardiovascular diseases, and infectious diseases. Finally, the prospects of SERS bioassays are discussed concerning the integration of SERS-based microdevices and portable Raman readers into POC systems, along with the utilization of artificial intelligence technology.
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Affiliation(s)
- Sungwoon Lee
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Hajun Dang
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Joung-Il Moon
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Kihyun Kim
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Younju Joung
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Sohyun Park
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Qian Yu
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Jiadong Chen
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Mengdan Lu
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Lingxin Chen
- School of Pharmacy, Binzhou Medical University, Yantai, 264003, China
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Yantai 264003, China.
| | - Sang-Woo Joo
- Department of Information Communication, Materials, and Chemistry Convergence Technology, Soongsil University, Seoul 06978, South Korea.
| | - Jaebum Choo
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
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3
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Lyu N, Hassanzadeh-Barforoushi A, Rey Gomez LM, Zhang W, Wang Y. SERS biosensors for liquid biopsy towards cancer diagnosis by detection of various circulating biomarkers: current progress and perspectives. NANO CONVERGENCE 2024; 11:22. [PMID: 38811455 PMCID: PMC11136937 DOI: 10.1186/s40580-024-00428-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 05/09/2024] [Indexed: 05/31/2024]
Abstract
Liquid biopsy has emerged as a promising non-invasive strategy for cancer diagnosis, enabling the detection of various circulating biomarkers, including circulating tumor cells (CTCs), circulating tumor nucleic acids (ctNAs), circulating tumor-derived small extracellular vesicles (sEVs), and circulating proteins. Surface-enhanced Raman scattering (SERS) biosensors have revolutionized liquid biopsy by offering sensitive and specific detection methodologies for these biomarkers. This review comprehensively examines the application of SERS-based biosensors for identification and analysis of various circulating biomarkers including CTCs, ctNAs, sEVs and proteins in liquid biopsy for cancer diagnosis. The discussion encompasses a diverse range of SERS biosensor platforms, including label-free SERS assay, magnetic bead-based SERS assay, microfluidic device-based SERS system, and paper-based SERS assay, each demonstrating unique capabilities in enhancing the sensitivity and specificity for detection of liquid biopsy cancer biomarkers. This review critically assesses the strengths, limitations, and future directions of SERS biosensors in liquid biopsy for cancer diagnosis.
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Affiliation(s)
- Nana Lyu
- School of Natural Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | | | - Laura M Rey Gomez
- School of Natural Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Wei Zhang
- School of Natural Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Yuling Wang
- School of Natural Sciences, Macquarie University, Sydney, NSW, 2109, Australia.
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4
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Myslicka M, Kawala-Sterniuk A, Bryniarska A, Sudol A, Podpora M, Gasz R, Martinek R, Kahankova Vilimkova R, Vilimek D, Pelc M, Mikolajewski D. Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes. Arch Dermatol Res 2024; 316:99. [PMID: 38446274 DOI: 10.1007/s00403-024-02828-1] [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: 12/28/2023] [Revised: 12/28/2023] [Accepted: 01/25/2024] [Indexed: 03/07/2024]
Abstract
This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis.
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Affiliation(s)
- Maria Myslicka
- Faculty of Medicine, Wroclaw Medical University, J. Mikulicza-Radeckiego 5, 50-345, Wroclaw, Poland.
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland.
| | - Anna Bryniarska
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland
| | - Adam Sudol
- Faculty of Natural Sciences and Technology, University of Opole, Dmowskiego 7-9, 45-368, Opole, Poland
| | - Michal Podpora
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland
| | - Rafal Gasz
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland
| | - Radek Martinek
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, Ostrava, 70800, Czech Republic
| | - Radana Kahankova Vilimkova
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758, Opole, Poland
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, Ostrava, 70800, Czech Republic
| | - Dominik Vilimek
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, Ostrava, 70800, Czech Republic
| | - Mariusz Pelc
- Institute of Computer Science, University of Opole, Oleska 48, 45-052, Opole, Poland
- School of Computing and Mathematical Sciences, University of Greenwich, Old Royal Naval College, Park Row, SE10 9LS, London, UK
| | - Dariusz Mikolajewski
- Institute of Computer Science, Kazimierz Wielki University in Bydgoszcz, ul. Kopernika 1, 85-074, Bydgoszcz, Poland
- Neuropsychological Research Unit, 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation, Medical University in Lublin, Gluska 1, 20-439, Lublin, Poland
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5
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Srivastava S, Terai Y, Liu J, Capellini G, Xie YH. Controlling the Nucleation and Growth of Salt from Bodily Fluid for Enhanced Biosensing Applications. BIOSENSORS 2023; 13:1016. [PMID: 38131777 PMCID: PMC10741434 DOI: 10.3390/bios13121016] [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: 11/16/2023] [Revised: 12/02/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) represents a transformative tool in medical diagnostics, particularly for the early detection of key biomarkers such as small extracellular vesicles (sEVs). Its unparalleled sensitivity and compatibility with intricate biological samples make it an ideal candidate for revolutionizing noninvasive diagnostic methods. However, a significant challenge that mars its efficacy is the throughput limitation, primarily anchored in the prerequisite of hotspot and sEV colocalization within a minuscule range. This paper delves deep into this issue, introducing a never-attempted-before approach which harnesses the principles of crystallization-nucleation and growth. By synergistically coupling lasers with plasmonic resonances, we navigate the challenges associated with the analyte droplet drying method and the notorious coffee ring effect. Our method, rooted in a profound understanding of crystallization's materials science, exhibits the potential to significantly increase the areal density of accessible plasmonic hotspots and efficiently guide exosomes to defined regions. In doing so, we not only overcome the throughput challenge but also promise a paradigm shift in the arena of minimally invasive biosensing, ushering in advanced diagnostic capabilities for life-threatening diseases.
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Affiliation(s)
- Siddharth Srivastava
- Department of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USA
| | - Yusuke Terai
- Department of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USA
- Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, Japan
| | - Jun Liu
- Department of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USA
| | - Giovanni Capellini
- IHP—Leibniz Institute for High Performance Microelectronics, 15236 Frankfurt (Oder), Germany;
- Department of Science, Università Degli Studi Roma Tre, Viale Marconi 446, 00146 Rome, Italy
| | - Ya-Hong Xie
- Department of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA 90095, USA
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6
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Bano A, Vats R, Verma D, Yadav P, Kamboj M, Bhardwaj R. Exploring salivary exosomes as early predictors of oral cancer in susceptible tobacco consumers: noninvasive diagnostic and prognostic applications. J Cancer Res Clin Oncol 2023; 149:15781-15793. [PMID: 37668794 DOI: 10.1007/s00432-023-05343-4] [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: 06/20/2023] [Accepted: 08/24/2023] [Indexed: 09/06/2023]
Abstract
BACKGROUND Salivary exosome analysis provides a noninvasive and comprehensive approach with potential applications in oral cancer diagnosis and prognosis. The early detection of oral cancer has remained a critical concern in enhancing the quality of life, especially for individuals who consume tobacco and are at greater risk of developing the disease. The current study investigates the potential of salivary exosomes in screening smokers for early signs and transformations of oral cancer. METHODS Morphological characterization of salivary exosomes among three study groups (non-smokers as control, smokers as high-risk tobacco consumers, and Oral cancer) (n = 120) was carried out through dynamic light scattering, and nanoparticle tracking analysis. For molecular characterization, EXOCET and Fourier transform infrared spectroscopy methods were utilized. The expression of the exosomal surface protein CD63 was evaluated using Western blotting. RESULTS Salivary exosomes exhibit noticeable differences in size between control group and tobacco consumers. The differentiation extended beyond exosome size and included variations in concentration and bio-molecular composition, as determined by FTIR screening. Tobacco consumers and oral cancer groups showed significantly larger and more concentrated exosomes compared to the healthy group. CONCLUSION Our study provides strong evidence that the properties of salivary exosomes can serve as reliable noninvasive biomarkers for distinguishing tobacco consumers from non-smokers and oral cancer patients. Our results underscore the potential of exosome-based diagnostics in early oral cancer detection for high-risk individuals. The larger size and higher concentration of exosomes in tobacco consumers indicate early changes in cell secretions associated with the transformation from healthy to abnormal cells.
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Affiliation(s)
- Afsareen Bano
- Centre for Medical Biotechnology, Maharshi Dayanand University, Rohtak, Haryana, 122001, India
| | - Ravina Vats
- Centre for Medical Biotechnology, Maharshi Dayanand University, Rohtak, Haryana, 122001, India
| | - Deepika Verma
- Department of Biochemistry, All India Institute of Medical Sciences, New Delhi, Delhi, 110029, India
| | - Pooja Yadav
- Centre for Medical Biotechnology, Maharshi Dayanand University, Rohtak, Haryana, 122001, India
| | - Mala Kamboj
- Department of Oral Pathology, Postgraduate Institute of Dental Sciences, Rohtak, Haryana, 124001, India
| | - Rashmi Bhardwaj
- Centre for Medical Biotechnology, Maharshi Dayanand University, Rohtak, Haryana, 122001, India.
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7
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Dixon K, Bonon R, Ivander F, Ale Ebrahim S, Namdar K, Shayegannia M, Khalvati F, Kherani NP, Zavodni A, Matsuura N. Using Machine Learning and Silver Nanoparticle-Based Surface-Enhanced Raman Spectroscopy for Classification of Cardiovascular Disease Biomarkers. ACS APPLIED NANO MATERIALS 2023; 6:15385-15396. [PMID: 37706067 PMCID: PMC10496841 DOI: 10.1021/acsanm.3c01442] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 08/03/2023] [Indexed: 09/15/2023]
Abstract
Characterizing complex biofluids using surface-enhanced Raman spectroscopy (SERS) coupled with machine learning (ML) has been proposed as a powerful tool for point-of-care detection of clinical disease. ML is well-suited to categorizing otherwise uninterpretable, patient-derived SERS spectra that contain a multitude of low concentration, disease-specific molecular biomarkers among a dense spectral background of biological molecules. However, ML can generate false, non-generalizable models when data sets used for model training are inadequate. It is thus critical to determine how different SERS experimental methodologies and workflow parameters can potentially impact ML disease classification of clinical samples. In this study, a label-free, broadband, Ag nanoparticle-based SERS platform was coupled with ML to assess simulated clinical samples for cardiovascular disease (CVD), containing randomized combinations of five key CVD biomarkers at clinically relevant concentrations in serum. Raman spectra obtained at 532, 633, and 785 nm from up to 300 unique samples were classified into physiological and pathological categories using two standard ML models. Label-free SERS and ML could correctly classify randomized CVD samples with high accuracies of up to 90.0% at 532 nm using as few as 200 training samples. Spectra obtained at 532 nm produced the highest accuracies with no significant increase achieved using multiwavelength SERS. Sample preparation and measurement methodologies (e.g., different SERS substrate lots, sample volumes, sample sizes, and known variations in randomization and experimental handling) were shown to strongly influence the ML classification and could artificially increase classification accuracies by as much as 27%. This detailed investigation into the proper application of ML techniques for CVD classification can lead to improved data set acquisition required for the SERS community, such that ML on labeled and robust SERS data sets can be practically applied for future point-of-care testing in patients.
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Affiliation(s)
- Katelyn Dixon
- Department
of Electrical and Computer Engineering, University of Toronto, Toronto M5S 1A4, Canada
| | - Raissa Bonon
- Institute
of Biomedical Engineering, University of
Toronto, Toronto M5S 3E2, Canada
| | - Felix Ivander
- Institute
of Biomedical Engineering, University of
Toronto, Toronto M5S 3E2, Canada
| | - Saba Ale Ebrahim
- Department
of Electrical and Computer Engineering, University of Toronto, Toronto M5S 1A4, Canada
| | - Khashayar Namdar
- Institute
of Medical Science, University of Toronto, Toronto M5S 1A8, Canada
| | - Moein Shayegannia
- Department
of Electrical and Computer Engineering, University of Toronto, Toronto M5S 1A4, Canada
| | - Farzad Khalvati
- Institute
of Medical Science, University of Toronto, Toronto M5S 1A8, Canada
- Department
of Medical Imaging, University of Toronto, Toronto M5T 1W7, Canada
- The
Hospital for Sick Children, Toronto, Ontario M5G 1E8, Canada
- Department
of Computer Science, University of Toronto, Toronto M5S 2E4, Canada
- Department
of Mechanical and Industrial Engineering, University of Toronto, Toronto M5S 3G8, Canada
| | - Nazir P. Kherani
- Department
of Electrical and Computer Engineering, University of Toronto, Toronto M5S 1A4, Canada
- Department
of Materials Science and Engineering, University
of Toronto, Toronto M5S 3E4, Canada
| | - Anna Zavodni
- Department
of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto M5T 1W7, Canada
| | - Naomi Matsuura
- Institute
of Biomedical Engineering, University of
Toronto, Toronto M5S 3E2, Canada
- Department
of Materials Science and Engineering, University
of Toronto, Toronto M5S 3E4, Canada
- Department
of Medical Imaging, University of Toronto, Toronto M5T 1W7, Canada
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8
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Dos Santos DP, Sena MM, Almeida MR, Mazali IO, Olivieri AC, Villa JEL. Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends. Anal Bioanal Chem 2023; 415:3945-3966. [PMID: 36864313 PMCID: PMC9981450 DOI: 10.1007/s00216-023-04620-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/02/2023] [Accepted: 02/20/2023] [Indexed: 03/04/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.
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Affiliation(s)
- Diego P Dos Santos
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, 13083-970, Brazil
| | - Marcelo M Sena
- Departamento de Química, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG, 31270-901, Brazil
- Instituto Nacional de Ciência e Tecnologia em Bioanalítica (INCT Bio), Campinas, SP, 13083-970, Brazil
| | - Mariana R Almeida
- Departamento de Química, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG, 31270-901, Brazil
| | - Italo O Mazali
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, 13083-970, Brazil
| | - Alejandro C Olivieri
- Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Instituto de Química Rosario (IQUIR-CONICET), Suipacha 531, 2000, Rosario, Argentina
| | - Javier E L Villa
- Instituto de Química, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, 13083-970, Brazil.
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9
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Tiwari H, Rai N, Singh S, Gupta P, Verma A, Singh AK, Kajal, Salvi P, Singh SK, Gautam V. Recent Advances in Nanomaterials-Based Targeted Drug Delivery for Preclinical Cancer Diagnosis and Therapeutics. Bioengineering (Basel) 2023; 10:760. [PMID: 37508788 PMCID: PMC10376516 DOI: 10.3390/bioengineering10070760] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/20/2023] [Accepted: 06/23/2023] [Indexed: 07/30/2023] Open
Abstract
Nano-oncology is a branch of biomedical research and engineering that focuses on using nanotechnology in cancer diagnosis and treatment. Nanomaterials are extensively employed in the field of oncology because of their minute size and ultra-specificity. A wide range of nanocarriers, such as dendrimers, micelles, PEGylated liposomes, and polymeric nanoparticles are used to facilitate the efficient transport of anti-cancer drugs at the target tumor site. Real-time labeling and monitoring of cancer cells using quantum dots is essential for determining the level of therapy needed for treatment. The drug is targeted to the tumor site either by passive or active means. Passive targeting makes use of the tumor microenvironment and enhanced permeability and retention effect, while active targeting involves the use of ligand-coated nanoparticles. Nanotechnology is being used to diagnose the early stage of cancer by detecting cancer-specific biomarkers using tumor imaging. The implication of nanotechnology in cancer therapy employs photoinduced nanosensitizers, reverse multidrug resistance, and enabling efficient delivery of CRISPR/Cas9 and RNA molecules for therapeutic applications. However, despite recent advancements in nano-oncology, there is a need to delve deeper into the domain of designing and applying nanoparticles for improved cancer diagnostics.
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Affiliation(s)
- Harshita Tiwari
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India
- Department of Botany, Institute of Science, Banaras Hindu University, Varanasi 221005, India
| | - Nilesh Rai
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Swati Singh
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Priyamvada Gupta
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Ashish Verma
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Akhilesh Kumar Singh
- Department of Oral and Maxillofacial Surgery, Faculty of Dental Sciences, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Kajal
- Department of Agriculture Biotechnology, National Agri-Food Biotechnology Institute, Sahibzada Ajit Singh Nagar 140306, India
| | - Prafull Salvi
- Department of Agriculture Biotechnology, National Agri-Food Biotechnology Institute, Sahibzada Ajit Singh Nagar 140306, India
| | - Santosh Kumar Singh
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Vibhav Gautam
- Centre of Experimental Medicine and Surgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India
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10
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Abe C, Bhaswant M, Miyazawa T, Miyazawa T. The Potential Use of Exosomes in Anti-Cancer Effect Induced by Polarized Macrophages. Pharmaceutics 2023; 15:pharmaceutics15031024. [PMID: 36986884 PMCID: PMC10054161 DOI: 10.3390/pharmaceutics15031024] [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: 02/15/2023] [Revised: 03/14/2023] [Accepted: 03/21/2023] [Indexed: 03/30/2023] Open
Abstract
The rapid development of aberrant cells outgrowing their normal bounds, which can subsequently infect other body parts and spread to other organs-a process known as metastasis-is one of the significant characteristics of cancer. The main reason why cancer patients die is because of widespread metastases. This abnormal cell proliferation varies in cancers of over a hundred types, and their response to treatment can vary substantially. Several anti-cancer drugs have been discovered to treat various tumors, yet they still have harmful side-effects. Finding novel, highly efficient targeted therapies based on modifications in the molecular biology of tumor cells is essential to reduce the indiscriminate destruction of healthy cells. Exosomes, an extracellular vesicle, are promising as a drug carrier for cancer therapy due to their good tolerance in the body. In addition, the tumor microenvironment is a potential target to regulate in cancer treatment. Therefore, macrophages are polarized toward M1 and M2 phenotypes, which are involved in cancer proliferation and are malignant. It is evident from recent studies that controlled macrophage polarization might contribute to cancer treatment, by the direct way of using miRNA. This review provides an insight into the potential use of exosomes to develop an 'indirect', more natural, and harmless cancer treatment through regulating macrophage polarization.
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Affiliation(s)
- Chizumi Abe
- New Industry Creation Hatchery Center (NICHe), Tohoku University, 6-6-10 Aramaki-aza-Aoba, Aoba-ku, Sendai 980-8579, Japan
| | - Maharshi Bhaswant
- New Industry Creation Hatchery Center (NICHe), Tohoku University, 6-6-10 Aramaki-aza-Aoba, Aoba-ku, Sendai 980-8579, Japan
| | - Teruo Miyazawa
- New Industry Creation Hatchery Center (NICHe), Tohoku University, 6-6-10 Aramaki-aza-Aoba, Aoba-ku, Sendai 980-8579, Japan
| | - Taiki Miyazawa
- New Industry Creation Hatchery Center (NICHe), Tohoku University, 6-6-10 Aramaki-aza-Aoba, Aoba-ku, Sendai 980-8579, Japan
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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: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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Affiliation(s)
| | | | - Venugopal Rao Soma
- Advanced Centre of Research in High Energy Materials (ACRHEM), DRDO Industry Academia—Centre of Excellence (DIA-COE), University of Hyderabad, Hyderabad 500046, Telangana, India
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Classification, Synthetic, and Characterization Approaches to Nanoparticles, and Their Applications in Various Fields of Nanotechnology: A Review. Catalysts 2022. [DOI: 10.3390/catal12111386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Nanoparticles typically have dimensions of less than 100 nm. Scientists around the world have recently become interested in nanotechnology because of its potential applications in a wide range of fields, including catalysis, gas sensing, renewable energy, electronics, medicine, diagnostics, medication delivery, cosmetics, the construction industry, and the food industry. The sizes and forms of nanoparticles (NPs) are the primary determinants of their properties. Nanoparticles’ unique characteristics may be explored for use in electronics (transistors, LEDs, reusable catalysts), energy (oil recovery), medicine (imaging, tumor detection, drug administration), and more. For the aforementioned applications, the synthesis of nanoparticles with an appropriate size, structure, monodispersity, and morphology is essential. New procedures have been developed in nanotechnology that are safe for the environment and can be used to reliably create nanoparticles and nanomaterials. This research aims to illustrate top-down and bottom-up strategies for nanomaterial production, and numerous characterization methodologies, nanoparticle features, and sector-specific applications of nanotechnology.
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