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Tas Z, Ciftci F, Icoz K, Unal M. Emerging biomedical applications of surface-enhanced Raman spectroscopy integrated with artificial intelligence and microfluidic technologies. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025; 339:126285. [PMID: 40294575 DOI: 10.1016/j.saa.2025.126285] [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: 02/14/2025] [Revised: 04/05/2025] [Accepted: 04/22/2025] [Indexed: 04/30/2025]
Abstract
The integration of surface-enhanced Raman spectroscopy (SERS), artificial intelligence (AI), and microfluidics represent a transformative approach for biomedical applications. By combining the molecular sensitivity of SERS, AI-driven spectral analysis, and the precise sample handling of microfluidics, these novel integrated systems enable ultrasensitive, label-free diagnostics with minimal sample processing. The development of portable, cost-effective platforms could democratize advanced diagnostics for resource-limited settings. However, challenges such as reproducibility, clinical validation, and system integration hinder widespread adoption. This review explores these new integrated platforms, beginning with a discussion of SERS principles, their biomedical applications, and the critical roles of AI and microfluidics in enhancing analytical performance. We evaluate recent advances in the application of these integrated systems, while addressing key challenges such as substrate scalability, biocompatibility, and point-of-care translation, with a focus on nanomaterials, AI models, and lab-on-chip designs. Finally, we outline future directions, including multimodal sensing, sustainable materials, and embedded AI for real-time diagnostics, to bridge the gap between technological innovation and clinical implementation.
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Affiliation(s)
- Zehra Tas
- Karaman Provincial Health Directorate, Karaman, 70100, Türkiye
| | - Fatih Ciftci
- Department of Biomedical Engineering, Faculty of Engineering, Fatih Sultan Mehmet Vakıf University, Istanbul, 34445, Türkiye; BioriginAI Research Group, Department of Biomedical Engineering, Fatih Sultan Mehmet Vakıf University, Istanbul, 34015, Türkiye; Department of Technology Transfer Office, Fatih Sultan Mehmet Vakıf University, Istanbul, 34445, Türkiye
| | - Kutay Icoz
- College of Engineering and Energy, Abdullah Al Salem University, Khaldiya, Kuwait.
| | - Mustafa Unal
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA 02015, USA; The Center for Advanced Orthopedic Studies, Department of Orthopaedics, BIDMC, Boston, MA 02015, USA.
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2
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Picchio V, Pontecorvi V, Dhori X, Bordin A, Floris E, Cozzolino C, Frati G, Pagano F, Chimenti I, De Falco E. The emerging role of artificial intelligence applied to exosome analysis: from cancer biology to other biomedical fields. Life Sci 2025; 375:123752. [PMID: 40409585 DOI: 10.1016/j.lfs.2025.123752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2025] [Revised: 05/06/2025] [Accepted: 05/20/2025] [Indexed: 05/25/2025]
Abstract
In recent years, exosomes versatility has prompted their study in the biomedical field for diagnostic, prognostic, and therapeutic applications. Exosomes are bi-lipid small extracellular vesicles (30-150 nm) secreted by various cell types, containing proteins, lipids, and DNA/RNA. They mediate intercellular communication and can influence multiple human physiological and pathological processes. So far, exosome analysis has revealed their role as promising diagnostic tools for human pathologies. Concurrently, artificial intelligence (AI) has revolutionised multiple sectors, including medicine, owing to its ability to analyse large datasets and identify complex patterns. The combination of exosome analysis with AI processing has displayed a novel diagnostic approach for cancer and other diseases. This review explores the current applications and prospects of the combined use of exosomes and AI in medicine. Firstly, we provide a biological overview of exosomes and their relevance in cancer biology. Then we explored exosome isolation techniques and Raman spectroscopy/SERS analysis. Finally, we present a summarised essential guide of AI methods for non-experts, emphasising the advancements made in AI applications for exosome characterisation and profiling in oncology research, as well as in other human diseases.
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Affiliation(s)
- Vittorio Picchio
- Department of Angio Cardio Neurology, IRCCS Neuromed, 86077 Pozzilli, Italy
| | - Virginia Pontecorvi
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University, 04100 Latina, Italy
| | - Xhulio Dhori
- CINECA, Super Computing Applications and Innovation Department, 000185 Roma, Italy
| | - Antonella Bordin
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University, 04100 Latina, Italy
| | - Erica Floris
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University, 04100 Latina, Italy
| | - Claudia Cozzolino
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University, 04100 Latina, Italy
| | - Giacomo Frati
- Department of Angio Cardio Neurology, IRCCS Neuromed, 86077 Pozzilli, Italy; Department of Medical Surgical Sciences and Biotechnologies, Sapienza University, 04100 Latina, Italy
| | - Francesca Pagano
- Institute of Biochemistry and Cell Biology, National Council of Research (IBBC-CNR), 00015 Monterotondo,Italy
| | - Isotta Chimenti
- Department of Angio Cardio Neurology, IRCCS Neuromed, 86077 Pozzilli, Italy; Department of Medical Surgical Sciences and Biotechnologies, Sapienza University, 04100 Latina, Italy; CINECA, Super Computing Applications and Innovation Department, 000185 Roma, Italy; Institute of Biochemistry and Cell Biology, National Council of Research (IBBC-CNR), 00015 Monterotondo,Italy; Maria Cecilia Hospital, GVM Care & Research, 48033 Cotignola, Italy.
| | - Elena De Falco
- Department of Angio Cardio Neurology, IRCCS Neuromed, 86077 Pozzilli, Italy; Department of Medical Surgical Sciences and Biotechnologies, Sapienza University, 04100 Latina, Italy; CINECA, Super Computing Applications and Innovation Department, 000185 Roma, Italy; Institute of Biochemistry and Cell Biology, National Council of Research (IBBC-CNR), 00015 Monterotondo,Italy; Maria Cecilia Hospital, GVM Care & Research, 48033 Cotignola, Italy
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3
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Behrouzi K, Khodabakhshi Fard Z, Chen CM, He P, Teng M, Lin L. Plasmonic coffee-ring biosensing for AI-assisted point-of-care diagnostics. Nat Commun 2025; 16:4597. [PMID: 40382337 DOI: 10.1038/s41467-025-59868-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 05/07/2025] [Indexed: 05/20/2025] Open
Abstract
A major challenge in addressing global health issues is developing simple, affordable biosensors with high sensitivity and specificity. Significant progress has been made in at-home medical detection kits, especially during the COVID-19 pandemic. Here, we demonstrated a coffee-ring biosensor with ultrahigh sensitivity, utilizing the evaporation of two sessile droplets and the formation of coffee-rings with asymmetric nanoplasmonic patterns to detect disease-relevant proteins as low as 3 pg/ml, under 12 min. Experimentally, a protein-laden droplet dries on a nanofibrous membrane, pre-concentrating biomarkers at the coffee ring. A second plasmonic droplet with functionalized gold nanoshells is then deposited at an overlapping spot and dried, forming a visible asymmetric plasmonic pattern due to distinct aggregation mechanisms. To enhance detection sensitivity, a deep neural model integrating generative and convolutional networks was used to enable quantitative biomarker diagnosis from smartphone photos. We tested four different proteins, Procalcitonin (PCT) for sepsis, SARS-CoV-2 Nucleocapsid (N) protein for COVID-19, Carcinoembryonic antigen (CEA) and Prostate-specific antigen (PSA) for cancer diagnosis, showing a working concentration range over five orders of magnitude. Sensitivities surpass equivalent lateral flow immunoassays by over two orders of magnitude using human saliva samples. The detection principle, along with the device, and materials can be further advanced for early disease diagnostics.
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Affiliation(s)
- Kamyar Behrouzi
- Department of Mechanical Engineering, University of California, Berkeley, CA, USA.
- Berkeley Sensor and Actuator Center (BSAC), Berkeley, CA, USA.
| | | | - Chun-Ming Chen
- Department of Mechanical Engineering, University of California, Berkeley, CA, USA
| | - Peisheng He
- Department of Mechanical Engineering, University of California, Berkeley, CA, USA
- Berkeley Sensor and Actuator Center (BSAC), Berkeley, CA, USA
| | - Megan Teng
- Department of Mechanical Engineering, University of California, Berkeley, CA, USA
- Berkeley Sensor and Actuator Center (BSAC), Berkeley, CA, USA
| | - Liwei Lin
- Department of Mechanical Engineering, University of California, Berkeley, CA, USA.
- Berkeley Sensor and Actuator Center (BSAC), Berkeley, CA, USA.
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4
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Chen B, Gao J, Sun H, Chen Z, Qiu X. Innovative applications of SERS in precision medicine: In situ and real-time live imaging. Talanta 2025; 294:128225. [PMID: 40327985 DOI: 10.1016/j.talanta.2025.128225] [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: 03/02/2025] [Revised: 04/20/2025] [Accepted: 04/24/2025] [Indexed: 05/08/2025]
Abstract
Surface-enhanced Raman scattering (SERS), a molecular spectroscopic technique with high sensitivity and specificity, has demonstrated groundbreaking potential in precision medicine in recent years. This review systematically summarizes recent advancements in SERS technology for in situ and real-time live imaging, focusing on its core value in early tumor diagnosis, intraoperative navigation, drug delivery monitoring, and dynamic pathological analysis. By optimizing nanoscale probe design-including targeted functionalization, enhanced biocompatibility, and integration with imaging systems-SERS overcomes the sensitivity and spatiotemporal resolution limitations of traditional imaging techniques, enabling precise capture and dynamic tracking of molecular events in live biological environments. The article further analyzes challenges in clinical translation, such as signal stability in complex biological environments, multimodal imaging coordination, and standardized data processing methods. Future directions for personalized therapy and intelligent integrated diagnostics are also discussed.
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Affiliation(s)
- Biqing Chen
- Gynaecology and Obstetrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Heilongjiang, 150081, PR China.
| | - Jiayin Gao
- Gynaecology and Obstetrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Heilongjiang, 150081, PR China
| | - Haizhu Sun
- Gynaecology and Obstetrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Heilongjiang, 150081, PR China
| | - Zhi Chen
- Gynaecology and Obstetrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Heilongjiang, 150081, PR China
| | - Xiaohong Qiu
- Gynaecology and Obstetrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Heilongjiang, 150081, PR China.
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Wang Z, Li L, Huang L, Zhang Y, Hong Y, He W, Chen Y, Yin G, Zhou G. Radial SERS acquisition on coffee ring for Serum-based breast cancer diagnosis through Multilayer Perceptron. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025; 330:125692. [PMID: 39756138 DOI: 10.1016/j.saa.2024.125692] [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: 08/31/2024] [Revised: 12/10/2024] [Accepted: 12/29/2024] [Indexed: 01/07/2025]
Abstract
The coffee-ring effect, involving spontaneous solute separation, has demonstrated promising potential in the context of patient serum analysis. In this study, an approach leveraging the coffee-ring-based analyte redistribution was developed for spectral analysis of surface-enhanced Raman scattering (SERS). By performing radical SERS scanning through the coffee-ring area and sampling across the coffee ring, complicated chemical information was spatially gathered for further spectra analysis. The corresponding application in classification of serum samples from breast cancer patients was also proposed. A simulated serum environment was constructed by mixing phenylalanine, hypoxanthine, and bovine serum albumin (BSA), yielding the coffee-ring patterns along with gold nanoparticles. Distinct divergence in the distributions between hypoxanthine and phenylalanine within the rings were characterized, which is attributed to the inherent electrostatic properties of the noble metal colloid and the interactions among different solvents. Subsequently, this method was applied to serum samples from patients diagnosed with the four breast cancer subtypes. By preparing serum with SERS substrates and forming the coffee-ring patterns, radial SERS scanning was conducted across the rings. The acquired spectra were spatially segmented and processed by employing a multilayer perceptron for learning and prediction. The classification results demonstrated a predictive accuracy of 85.7% in distinguishing among the four breast cancer subtypes, highlighting the feasibility and effectiveness of the coffee-ring assisted radial SERS analysis.
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Affiliation(s)
- Zehua Wang
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Lintao Li
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China.
| | - Libin Huang
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610065, China
| | - Yating Zhang
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yan Hong
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Wei He
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yuanming Chen
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Gang Yin
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China
| | - Guoyun Zhou
- School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 611731, China
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6
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Oktem F, Akdeniz M, Al-Shaebi Z, Akyol G, Keklik M, Aydin O. SERS and Machine Learning-Enabled Liquid Biopsy: A Promising Tool for Early Detection and Recurrence Prediction in Acute Leukemia. ACS OMEGA 2025; 10:11887-11899. [PMID: 40191347 PMCID: PMC11966330 DOI: 10.1021/acsomega.4c08499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2024] [Revised: 02/23/2025] [Accepted: 02/27/2025] [Indexed: 04/09/2025]
Abstract
Acute leukemia (AL), classified as acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL), is a hematologic malignancy caused by the uncontrolled proliferation of leucocytes in the bone marrow. Early detection of AL is crucial for clinical treatment. Detection methods of AL are currently blood tests, bone marrow tests, imaging, and spinal fluid tests. However, these tests have drawbacks, such as high cost and time consumption. Liquid biopsy using biological fluids such as blood or serum is an emerging technique for noninvasive cancer detection and monitoring. Surface-enhanced Raman spectroscopy (SERS), which enhanced Raman signals by the interaction of plasmonic nanostructures with the analyte, is a highly sensitive and specific detection method with simple sample preparation that has been used in combination with machine learning techniques to analyze liquid biopsy. In this study, we developed a SERS-based liquid biopsy approach that enables accurate classification of AML and ALL subtypes and the prediction of disease recurrence. SERS spectra of serum samples from 24 healthy individuals, 43 AML patients, and 18 ALL patients were obtained using an Ag-based SERS substrate and clustered using hierarchical cluster analysis (HCA). The spectra were then classified using three commonly used classifiers, namely, support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN). Our findings demonstrate that the RF classifier has the highest accuracy values, with 96.1, 95.5, and 98.5% for classifying three groups and predicting the recurrence of AML and ALL, respectively. The combination of SERS-based serum analysis with machine learning algorithms represents a remarkable advancement in the realm of hematological disease diagnostics, particularly for AML and ALL. This approach not only facilitates the precise differentiation of disease subtypes but also introduces the novel capability of prognosticating disease recurrence.
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Affiliation(s)
- Fatih Oktem
- Department
of Hematology, Faculty of Medicine, Erciyes
University, 38039 Kayseri, Turkiye
| | - Munevver Akdeniz
- Department
of Biomedical Engineering, Erciyes University, 38039 Kayseri, Turkiye
- Nanothera
Lab, Drug Application and Research Center (ERFARMA), Erciyes University, 38039 Kayseri, Turkiye
| | - Zakarya Al-Shaebi
- Department
of Biomedical Engineering, Erciyes University, 38039 Kayseri, Turkiye
- Nanothera
Lab, Drug Application and Research Center (ERFARMA), Erciyes University, 38039 Kayseri, Turkiye
| | - Gulsah Akyol
- Department
of Hematology, Faculty of Medicine, Erciyes
University, 38039 Kayseri, Turkiye
| | - Muzaffer Keklik
- Department
of Hematology, Faculty of Medicine, Erciyes
University, 38039 Kayseri, Turkiye
| | - Omer Aydin
- Department
of Biomedical Engineering, Erciyes University, 38039 Kayseri, Turkiye
- Nanothera
Lab, Drug Application and Research Center (ERFARMA), Erciyes University, 38039 Kayseri, Turkiye
- Clinical
Engineering Research and Implementation Center (ERKAM), Erciyes University, 38040 Kayseri, Turkiye
- Nanotechnology
Research and Application Center (ERNAM), Erciyes University, 38040 Kayseri, Turkiye
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7
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Liu YJ, Kyne M, Kang C, Wang C. Raman spectroscopy in extracellular vesicles analysis: Techniques, applications and advancements. Biosens Bioelectron 2025; 270:116970. [PMID: 39603214 DOI: 10.1016/j.bios.2024.116970] [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: 05/20/2024] [Revised: 11/15/2024] [Accepted: 11/18/2024] [Indexed: 11/29/2024]
Abstract
Raman spectroscopy provides a robust approach for detailed analysis of the chemical and molecular profiles of extracellular vesicles (EVs). Recent advancements in Raman techniques have significantly enhanced the sensitivity and accuracy of EV characterization, enabling precise detection and profiling of molecular components within EV samples. This review introduces and compares various Raman-based techniques for EV characterization. These include Raman spectroscopy (RS), which provides fundamental molecular information; Raman trapping analysis (RTA), which combines optical trapping with Raman scattering for the manipulation and analysis of individual EVs; surface-enhanced Raman spectroscopy (SERS), which enhances the Raman signal through the use of metallic nanostructures, significantly improving sensitivity; and microfluidic SERS, which integrates SERS with microfluidic platforms to allow high-throughput, label-free analysis of EVs in biological fluids. In addition to comparing various Raman techniques, this review provides a comprehensive analysis that includes comparisons of machine learning methods, EV isolation techniques, and characterization strategies. By integrating these approaches, the review presents a holistic perspective on Raman-based EV analysis, covering profiling, purity, heterogeneity and size analysis as well as imaging. The combined assessment of Raman technologies with advanced computational and experimental methodologies supports the development of more robust diagnostic and therapeutic applications involving EVs.
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Affiliation(s)
- Ya-Juan Liu
- Key Laboratory of Molecular Target & Clinical Pharmacology, and the NMPA & State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences & the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, 511436, China.
| | - Michelle Kyne
- School of Chemistry, National University of Ireland, Galway, Galway, H91 CF50, Ireland
| | - Chao Kang
- School of Chemistry and Chemical Engineering, Guizhou University, Guiyang, 550025, China.
| | - Cheng Wang
- Precise Genome Engineering Center, School of Life Sciences, Guangzhou University, Guangzhou, China; Smurfit Institute of Genetics, Trinity College Dublin, Dublin 2, Ireland.
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8
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Song L, Xue F, Li T, Zhang Q, Xu X, He C, Zhao B, Han XX, Cai L. Differential Diagnosis of Urinary Cancers by Surface-Enhanced Raman Spectroscopy and Machine Learning. Anal Chem 2025; 97:27-32. [PMID: 39757799 DOI: 10.1021/acs.analchem.4c05287] [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: 01/07/2025]
Abstract
Bladder, kidney, and prostate cancers are prevalent urinary cancers, and developing efficient detection methods is of significance for the early diagnosis of them. However, noninvasive and sensitive detection of urinary cancers still challenges traditional techniques. In this study, we developed a SERS-based method to analyze serum samples from patients with urinary cancers. Rapid, label-free, and highly sensitive detection of human sera is achieved by cleaning and aggregating silver nanoparticles. Furthermore, a long short-term memory deep learning algorithm is used to distinguish serum spectra, and the performance of the model is evaluated by comparing the accuracy, sensitivity, specificity, and receiver operating characteristic curves. Taking advantage of SERS and machine learning in sensitivity and data processing, the three urinary cancers are clearly classified. This is the first attempt to exploit the SERS-machine learning strategy to discriminate multiple urinary cancers with clinical serum samples, and our results showed the potential application of this method in the early diagnosis and screening of cancers.
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Affiliation(s)
- Li Song
- National Engineering Laboratory for AIDS Vaccine, School of Life Sciences, Jilin University, Changchun 130012, P. R. China
- State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Fei Xue
- Department of Laboratory Medicine, China-Japan Union Hospital of Jilin University, Changchun 130033, P. R. China
| | - Tingmiao Li
- Department of Laboratory Medicine, China-Japan Union Hospital of Jilin University, Changchun 130033, P. R. China
| | - Qian Zhang
- National Engineering Laboratory for AIDS Vaccine, School of Life Sciences, Jilin University, Changchun 130012, P. R. China
| | - Xuesong Xu
- Department of Laboratory Medicine, China-Japan Union Hospital of Jilin University, Changchun 130033, P. R. China
| | - Chengyan He
- Department of Laboratory Medicine, China-Japan Union Hospital of Jilin University, Changchun 130033, P. R. China
| | - Bing Zhao
- State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Xiao Xia Han
- State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Linjun Cai
- National Engineering Laboratory for AIDS Vaccine, School of Life Sciences, Jilin University, Changchun 130012, P. R. China
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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] [MESH Headings] [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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Tabasz T, Szymańska N, Bąk-Drabik K, Damasiewicz-Bodzek A, Nowak A. Is Raman Spectroscopy of Fingernails a Promising Tool for Diagnosing Systemic and Dermatological Diseases in Adult and Pediatric Populations? MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1283. [PMID: 39202564 PMCID: PMC11356747 DOI: 10.3390/medicina60081283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Accepted: 08/05/2024] [Indexed: 09/03/2024]
Abstract
Background: Raman spectroscopy is a well-known tool used in criminology, molecular biology, and histology. It is also applied to diagnose bone mineral disorders by taking advantage of the similarity of the structure of keratin and bone collagen. Raman spectroscopy can also be used in dermatology and diabetology. The purpose of the present review is to critically evaluate the available research about the use of Raman spectroscopy in the mentioned areas of medicine. Methodology: PubMed was searched for peer-reviewed articles on the subject of use of Raman spectroscopy in bone mineral disorders, dermatology, and diabetes mellitus. Results: Nail keratin and bone collagen are related structural proteins that require disulfide bond for structural stability. Therefore, Raman spectroscopy of keratin may have potential as a diagnostic tool for screening bone quality and distinguishing patients at risk of fracture for reasons different from low bone mineral density (BMD) in the adult women population. Raman spectroscopy can also investigate the changes in keratin's structure in nails affected by onychomycosis and distinguish between healthy and onychomycosis nail samples. It could also reduce the need for nail biopsy by distinguishing between dermatophytic and non-dermatophytic agents of onychomycosis. Additionally, Raman spectroscopy could expedite the diagnostic process in psoriasis (by assessing the secondary structure of keratin) and in diabetes mellitus (by examining the protein glycation level). Conclusions: In adult populations, Raman spectroscopy is a promising and safe method for assessing the structure of fingernails. However, data are scarce in the pediatric population; therefore, more studies are required in children.
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Affiliation(s)
- Teresa Tabasz
- Faculty of Medical Sciences in Zabrze, Students Association, Medical University of Silesia, 41-808 Katowice, Poland; (T.T.); (N.S.)
| | - Natalia Szymańska
- Faculty of Medical Sciences in Zabrze, Students Association, Medical University of Silesia, 41-808 Katowice, Poland; (T.T.); (N.S.)
| | - Katarzyna Bąk-Drabik
- Department of Paediatrics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 41-808 Katowice, Poland
| | - Aleksandra Damasiewicz-Bodzek
- Department of Chemistry, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Katowice, Poland; (A.D.-B.); (A.N.)
| | - Agnieszka Nowak
- Department of Chemistry, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Katowice, Poland; (A.D.-B.); (A.N.)
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11
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Zhang Q, Lin Y, Lin D, Lin X, Liu M, Tao H, Wu J, Wang T, Wang C, Feng S. Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms. Talanta 2024; 275:126136. [PMID: 38692045 DOI: 10.1016/j.talanta.2024.126136] [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: 02/05/2024] [Revised: 04/06/2024] [Accepted: 04/19/2024] [Indexed: 05/03/2024]
Abstract
Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.
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Affiliation(s)
- Qiyi Zhang
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Yuxiang Lin
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian, 350001, China
| | - Duo Lin
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Xueliang Lin
- Fujian Provincial Key Laboratory for Advanced Micro-nano Photonics Technology and Devices, Quanzhou Normal University, Quanzhou, 362000, China
| | - Miaomiao Liu
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Hong Tao
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Jinxun Wu
- Department of Pathology, Fuzhou Lianjiang Country Hospital, Fuzhou, Fujian, 350500, China
| | - Tingyin Wang
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China.
| | - Chuan Wang
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian, 350001, China.
| | - Shangyuan Feng
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China.
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12
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Nguyen HA, Mai QD, Nguyet Nga DT, Pham MK, Nguyen QK, Do TH, Luong VT, Lam VD, Le AT. Paper/GO/e-Au flexible SERS sensors for in situ detection of tricyclazole in orange juice and on cucumber skin at the sub-ppb level: machine learning-assisted data analysis. NANOSCALE ADVANCES 2024; 6:3106-3118. [PMID: 38868820 PMCID: PMC11166118 DOI: 10.1039/d3na01113e] [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: 12/13/2023] [Accepted: 04/23/2024] [Indexed: 06/14/2024]
Abstract
Despite being an excellent surface enhanced Raman scattering (SERS) active material, gold nanoparticles were difficult to be loaded onto the surface of filter paper to fabricate flexible SERS substrates. In this study, electrochemically synthesized gold nanoparticles (e-AuNPs) were deposited on graphene oxide (GO) nanosheets in solution by ultrasonication, resulting in the formation of a GO/Au hybrid material. Thanks to the support of GO, the hybrid material could adhere onto the surface of filter paper, which was immersed into a GO/Au solution for 24 h and dried naturally at room temperature. The paper-based materials were then employed as substrates for a surface enhanced Raman scattering (SERS) sensing platform to detect tricyclazole (TCZ), a widely used pesticide, resulting in better sensitivity compared to the use of paper/Au SERS sensors. With the most optimal GO content of 4%, paper/GO/Au SERS sensors could achieve a limit of detection of 1.32 × 10-10 M in standard solutions. Furthermore, the filter paper-based SERS sensors also exhibited significant advantages in sample collection in real samples. On one hand, the sensors were dipped into orange juice, allowing TCZ molecules in this real sample to be adsorbed onto their SERS active surface. On the other hand, they were pasted onto cucumber skin to collect the analytes. As a result, the paper/GO/Au SERS sensors could sense TCZ in orange juice and on cucumber skin at concentrations as low as 10-9 M (∼2 ppb). In addition, a machine learning model was designed and developed, allowing the sensing system to discriminate TCZ from nine other organic compounds and predict the presence of TCZ on cucumber skin at concentrations down to 10-9 M.
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Affiliation(s)
- Ha Anh Nguyen
- Phenikaa University Nano Institute (PHENA), Phenikaa University Hanoi 12116 Vietnam
| | - Quan Doan Mai
- Phenikaa University Nano Institute (PHENA), Phenikaa University Hanoi 12116 Vietnam
| | - Dao Thi Nguyet Nga
- Phenikaa University Nano Institute (PHENA), Phenikaa University Hanoi 12116 Vietnam
| | - Minh Khanh Pham
- Phenikaa University Nano Institute (PHENA), Phenikaa University Hanoi 12116 Vietnam
| | - Quoc Khanh Nguyen
- Faculty of Computer Science, Phenikaa University Hanoi 12116 Vietnam
| | - Trong Hiep Do
- Faculty of Computer Science, Phenikaa University Hanoi 12116 Vietnam
| | - Van Thien Luong
- Faculty of Computer Science, Phenikaa University Hanoi 12116 Vietnam
| | - Vu Dinh Lam
- Institute of Materials Science (IMS), Graduate University of Science and Technology (GUST), Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Hanoi 10000 Vietnam
| | - Anh-Tuan Le
- Phenikaa University Nano Institute (PHENA), Phenikaa University Hanoi 12116 Vietnam
- Faculty of Materials Science and Engineering (MSE), Phenikaa University Hanoi 12116 Vietnam
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13
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Kočišová E, Kuižová A, Procházka M. Analytical applications of droplet deposition Raman spectroscopy. Analyst 2024; 149:3276-3287. [PMID: 38770583 DOI: 10.1039/d4an00336e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
The droplet deposition methods in Raman spectroscopy have received considerable attention in the field of analytical sensing focusing on effective pre-concentration of the studied analyte (coffee-ring effect or small spots). This review covers different analytical applications of drop-coating deposition Raman scattering (DCDRS) and droplet deposition surface-enhanced Raman scattering (SERS) spectroscopy. Two main advantages of droplet deposition Raman techniques are considered: the drying-induced segregation of the components from the mixtures (such as body fluids) and the sensitivity of detection of various analytically important molecules. Some recent advanced applications, including clinical cancer diagnosis, are discussed and summarized. Finally, the potential and further perspectives of the droplet deposition Raman methods for analytical studies are introduced.
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Affiliation(s)
- Eva Kočišová
- Charles University, Faculty of Mathematics and Physics, Institute of Physics, Ke Karlovu 5, 121 16 Prague 2, Czech Republic.
| | - Alžbeta Kuižová
- Charles University, Faculty of Mathematics and Physics, Institute of Physics, Ke Karlovu 5, 121 16 Prague 2, Czech Republic.
| | - Marek Procházka
- Charles University, Faculty of Mathematics and Physics, Institute of Physics, Ke Karlovu 5, 121 16 Prague 2, Czech Republic.
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14
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Cheng N, Gao Y, Ju S, Kong X, Lyu J, Hou L, Jin L, Shen B. Serum analysis based on SERS combined with 2D convolutional neural network and Gramian angular field for breast cancer screening. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 312:124054. [PMID: 38382221 DOI: 10.1016/j.saa.2024.124054] [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: 09/12/2023] [Revised: 02/08/2024] [Accepted: 02/17/2024] [Indexed: 02/23/2024]
Abstract
Breast cancer is a significant cause of death among women worldwide. It is crucial to quickly and accurately diagnose breast cancer in order to reduce mortality rates. While traditional diagnostic techniques for medical imaging and pathology samples have been commonly used in breast cancer screening, they still have certain limitations. Surface-enhanced Raman spectroscopy (SERS) is a fast, highly sensitive and user-friendly method that is often combined with deep learning techniques like convolutional neural networks. This combination helps identify unique molecular spectral features, also known as "fingerprint", in biological samples such as serum. Ultimately, this approach is able to accurately screen for cancer. The Gramian angular field (GAF) algorithm can convert one-dimensional (1D) time series into two-dimensional (2D) images. These images can be used for data visualization, pattern recognition and machine learning tasks. In this study, 640 serum SERS from breast cancer patients and healthy volunteers were converted into 2D spectral images by Gramian angular field (GAF) technique. These images were then used to train and test a two-dimensional convolutional neural network-GAF (2D-CNN-GAF) model for breast cancer classification. We compared the performance of the 2D-CNN-GAF model with other methods, including one-dimensional convolutional neural network (1D-CNN), support vector machine (SVM), K-nearest neighbor (KNN) and principal component analysis-linear discriminant analysis (PCA-LDA), using various evaluation metrics such as accuracy, precision, sensitivity, F1-score, receiver operating characteristic (ROC) curve and area under curve (AUC) value. The results showed that the 2D-CNN model outperformed the traditional models, achieving an AUC value of 0.9884, an accuracy of 98.13%, sensitivity of 98.65% and specificity of 97.67% for breast cancer classification. In this study, we used conventional nano-silver sol as the SERS-enhanced substrate and a portable laser Raman spectrometer to obtain the serum SERS data. The 2D-CNN-GAF model demonstrated accurate and automatic classification of breast cancer patients and healthy volunteers. The method does not require augmentation and preprocessing of spectral data, simplifying the processing steps of spectral data. This method has great potential for accurate breast cancer screening and also provides a useful reference in more types of cancer classification and automatic screening.
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Affiliation(s)
- Nuo Cheng
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China
| | - Yan Gao
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China; Chinese Academy of Science, Shenzhen Institutes of Advanced and Technology, Shenzhen 518000, PR China
| | - Shaowei Ju
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China
| | - Xiangwei Kong
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China
| | - Jiugong Lyu
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China; School of Biological Engineering, Dalian University of Technology, Dalian 116024, PR China
| | - Lijie Hou
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China
| | - Lihong Jin
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China
| | - Bingjun Shen
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, PR China
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15
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Wang Y, Fang L, Wang Y, Xiong Z. Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2300668. [PMID: 38072672 PMCID: PMC10870035 DOI: 10.1002/advs.202300668] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 09/08/2023] [Indexed: 02/17/2024]
Abstract
Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.
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Affiliation(s)
- Yumei Wang
- Department of NephrologyUnion HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China
| | - Liuru Fang
- Hubei Province Key Laboratory of Systems Science in Metallurgical ProcessWuhan University of Science and TechnologyWuhan430081China
| | - Yuhua Wang
- Hubei Province Key Laboratory of Systems Science in Metallurgical ProcessWuhan University of Science and TechnologyWuhan430081China
| | - Zuzhao Xiong
- Hubei Province Key Laboratory of Systems Science in Metallurgical ProcessWuhan University of Science and TechnologyWuhan430081China
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16
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Rourke-Funderburg AS, Walter AB, Carroll B, Mahadevan-Jansen A, Locke AK. Development of a Low-Cost Paper-Based Platform for Coffee Ring-Assisted SERS. ACS OMEGA 2023; 8:33745-33754. [PMID: 37744797 PMCID: PMC10515595 DOI: 10.1021/acsomega.3c03690] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 07/14/2023] [Indexed: 09/26/2023]
Abstract
The need for highly sensitive, low-cost, and timely diagnostic technologies at the point of care is increasing. Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopic technique that is an advantageous technique to address this need, as it can rapidly detect analytes in small or dilute samples with improved sensitivity compared to conventional Raman spectroscopy. Despite the many advantages of SERS, one drawback of the technique is poor reproducibility due to variable interactions between nanoparticles and target analytes. To overcome this limitation, coupling SERS with the coffee ring effect has been implemented to concentrate and localize analyte-nanoparticle conjugates for improved signal reproducibility. However, current coffee ring platforms require laborious fabrication steps. Herein, we present a low-cost, two-step fabrication process for coffee ring-assisted SERS, utilizing wax-printed nitrocellulose paper. The platform was designed to produce a highly hydrophobic paper substrate that supports the coffee ring effect and tested using gold nanoparticles for SERS sensing. The nanoparticle concentration and solvent were varied to determine the effect of solution composition on ring formation and center clearance. The SERS signal was validated using 4-mercaptobenzoic acid (MBA) and tested with Moraxella catarrhalis bacteria to ensure functionality for chemical and biological applications. The limit of detection using MBA is 41.56 nM, and the biochemical components of the bacterial cell wall were enhanced with low spectral variability. The developed platform is advantageous due to ease of fabrication and use, representing the next step toward implementing low-cost coffee ring-assisted SERS for point-of-care sensing.
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Affiliation(s)
- Anna S. Rourke-Funderburg
- Department
of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37240-0002, United
States
- Vanderbilt
Biophotonics Center, Vanderbilt University, Nashville, Tennessee 37240-0002, United
States
| | - Alec B. Walter
- Department
of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37240-0002, United
States
- Vanderbilt
Biophotonics Center, Vanderbilt University, Nashville, Tennessee 37240-0002, United
States
| | - Braden Carroll
- Vanderbilt
Biophotonics Center, Vanderbilt University, Nashville, Tennessee 37240-0002, United
States
| | - Anita Mahadevan-Jansen
- Department
of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37240-0002, United
States
- Vanderbilt
Biophotonics Center, Vanderbilt University, Nashville, Tennessee 37240-0002, United
States
| | - Andrea K. Locke
- Department
of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37240-0002, United
States
- Vanderbilt
Biophotonics Center, Vanderbilt University, Nashville, Tennessee 37240-0002, United
States
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37240-0002, United
States
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17
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Sonbhadra S, Mehak, Pandey LM. Biogenesis, Isolation, and Detection of Exosomes and Their Potential in Therapeutics and Diagnostics. BIOSENSORS 2023; 13:802. [PMID: 37622888 PMCID: PMC10452587 DOI: 10.3390/bios13080802] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/01/2023] [Accepted: 08/05/2023] [Indexed: 08/26/2023]
Abstract
The increasing research and rapid developments in the field of exosomes provide insights into their role and significance in human health. Exosomes derived from various sources, such as mesenchymal stem cells, cardiac cells, and tumor cells, to name a few, can be potential therapeutic agents for the treatment of diseases and could also serve as biomarkers for the early detection of diseases. Cellular components of exosomes, several proteins, lipids, and miRNAs hold promise as novel biomarkers for the detection of various diseases. The structure of exosomes enables them as drug delivery vehicles. Since exosomes exhibit potential therapeutic applications, their efficient isolation from complex biological/clinical samples and precise real-time analysis becomes significant. With the advent of microfluidics, nano-biosensors are being designed to capture exosomes efficiently and rapidly. Herein, we have summarized the history, biogenesis, characteristics, functions, and applications of exosomes, along with the isolation, detection, and quantification techniques. The implications of surface modifications to enhance specificity have been outlined. The review also sheds light on the engineered nanoplatforms being developed for exosome detection and capture.
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Affiliation(s)
| | | | - Lalit M. Pandey
- Bio-Interface & Environmental Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam 781039, India; (S.S.); (M.)
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18
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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: 29] [Impact Index Per Article: 14.5] [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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Pino AFS, Espinosa ZYD, Cabrera EVR. Characterization of the Rhizosphere Bacterial Microbiome and Coffee Bean Fermentation in the Castillo-Tambo and Bourbon Varieties in the Popayán-Colombia Plateau. BMC PLANT BIOLOGY 2023; 23:217. [PMID: 37098489 PMCID: PMC10127060 DOI: 10.1186/s12870-023-04182-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/20/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND The microbial biodiversity and the role of microorganisms in the fermentation of washed coffee in Colombia were investigated using the Bourbon and Castillo coffee varieties. DNA sequencing was used to evaluate the soil microbial biota and their contribution to fermentation. The potential benefits of these microorganisms were analyzed, including increased productivity and the need to understand the rhizospheric bacterial species to optimize these benefits. METHODS This study used coffee beans for DNA extraction and 16 S rRNA sequencing. The beans were pulped, samples were stored at 4ºC, and the fermentation process was at 19.5ºC and 24ºC. The fermented mucilage and root-soil samples were collected in duplicate at 0, 12, and 24 h. DNA was extracted from the samples at a concentration of 20 ng/µl per sample, and the data obtained were analyzed using the Mothur platform. RESULTS The study demonstrates that the coffee rhizosphere is a diverse ecosystem composed primarily of microorganisms that cannot be cultured in the laboratory. This suggests that the microbial community may vary depending on the coffee variety and play an essential role in fermentation and overall coffee quality. CONCLUSIONS The study highlights the importance of understanding and optimizing the microbial diversity in coffee production, which could have implications for the sustainability and success of coffee production. DNA sequencing techniques can help characterize the structure of the soil microbial biota and evaluate its contribution to coffee fermentation. Finally, further research is needed to fully understand the biodiversity of coffee rhizospheric bacteria and their role.
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Affiliation(s)
- Andrés Felipe Solis Pino
- Corporación Universitaria Comfacauca - Unicomfacauca, Cl. 4 N. 8-30, Popayán, Cauca, 190001, Colombia.
| | | | - Efren Venancio Ramos Cabrera
- Corporación Universitaria Comfacauca - Unicomfacauca, Cl. 4 N. 8-30, Popayán, Cauca, 190001, Colombia
- Universidad Nacional Abierta y a Distancia - UNAD, Calle 5 # 46N -67, Popayán, Cauca, 190001, Colombia
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20
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Chen X, Wu X, Chen C, Luo C, Shi Y, Li Z, Lv X, Chen C, Su J, Wu L. Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren's syndrome. Sci Rep 2023; 13:5137. [PMID: 36991016 PMCID: PMC10060214 DOI: 10.1038/s41598-023-29943-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 02/13/2023] [Indexed: 03/31/2023] Open
Abstract
The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and standard deviations of the raw spectra of patients with pSS and HCs were calculated. Spectral features were assigned based on the literature. Principal component analysis (PCA) was used to extract the spectral features. Then, a particle swarm optimization (PSO)-support vector machine (SVM) was selected as the method of parameter optimization to rapidly classify patients with pSS and HCs. In this study, the SVM algorithm was used as the classification model, and the radial basis kernel function was selected as the kernel function. In addition, the PSO algorithm was used to establish a model for the parameter optimization method. The training set and test set were randomly divided at a ratio of 7:3. After PCA dimension reduction, the specificity, sensitivity and accuracy of the PSO-SVM model were obtained, and the results were 88.89%, 100% and 94.44%, respectively. This study showed that the combination of Raman spectroscopy and a support vector machine algorithm could be used as an effective pSS diagnosis method with broad application value.
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Affiliation(s)
- Xiaomei Chen
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Xue Wu
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Chen Chen
- College of Software, Xinjiang University, Urumqi, Xinjiang, China
| | - Cainan Luo
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Yamei Shi
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Zhengfang Li
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Xiaoyi Lv
- College of Software, Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, Xinjiang, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, Xinjiang, China
| | - Jinmei Su
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China.
- Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Lijun Wu
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China.
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China.
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21
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Shin H, Choi BH, Shim O, Kim J, Park Y, Cho SK, Kim HK, Choi Y. Single test-based diagnosis of multiple cancer types using Exosome-SERS-AI for early stage cancers. Nat Commun 2023; 14:1644. [PMID: 36964142 PMCID: PMC10039041 DOI: 10.1038/s41467-023-37403-1] [Citation(s) in RCA: 90] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 03/16/2023] [Indexed: 03/26/2023] Open
Abstract
Early cancer detection has significant clinical value, but there remains no single method that can comprehensively identify multiple types of early-stage cancer. Here, we report the diagnostic accuracy of simultaneous detection of 6 types of early-stage cancers (lung, breast, colon, liver, pancreas, and stomach) by analyzing surface-enhanced Raman spectroscopy profiles of exosomes using artificial intelligence in a retrospective study design. It includes classification models that recognize signal patterns of plasma exosomes to identify both their presence and tissues of origin. Using 520 test samples, our system identified cancer presence with an area under the curve value of 0.970. Moreover, the system classified the tumor organ type of 278 early-stage cancer patients with a mean area under the curve of 0.945. The final integrated decision model showed a sensitivity of 90.2% at a specificity of 94.4% while predicting the tumor organ of 72% of positive patients. Since our method utilizes a non-specific analysis of Raman signatures, its diagnostic scope could potentially be expanded to include other diseases.
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Affiliation(s)
- Hyunku Shin
- EXoPERT Corporation, Seoul, 02580, Republic of Korea
| | - Byeong Hyeon Choi
- Department of Thoracic and Cardiovascular Surgery, College of Medicine, Korea University Guro Hospital, Seoul, 08308, Republic of Korea
- Korea Artificial Organ Center, Korea University, Seoul, 02841, Republic of Korea
| | - On Shim
- EXoPERT Corporation, Seoul, 02580, Republic of Korea
| | - Jihee Kim
- EXoPERT Corporation, Seoul, 02580, Republic of Korea
| | - Yong Park
- Division of Hematology-Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, 02841, Republic of Korea
| | - Suk Ki Cho
- Division of Thoracic Surgery, Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea
| | - Hyun Koo Kim
- Department of Thoracic and Cardiovascular Surgery, College of Medicine, Korea University Guro Hospital, Seoul, 08308, Republic of Korea.
- Department of Biomedical Sciences, College of Medicine, Korea University, 02841, Seoul, Republic of Korea.
| | - Yeonho Choi
- EXoPERT Corporation, Seoul, 02580, Republic of Korea.
- School of Biomedical Engineering, Korea University, Seoul, 02841, Republic of Korea.
- Department of Biomedical Engineering, Korea University, Seoul, 02841, Republic of Korea.
- Interdisciplinary Program in Precision Public Health, Korea University, 02841, Seoul, Republic of Korea.
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22
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Pal A, Gope A, Sengupta A. Drying of bio-colloidal sessile droplets: Advances, applications, and perspectives. Adv Colloid Interface Sci 2023; 314:102870. [PMID: 37002959 DOI: 10.1016/j.cis.2023.102870] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 03/03/2023] [Accepted: 03/03/2023] [Indexed: 04/03/2023]
Abstract
Drying of biologically-relevant sessile droplets, including passive systems such as DNA, proteins, plasma, and blood, as well as active microbial systems comprising bacterial and algal dispersions, has garnered considerable attention over the last decades. Distinct morphological patterns emerge when bio-colloids undergo evaporative drying, with significant potential in a wide range of biomedical applications, spanning bio-sensing, medical diagnostics, drug delivery, and antimicrobial resistance. Consequently, the prospects of novel and thrifty bio-medical toolkits based on drying bio-colloids have driven tremendous progress in the science of morphological patterns and advanced quantitative image-based analysis. This review presents a comprehensive overview of bio-colloidal droplets drying on solid substrates, focusing on the experimental progress during the last ten years. We provide a summary of the physical and material properties of relevant bio-colloids and link their native composition (constituent particles, solvent, and concentrations) to the patterns emerging due to drying. We specifically examined the drying patterns generated by passive bio-colloids (e.g., DNA, globular, fibrous, composite proteins, plasma, serum, blood, urine, tears, and saliva). This article highlights how the emerging morphological patterns are influenced by the nature of the biological entities and the solvent, micro- and global environmental conditions (temperature and relative humidity), and substrate attributes like wettability. Crucially, correlations between emergent patterns and the initial droplet compositions enable the detection of potential clinical abnormalities when compared with the patterns of drying droplets of healthy control samples, offering a blueprint for the diagnosis of the type and stage of a specific disease (or disorder). Recent experimental investigations of pattern formation in the bio-mimetic and salivary drying droplets in the context of COVID-19 are also presented. We further summarized the role of biologically active agents in the drying process, including bacteria, algae, spermatozoa, and nematodes, and discussed the coupling between self-propulsion and hydrodynamics during the drying process. We wrap up the review by highlighting the role of cross-scale in situ experimental techniques for quantifying sub-micron to micro-scale features and the critical role of cross-disciplinary approaches (e.g., experimental and image processing techniques with machine learning algorithms) to quantify and predict the drying-induced features. We conclude the review with a perspective on the next generation of research and applications based on drying droplets, ultimately enabling innovative solutions and quantitative tools to investigate this exciting interface of physics, biology, data sciences, and machine learning.
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Affiliation(s)
- Anusuya Pal
- University of Warwick, Department of Physics, Coventry CV47AL, West Midlands, UK; Worcester Polytechnic Institute, Department of Physics, Worcester 01609, MA, USA.
| | - Amalesh Gope
- Tezpur University, Department of Linguistics and Language Technology, Tezpur 784028, Assam, India
| | - Anupam Sengupta
- University of Luxembourg, Physics of Living Matter, Department of Physics and Materials Science, Luxembourg L-1511, Luxembourg
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23
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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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24
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Dawuti W, Dou J, Li J, Liu H, Zhao H, Sun L, Chu J, Lin R, Lü G. Rapid Identification of Benign Gallbladder Diseases Using Serum Surface-Enhanced Raman Spectroscopy Combined with Multivariate Statistical Analysis. Diagnostics (Basel) 2023; 13:diagnostics13040619. [PMID: 36832107 PMCID: PMC9955438 DOI: 10.3390/diagnostics13040619] [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: 12/15/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023] Open
Abstract
In this study, we looked at the viability of utilizing serum to differentiate between gallbladder (GB) stones and GB polyps using Surface-enhanced Raman spectroscopy (SERS), which has the potential to be a quick and accurate means of diagnosing benign GB diseases. Rapid and label-free SERS was used to conduct the tests on 148 serum samples, which included those from 51 patients with GB stones, 25 patients with GB polyps and 72 healthy persons. We used an Ag colloid as a Raman spectrum enhancement substrate. In addition, we employed orthogonal partial least squares discriminant analysis (OPLS-DA) and principal component linear discriminant analysis (PCA-LDA) to compare and diagnose the serum SERS spectra of GB stones and GB polyps. The diagnostic results showed that the sensitivity, specificity, and area under curve (AUC) values of the GB stones and GB polyps based on OPLS-DA algorithm reached 90.2%, 97.2%, 0.995 and 92.0%, 100%, 0.995, respectively. This study demonstrated an accurate and rapid means of combining serum SERS spectra with OPLS-DA to identify GB stones and GB polyps.
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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
| | - Hui Liu
- 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
| | - Hui Zhao
- Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Li Sun
- 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
| | - Jin Chu
- 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
| | - 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
- Correspondence: (R.L.); (G.L.)
| | - 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
- Correspondence: (R.L.); (G.L.)
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25
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Zhou H, Xu L, Ren Z, Zhu J, Lee C. Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics. NANOSCALE ADVANCES 2023; 5:538-570. [PMID: 36756499 PMCID: PMC9890940 DOI: 10.1039/d2na00608a] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/06/2022] [Indexed: 06/17/2023]
Abstract
The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.
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Affiliation(s)
- Hong Zhou
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
| | - Liangge Xu
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
- National Key Laboratory of Special Environment Composite Technology, Harbin Institute of Technology Harbin 150001 China
| | - Zhihao Ren
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
| | - Jiaqi Zhu
- National Key Laboratory of Special Environment Composite Technology, Harbin Institute of Technology Harbin 150001 China
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
- NUS Suzhou Research Institute (NUSRI) Suzhou 215123 China
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26
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Cheng Z, Li H, Chen C, Lv X, Zuo E, Han S, Li Z, Liu P, Li H, Chen C. Application of serum SERS technology based on thermally annealed silver nanoparticle composite substrate in breast cancer. Photodiagnosis Photodyn Ther 2023; 41:103284. [PMID: 36646366 DOI: 10.1016/j.pdpdt.2023.103284] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/24/2022] [Accepted: 01/09/2023] [Indexed: 01/15/2023]
Abstract
Liquid biopsy is currently a non-destructive and convenient method of cancer screening, due to human blood containing a variety of cancer-related biomolecules. Therefore, the development of an accurate and rapid breast cancer screening technique combined with breast cancer serum is crucial for the treatment and prognosis of breast cancer patients. In this study, the surface enhanced Raman spectroscopy (SERS) technique is used to enhance the Raman spectroscopy (RS) signal of serum based on a high sensitivity thermally annealed silver nanoparticle/porous silicon bragg mirror (AgNPs/PSB) composite substrate. Compared with RS, SERS reflects more and stronger spectral peak information, which is beneficial to discover new biomarkers of breast cancer. At the same time, to further explore the diagnostic ability of SERS technology for breast cancer. In this study, the raw spectral data are processed by baseline correction, polynomial smoothing, and normalization. Then, the relevant feature information of SERS and RS is extracted by principal component analysis (PCA), and five classification models are established to compare the diagnostic performance of SERS and RS models respectively. The experimental results show that the breast cancer diagnosis model based on the improved SERS substrate combined with the machine learning algorithm can be used to distinguish breast cancer patients from controls. The accuracy, sensitivity, specificity and AUC values of the SVM model are 100%, 100%, 100% and 100%, respectively, as well as the training time of 4ms. The above experimental results show that the SERS technology based on AgNPs/PSB composite substrate, combined with machine learning methods, has great potential in the rapid and accurate identification of breast cancer patients.
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Affiliation(s)
- Zhiyuan Cheng
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Hongyi Li
- Guangzhou Panyu Polytechnic, No. 1342 Shiliang Road, Guangzhou Panyu 511483, Guangdong, China
| | - Chen Chen
- College of Information Science, Engineering Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China
| | - EnGuang Zuo
- College of Information Science, Engineering Xinjiang University, Urumqi 830046, China
| | - Shibin Han
- School of Physical Science and Technology, Xinjiang University, Urumqi 830046, China
| | - Zhongyuan Li
- College of Information Science, Engineering Xinjiang University, Urumqi 830046, China
| | - Pei Liu
- College of Information Science, Engineering Xinjiang University, Urumqi 830046, China
| | - Hongtao Li
- Xinjiang Medical University Affiliated Tumor Hospital, Urumqi 830054, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
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27
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Ding Y, Sun Y, Liu C, Jiang Q, Chen F, Cao Y. SERS-Based Biosensors Combined with Machine Learning for Medical Application. ChemistryOpen 2023; 12:e202200192. [PMID: 36627171 PMCID: PMC9831797 DOI: 10.1002/open.202200192] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/09/2022] [Indexed: 01/12/2023] Open
Abstract
Surface-enhanced Raman spectroscopy (SERS) has shown strength in non-invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi-quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.
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Affiliation(s)
- Yan Ding
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Yang Sun
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Cheng Liu
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Qiao‐Yan Jiang
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Feng Chen
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Yue Cao
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
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28
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Yuan K, Jurado-Sánchez B, Escarpa A. Nanomaterials meet surface-enhanced Raman scattering towards enhanced clinical diagnosis: a review. J Nanobiotechnology 2022; 20:537. [PMID: 36544151 PMCID: PMC9771791 DOI: 10.1186/s12951-022-01711-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 11/15/2022] [Indexed: 12/24/2022] Open
Abstract
Surface-enhanced Raman scattering (SERS) is a very promising tool for the direct detection of biomarkers for the diagnosis of i.e., cancer and pathogens. Yet, current SERS strategies are hampered by non-specific interactions with co-existing substances in the biological matrices and the difficulties of obtaining molecular fingerprint information from the complex vibrational spectrum. Raman signal enhancement is necessary, along with convenient surface modification and machine-based learning to address the former issues. This review aims to describe recent advances and prospects in SERS-based approaches for cancer and pathogens diagnosis. First, direct SERS strategies for key biomarker sensing, including the use of substrates such as plasmonic, semiconductor structures, and 3D order nanostructures for signal enhancement will be discussed. Secondly, we will illustrate recent advances for indirect diagnosis using active nanomaterials, Raman reporters, and specific capture elements as SERS tags. Thirdly, critical challenges for translating the potential of the SERS sensing techniques into clinical applications via machine learning and portable instrumentation will be described. The unique nature and integrated sensing capabilities of SERS provide great promise for early cancer diagnosis or fast pathogens detection, reducing sanitary costs but most importantly allowing disease prevention and decreasing mortality rates.
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Affiliation(s)
- Kaisong Yuan
- Bio-Analytical Laboratory, Shantou University Medical College, No. 22, Xinling Road, Shantou, 515041, China
- Department of Analytical Chemistry, Physical Chemistry, and Chemical Engineering, University of Alcala, Alcala de Henares, 28802, Madrid, Spain
| | - Beatriz Jurado-Sánchez
- Department of Analytical Chemistry, Physical Chemistry, and Chemical Engineering, University of Alcala, Alcala de Henares, 28802, Madrid, Spain
- Chemical Research Institute "Andrés M. del Río", University of Alcala, Alcala de Henares, 28802, Madrid, Spain
| | - Alberto Escarpa
- Department of Analytical Chemistry, Physical Chemistry, and Chemical Engineering, University of Alcala, Alcala de Henares, 28802, Madrid, Spain
- Chemical Research Institute "Andrés M. del Río", University of Alcala, Alcala de Henares, 28802, Madrid, Spain
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29
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Sultangaziyev A, Ilyas A, Dyussupova A, Bukasov R. Trends in Application of SERS Substrates beyond Ag and Au, and Their Role in Bioanalysis. BIOSENSORS 2022; 12:bios12110967. [PMID: 36354477 PMCID: PMC9688019 DOI: 10.3390/bios12110967] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 10/26/2022] [Accepted: 10/30/2022] [Indexed: 05/31/2023]
Abstract
This article compares the applications of traditional gold and silver-based SERS substrates and less conventional (Pd/Pt, Cu, Al, Si-based) SERS substrates, focusing on sensing, biosensing, and clinical analysis. In recent decades plethora of new biosensing and clinical SERS applications have fueled the search for more cost-effective, scalable, and stable substrates since traditional gold and silver-based substrates are quite expensive, prone to corrosion, contamination and non-specific binding, particularly by S-containing compounds. Following that, we briefly described our experimental experience with Si and Al-based SERS substrates and systematically analyzed the literature on SERS on substrate materials such as Pd/Pt, Cu, Al, and Si. We tabulated and discussed figures of merit such as enhancement factor (EF) and limit of detection (LOD) from analytical applications of these substrates. The results of the comparison showed that Pd/Pt substrates are not practical due to their high cost; Cu-based substrates are less stable and produce lower signal enhancement. Si and Al-based substrates showed promising results, particularly in combination with gold and silver nanostructures since they could produce comparable EFs and LODs as conventional substrates. In addition, their stability and relatively low cost make them viable alternatives for gold and silver-based substrates. Finally, this review highlighted and compared the clinical performance of non-traditional SERS substrates and traditional gold and silver SERS substrates. We discovered that if we take the average sensitivity, specificity, and accuracy of clinical SERS assays reported in the literature, those parameters, particularly accuracy (93-94%), are similar for SERS bioassays on AgNP@Al, Si-based, Au-based, and Ag-based substrates. We hope that this review will encourage research into SERS biosensing on aluminum, silicon, and some other substrates. These Al and Si based substrates may respond efficiently to the major challenges to the SERS practical application. For instance, they may be not only less expensive, e.g., Al foil, but also in some cases more selective and sometimes more reproducible, when compared to gold-only or silver-only based SERS substrates. Overall, it may result in a greater diversity of applicable SERS substrates, allowing for better optimization and selection of the SERS substrate for a specific sensing/biosensing or clinical application.
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30
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Gong T, Das CM, Yin MJ, Lv TR, Singh NM, Soehartono AM, Singh G, An QF, Yong KT. Development of SERS tags for human diseases screening and detection. Coord Chem Rev 2022. [DOI: 10.1016/j.ccr.2022.214711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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31
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Mai QD, Nguyen HA, Dinh NX, Thu Thuy NT, Tran QH, Thanh PC, Pham AT, Le AT. Versatile and high performance in-paper flexible SERS chips for simple and in-situ detection of methylene blue in river water and thiram on apple skin. Talanta 2022. [DOI: 10.1016/j.talanta.2022.124114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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32
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Avci E, Yilmaz H, Sahiner N, Tuna BG, Cicekdal MB, Eser M, Basak K, Altıntoprak F, Zengin I, Dogan S, Çulha M. Label-Free Surface Enhanced Raman Spectroscopy for Cancer Detection. Cancers (Basel) 2022; 14:5021. [PMID: 36291805 PMCID: PMC9600112 DOI: 10.3390/cancers14205021] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/04/2022] [Accepted: 10/10/2022] [Indexed: 11/16/2022] Open
Abstract
Blood is a vital reservoir housing numerous disease-related metabolites and cellular components. Thus, it is also of interest for cancer diagnosis. Surface-enhanced Raman spectroscopy (SERS) is widely used for molecular detection due to its very high sensitivity and multiplexing properties. Its real potential for cancer diagnosis is not yet clear. In this study, using silver nanoparticles (AgNPs) as substrates, a number of experimental parameters and scenarios were tested to disclose the potential for this technique for cancer diagnosis. The discrimination of serum samples from cancer patients, healthy individuals and patients with chronic diseases was successfully demonstrated with over 90% diagnostic accuracies. Moreover, the SERS spectra of the blood serum samples obtained from cancer patients before and after tumor removal were compared. It was found that the spectral pattern for serum from cancer patients evolved into the spectral pattern observed with serum from healthy individuals after the removal of tumors. The data strongly suggests that the technique has a tremendous potential for cancer detection and screening bringing the possibility of early detection onto the table.
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Affiliation(s)
- Ertug Avci
- Department of Genetics and Bioengineering, Faculty of Engineering, Yeditepe University, Istanbul 34755, Turkey
| | - Hulya Yilmaz
- Sabanci University Nanotechnology Research and Application Center (SUNUM), Istanbul 34956, Turkey
| | - Nurettin Sahiner
- Department of Ophthalmology, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA
- Department of Chemistry, Canakkale Onsekiz Mart University, Canakkale 17020, Turkey
| | - Bilge Guvenc Tuna
- Department of Biophysics, School of Medicine, Yeditepe University, Istanbul 34755, Turkey
| | - Munevver Burcu Cicekdal
- Department of Medical Biology, School of Medicine, Yeditepe University, Istanbul 34755, Turkey
| | - Mehmet Eser
- Department of General Surgery, School of Medicine, Istinye University, Istanbul 34010, Turkey
| | - Kayhan Basak
- Department of Pathology, Kartal Dr. Lütfi Kırdar City Hospital, University of Health Sciences, Istanbul 34865, Turkey
| | - Fatih Altıntoprak
- Department of General Surgery, Research and Educational Hospital, Sakarya University, Serdivan 54100, Turkey
| | - Ismail Zengin
- Department of General Surgery, Research and Educational Hospital, Sakarya University, Serdivan 54100, Turkey
| | - Soner Dogan
- Department of Medical Biology, School of Medicine, Yeditepe University, Istanbul 34755, Turkey
| | - Mustafa Çulha
- Sabanci University Nanotechnology Research and Application Center (SUNUM), Istanbul 34956, Turkey
- The Knight Cancer Institute, Cancer Early Detection Advanced Research Center (CEDAR), Oregon Health and Science University, Portland, OR 97239, USA
- Department of Chemistry and Physics, College of Science and Mathematics, Augusta University, Augusta, GA 30912, USA
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Constantinou M, Hadjigeorgiou K, Abalde-Cela S, Andreou C. Label-Free Sensing with Metal Nanostructure-Based Surface-Enhanced Raman Spectroscopy for Cancer Diagnosis. ACS APPLIED NANO MATERIALS 2022; 5:12276-12299. [PMID: 36210923 PMCID: PMC9534173 DOI: 10.1021/acsanm.2c02392] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 07/27/2022] [Indexed: 05/03/2023]
Abstract
Surface-Enhanced Raman Spectroscopy (SERS) is a powerful analytical technique for the detection of small analytes with great potential for medical diagnostic applications. Its high sensitivity and excellent molecular specificity, which stems from the unique fingerprint of molecular species, have been applied toward the detection of different types of cancer. The noninvasive and rapid detection offered by SERS highlights its applicability for point-of-care (PoC) deployment for cancer diagnosis, screening, and staging, as well as for predicting tumor recurrence and treatment monitoring. This review provides an overview of the progress in label-free (direct) SERS-based chemical detection for cancer diagnosis with the main focus on the advances in the design and preparation of SERS substrates on the basis of metal nanoparticle structures formed via bottom-up strategies. It begins by introducing a synopsis of the working principles of SERS, including key chemometric approaches for spectroscopic data analysis. Then it introduces the advances of label-free sensing with SERS in cancer diagnosis using biofluids (blood, urine, saliva, sweat) and breath as the detection media. In the end, an outlook of the advances and challenges in cancer diagnosis via SERS is provided.
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Affiliation(s)
- Marios Constantinou
- Department
of Electrical and Computer Engineering, University of Cyprus, Nicosia, 2112, Cyprus
| | - Katerina Hadjigeorgiou
- Department
of Electrical and Computer Engineering, University of Cyprus, Nicosia, 2112, Cyprus
| | - Sara Abalde-Cela
- International
Iberian Nanotechnology Laboratory, Avenida Mestre José Veiga s/n, Braga 4715-330, Portugal
| | - Chrysafis Andreou
- Department
of Electrical and Computer Engineering, University of Cyprus, Nicosia, 2112, Cyprus
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Gong T, Li H, Wang G, Guan F, Huang W, Zhang X. An anti-scratch flexible SERS substrate for pesticide residue detection on the surface of fruits and vegetables. NANOTECHNOLOGY 2022; 33:405501. [PMID: 35767929 DOI: 10.1088/1361-6528/ac7cf3] [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: 03/04/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
We propose an anti-scratch flexible surface-enhanced Raman scattering substrate with arrayed nanocavity microstructures fabricated by colloidal lithography. The nanocavity microstructure of the substrate can well protect the inner gold nanoparticles during wipe sampling. The prepared flexible substrate was able to detect 4-aminothiophenol (4-ATP) with a concentration down to 1 fM. Furthermore, the substrate was used to detect 6-BA residues on the surface of apples and bean sprouts through wipe sampling, which shows great potential in the field of rapid on-site detection, especially in the detection of pesticide residues on the surface of fruits and vegetables.
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Affiliation(s)
- Tianxun Gong
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Electronic Science and Engineering (National Exemplary School of Microelectronics), University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Haonan Li
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Electronic Science and Engineering (National Exemplary School of Microelectronics), University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Guilin Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Electronic Science and Engineering (National Exemplary School of Microelectronics), University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Fang Guan
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Electronic Science and Engineering (National Exemplary School of Microelectronics), University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Wen Huang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Electronic Science and Engineering (National Exemplary School of Microelectronics), University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Xiaosheng Zhang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Electronic Science and Engineering (National Exemplary School of Microelectronics), University of Electronic Science and Technology of China, Chengdu, People's Republic of China
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35
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Iancu SD, Cozan RG, Stefancu A, David M, Moisoiu T, Moroz-Dubenco C, Bajcsi A, Chira C, Andreica A, Leopold LF, Eniu D, Staicu A, Goidescu I, Socaciu C, Eniu DT, Diosan L, Leopold N. SERS liquid biopsy in breast cancer. What can we learn from SERS on serum and urine? SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 273:120992. [PMID: 35220052 DOI: 10.1016/j.saa.2022.120992] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/21/2022] [Accepted: 02/01/2022] [Indexed: 06/14/2023]
Abstract
SERS analysis of biofluids, coupled with classification algorithms, has recently emerged as a candidate for point-of-care medical diagnosis. Nonetheless, despite the impressive results reported in the literature, there are still gaps in our knowledge of the biochemical information provided by the SERS analysis of biofluids. Therefore, by a critical assignment of the SERS bands, our work aims to provide a systematic analysis of the molecular information that can be achieved from the SERS analysis of serum and urine obtained from breast cancer patients and controls. Further, we compared the relative performance of five different machine learning algorithms for breast cancer and control samples classification based on the serum and urine SERS datasets, and found comparable classification accuracies in the range of 61-89%. This result is not surprising since both biofluids show striking similarities in their SERS spectra providing similar metabolic information, related to purine metabolites. Lastly, by carefully comparing the two datasets (i.e., serum and urine) we show that it is possible to link the misclassified samples to specific metabolic imbalances, such as carotenoid levels, or variations in the creatinine concentration.
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Affiliation(s)
- Stefania D Iancu
- Faculty of Physics, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania
| | - Ramona G Cozan
- Faculty of Physics, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania
| | - Andrei Stefancu
- Faculty of Physics, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania
| | - Maria David
- Faculty of Chemistry and Chemical Engineering, Babeș-Bolyai University, 400028 Cluj-Napoca, Romania
| | - Tudor Moisoiu
- Clinical Institute of Urology and Renal Transplant, 400006 Cluj-Napoca, Romania; Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania; Biomed Data Analytics SRL, 400696 Cluj-Napoca, Romania
| | - Cristiana Moroz-Dubenco
- Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania
| | - Adel Bajcsi
- Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania
| | - Camelia Chira
- Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania; Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania
| | - Anca Andreica
- Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania
| | - Loredana F Leopold
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine, 400372 Cluj-Napoca, Romania
| | - Daniela Eniu
- Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania
| | - Adelina Staicu
- Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania
| | - Iulian Goidescu
- Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania
| | - Carmen Socaciu
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine, 400372 Cluj-Napoca, Romania; BIODIATECH Research Centre for Applied Biotechnology, SC Proplanta, 400478 Cluj-Napoca, Romania
| | - Dan T Eniu
- Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania; Department of Surgical and Gynecological Oncology, Ion Chiricuta Clinical Cancer Center, 400015 Cluj-Napoca, Romania
| | - Laura Diosan
- Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania.
| | - Nicolae Leopold
- Faculty of Physics, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania; Biomed Data Analytics SRL, 400696 Cluj-Napoca, Romania.
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Gao S, Lin Y, Zhao X, Gao J, Xie S, Gong W, Yu Y, Lin J. Label-free surface enhanced Raman spectroscopy analysis of blood serum via coffee ring effect for accurate diagnosis of cancers. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 267:120605. [PMID: 34802933 DOI: 10.1016/j.saa.2021.120605] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 11/04/2021] [Accepted: 11/07/2021] [Indexed: 05/20/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) is considered as an ultrasensitive, non-invasive as well as rapid detection technology for cancer diagnosis. In this study, we developed a novel blood serum analysis strategy using coffee ring effect-assisted label-free SERS for different types of cancer screening. Additionally, the pretreated Ag nanoparticles (Ag NPs) were mixed with the serum from liver cancer patients (n = 40), prostate cancer patients (n = 32) and healthy volunteers (n = 30) for SERS measurement. The droplets of Ag NPs-serum mixture formed the coffee ring on the peripheral after air-drying, and thus extremely enhancing Raman signal and ensuring the stability and reliability of SERS detection. Partial least square (PLS) and support vector machine (SVM) algorithms were utilized to establish the diagnosis model for SERS spectra data classifying, yielding the high diagnostic accuracy of 98.04% for normal group and two types of cancers simultaneously distinguishing. More importantly, for the unknown testing set, an ideal diagnostic accuracy of 100% could be achieved by PLS-SVM algorithm for differentiating cancers from the normal group. The results from this exploratory work demonstrate that serum SERS detection combined with PLS-SVM diagnostic algorithm and coffee ring effect has great potential for the noninvasive and label-free detection of cancer.
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Affiliation(s)
- Siqi Gao
- Key Laboratory of Optoelectronic Devices and Systems of Guangdong Province and the Ministry of Education, College of Physics and Optoelectronic Engineering, Shenzhen University, China; Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Yamin Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Xin Zhao
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Jiamin Gao
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Shusen Xie
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Wei Gong
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Yun Yu
- College of Integrated Traditional Chinese and Western Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
| | - Juqiang Lin
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, Fujian, China; Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China.
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Chen X, Li X, Yang H, Xie J, Liu A. Diagnosis and staging of diffuse large B-cell lymphoma using label-free surface-enhanced Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 267:120571. [PMID: 34752994 DOI: 10.1016/j.saa.2021.120571] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/24/2021] [Accepted: 10/28/2021] [Indexed: 05/27/2023]
Abstract
Non-invasive diagnosis and staging of diffuse large B-cell lymphoma (DLBCL) were achieved using label-free surface-enhanced Raman spectroscopy (SERS). SERS spectra were measured for serum samples of DLBCL patients at different progressive stages and healthy controls (HCs), using colloidal silver nano-particles (AgNPs) as the substrate. Differences in the spectral intensities of Raman peaks were observed between the DLBCL and HC groups, and a close correlation between the spectral intensities of Raman peaks with the progressive stages of the cancer was obtained, demonstrating the possibility of diagnosis and staging of the disease using the serum SERS spectra. Multivariate analysis methods, including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM) classifier, and k-nearest neighbors (kNN) classifier, were used to build the diagnosis and staging models for DLBCL. Leave-one-out cross-validation was used to evaluate the performances of the models. The kNN model achieved the best performances for both diagnosis and staging of DLBCL: for the diagnosis analysis, the accuracy, sensitivity, and specificity were 87.3%, 0.921, and 0.809, respectively; for the staging analysis between the early (Stage I & II) and the late (Stage III & IV) stages, the accuracy was 90.6%, and the sensitivity values for the early and the late stages were 0.947 and 0.800, respectively. The label-free serum SERS in combination with multivariate analysis could serve as a potential technique for non-invasive diagnosis and staging of DLBCL.
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Affiliation(s)
- Xue Chen
- Department of Hematology, Harbin Medical University Cancer Hospital, 150 Haping Road, 150081 Harbin, China.
| | - Xiaohui Li
- Institute of Opto-electronics, Harbin Institute of Technology, 2 Yikuang Street, 150080 Harbin, China; National Key Laboratory on Tunable Laser, Harbin Institute of Technology, 2 Yikuang Street, 150080 Harbin, China.
| | - Hao Yang
- Institute of Opto-electronics, Harbin Institute of Technology, 2 Yikuang Street, 150080 Harbin, China; National Key Laboratory on Tunable Laser, Harbin Institute of Technology, 2 Yikuang Street, 150080 Harbin, China
| | - Jinmei Xie
- Institute of Opto-electronics, Harbin Institute of Technology, 2 Yikuang Street, 150080 Harbin, China; National Key Laboratory on Tunable Laser, Harbin Institute of Technology, 2 Yikuang Street, 150080 Harbin, China
| | - Aichun Liu
- Department of Hematology, Harbin Medical University Cancer Hospital, 150 Haping Road, 150081 Harbin, China
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38
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Mi Y, Yan Y, Wang M, Yang L, He J, Jiang Y. Cascaded microsphere-coupled surface-enhanced Raman spectroscopy (CMS-SERS) for ultrasensitive trace-detection. NANOPHOTONICS (BERLIN, GERMANY) 2022; 11:559-570. [PMID: 39633797 PMCID: PMC11501333 DOI: 10.1515/nanoph-2021-0620] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/08/2021] [Accepted: 12/17/2021] [Indexed: 12/07/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has been widely investigated and employed as a powerful optical analytical technique providing fingerprint vibrational information of molecules with high sensitivity and resolution. In addition to metallic nanostructure, dielectric micro-/nano-structures with extraordinary optical manipulation properties have demonstrated capability in enhanced Raman scattering with ultralow energy losses. Here we report a facile cascaded structure composed of a large microsphere (LMS) and a small microsphere array with Ag nanoparticles as a novel hybrid SERS substrate, for the first time. The cascaded microsphere-coupled SERS substrate provides a platform to increase the molecular concentration, boost the intensity of localized excitation light, and direct the far-field emission, for giant Raman enhancement. It demonstrates the maximum enhancement factor of Raman intensity greater than 108 for the limit of detection down to 10-11 M of 4-nitrothiphenol molecules in aqueous solution. The present work inspires a novel strategy to fabricate cascaded dielectric/metallic micro-/nano-structures superior to traditional SERS substrates towards practical applications in cost-effective and ultrahigh-sensitive trace-detection.
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Affiliation(s)
- Yanlin Mi
- Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing100124, China
| | - Yinzhou Yan
- Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing100124, China
- Key Laboratory of Trans-scale Laser Manufacturing Technology (Ministry of Education), Beijing100124, China
- Beijing Engineering Research Center of Laser Technology, Beijing University of Technology, Beijing100124, China
| | - Mengyuan Wang
- Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing100124, China
| | - Lixue Yang
- Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing100124, China
| | - Jing He
- Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing100124, China
| | - Yijian Jiang
- Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing100124, China
- Key Laboratory of Trans-scale Laser Manufacturing Technology (Ministry of Education), Beijing100124, China
- Beijing Engineering Research Center of Laser Technology, Beijing University of Technology, Beijing100124, China
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39
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Lin Y, Gao J, Tang S, Zhao X, Zheng M, Gong W, Xie S, Gao S, Yu Y, Lin J. Label-free diagnosis of breast cancer based on serum protein purification assisted surface-enhanced Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 263:120234. [PMID: 34343842 DOI: 10.1016/j.saa.2021.120234] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/20/2021] [Accepted: 07/25/2021] [Indexed: 05/20/2023]
Abstract
Serum protein is generally used to assess the severity of disease, as well as cancer progression and prognosis. Herein, a simple and rapid serum proteins analysis method combined with surface-enhanced Raman spectroscopy (SERS) technology was applied for breast cancer detection. The cellulose acetate membrane (CA) was employed to extract human serum proteins from 30 breast cancer patients and 45 healthy volunteers and then extracted proteins were mixed with silver nanoparticles for SERS measurement. Additionally, we also mainly assessed the use of different ratios of proteins-silver nanoparticles (Ag NPs) mixture to generate maximum SERS signal for clinical samples detection. Two multivariate statistical analyses, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-support vector machines (PLS-SVM) were used to analyze the obtained serum protein SERS spectra and establish the diagnostic model. The results demonstrate that the PLS-SVM model provides superior performance in the classification of breast cancer diagnosis compared with PCA-LDA. This exploratory work demonstrates that the label-free SERS analysis technique combined with CA membrane purified serum proteins has great potential for breast cancer diagnosis.
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Affiliation(s)
- Yamin Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Jiamin Gao
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Shuzhen Tang
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Xin Zhao
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Mengmeng Zheng
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Wei Gong
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Shusen Xie
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China
| | - Siqi Gao
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China.
| | - Yun Yu
- College of Integrated Traditional Chinese and Western Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
| | - Juqiang Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, Fujian, China; School of opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, Fujian, China.
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40
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Becker L, Janssen N, Layland SL, Mürdter TE, Nies AT, Schenke-Layland K, Marzi J. Raman Imaging and Fluorescence Lifetime Imaging Microscopy for Diagnosis of Cancer State and Metabolic Monitoring. Cancers (Basel) 2021; 13:cancers13225682. [PMID: 34830837 PMCID: PMC8616063 DOI: 10.3390/cancers13225682] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/05/2021] [Accepted: 11/10/2021] [Indexed: 02/08/2023] Open
Abstract
Hurdles for effective tumor therapy are delayed detection and limited effectiveness of systemic drug therapies by patient-specific multidrug resistance. Non-invasive bioimaging tools such as fluorescence lifetime imaging microscopy (FLIM) and Raman-microspectroscopy have evolved over the last decade, providing the potential to be translated into clinics for early-stage disease detection, in vitro drug screening, and drug efficacy studies in personalized medicine. Accessing tissue- and cell-specific spectral signatures, Raman microspectroscopy has emerged as a diagnostic tool to identify precancerous lesions, cancer stages, or cell malignancy. In vivo Raman measurements have been enabled by recent technological advances in Raman endoscopy and signal-enhancing setups such as coherent anti-stokes Raman spectroscopy or surface-enhanced Raman spectroscopy. FLIM enables in situ investigations of metabolic processes such as glycolysis, oxidative stress, or mitochondrial activity by using the autofluorescence of co-enzymes NADH and FAD, which are associated with intrinsic proteins as a direct measure of tumor metabolism, cell death stages and drug efficacy. The combination of non-invasive and molecular-sensitive in situ techniques and advanced 3D tumor models such as patient-derived organoids or microtumors allows the recapitulation of tumor physiology and metabolism in vitro and facilitates the screening for patient-individualized drug treatment options.
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Affiliation(s)
- Lucas Becker
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
| | - Nicole Janssen
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tübingen, 72076 Tübingen, Germany
| | - Shannon L Layland
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
| | - Thomas E Mürdter
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tübingen, 72076 Tübingen, Germany
| | - Anne T Nies
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tübingen, 72076 Tübingen, Germany
| | - Katja Schenke-Layland
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
- Cardiovascular Research Laboratories, Department of Medicine/Cardiology, David Geffen School of Medicine, UCLA, Los Angeles, CA 90073, USA
| | - Julia Marzi
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
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Abstract
Raman spectroscopy is a very powerful tool for material analysis, allowing for exploring the properties of a wide range of different materials. Since its discovery, Raman spectroscopy has been used to investigate several features of materials such carbonaceous and inorganic properties, providing useful information on their phases, functions, and defects. Furthermore, techniques such as surface and tip enhanced Raman spectroscopy have extended the field of application of Raman analysis to biological and analytical fields. Additionally, the robustness and versatility of Raman instrumentations represent a promising solution for performing on-field analysis for a wide range of materials. Recognizing the many hot applications of Raman spectroscopy, we herein overview the main and more recent applications for the investigation of a wide range of materials, such as carbonaceous and biological materials. We also provide a brief but exhaustive theoretical background of Raman spectroscopy, also providing deep insight into the analytical achievements.
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Noothalapati H, Iwasaki K, Yamamoto T. Non-invasive diagnosis of colorectal cancer by Raman spectroscopy: Recent developments in liquid biopsy and endoscopy approaches. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 258:119818. [PMID: 33957445 DOI: 10.1016/j.saa.2021.119818] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/31/2021] [Accepted: 04/08/2021] [Indexed: 06/12/2023]
Abstract
Colorectal cancer (CRC) is the third most common cancer diagnosed globally and is also one of the leading causes of cancer deaths in both men and women. The progression of CRC is slow and is often contained in colon but the risk increases with age. Based on the high certainty that the net benefit of screening in an age group is substantial, screening for CRC is recommended beginning at the age of 50. Currently, most of the incidence is concentrated in developed countries but the rate is increasing rapidly in developing geographies. Detecting CRC at an early stage is critical to reduce morbidity and mortality. Colonoscopy is the most preferred screening method but not very widely implemented due to practical considerations such as cost involved, lack of personnel and facility. To address these concerns, Raman spectroscopy (RS) has been suggested as a viable alternative due to its potential as a rapid non-invasive diagnostic tool. Recently, several studies have been reported but many variations of RS applications in CRC exists and are not well understood by non-specialists. This review focuses particularly on developments of Raman based liquid biopsy and endoscopic studies in order to throw light on each of their significance and limitations. Necessary developments in the future to translate RS into a clinical tool for screening and diagnosis of CRC are also briefly presented.
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Affiliation(s)
- Hemanth Noothalapati
- Raman Project Center for Medical and Biological Applications, Shimane University, Matsue, Japan; Research Administration Office, Shimane University, Matsue, Japan; Faculty of Life and Environmental Sciences, Shimane University, Matsue, Japan.
| | - Keita Iwasaki
- The United Graduate School of Agricultural Sciences, Tottori University, Tottori, Japan
| | - Tatsuyuki Yamamoto
- Raman Project Center for Medical and Biological Applications, Shimane University, Matsue, Japan; Faculty of Life and Environmental Sciences, Shimane University, Matsue, Japan.
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Dong R, Wang J, Weng S, Yuan H, Yang L. Field determination of hazardous chemicals in public security by using a hand-held Raman spectrometer and a deep architecture-search network. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 258:119871. [PMID: 33957446 DOI: 10.1016/j.saa.2021.119871] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/08/2021] [Accepted: 04/21/2021] [Indexed: 06/12/2023]
Abstract
With the advanced development of miniaturization and integration of instruments, Raman spectroscopy (RS) has demonstrated its great significance because of its non-invasive property and fingerprint identification ability, and extended its applications in public security, especially for hazardous chemicals. However, the fast and accurate RS analysis of hazardous chemicals in field test by non-professionals is still challenging due to the lack of an effective and timely spectral-based chemical-discriminating solution. In this study, a platform was developed for the field determination of hazardous chemicals in public security by using a hand-held Raman spectrometer and a deep architecture-search network (DASN) incorporated into a cloud server. With the Raman spectra of 300 chemicals, DASN stands out with identification accuracy of 100% and outweighs other machine learning and deep learning methods. The network feature maps for the spectra of methamphetamine and ketamine focus on the main peaks of 1001 and 652 cm-1, which indicates the powerful feature extraction capability of DASN. Its receiver operating characteristic (ROC) curve completely encloses the other models, and the area under the curve is up to 1, implying excellent robustness. With the well-built platform combining RS, DASN, and cloud server, one test process including Raman measurement and identification can be performed in tens of seconds. Hence, the developed platform is simple, fast, accurate, and could be considered as a promising tool for hazardous chemical identification in public security on the scene.
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Affiliation(s)
- Ronglu Dong
- Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Jinghong Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China.
| | - Hecai Yuan
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Liangbao Yang
- Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
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Moisoiu V, Iancu SD, Stefancu A, Moisoiu T, Pardini B, Dragomir MP, Crisan N, Avram L, Crisan D, Andras I, Fodor D, Leopold LF, Socaciu C, Bálint Z, Tomuleasa C, Elec F, Leopold N. SERS liquid biopsy: An emerging tool for medical diagnosis. Colloids Surf B Biointerfaces 2021; 208:112064. [PMID: 34517219 DOI: 10.1016/j.colsurfb.2021.112064] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/09/2021] [Accepted: 08/20/2021] [Indexed: 02/02/2023]
Abstract
Surface-enhanced Raman scattering (SERS) is emerging as a novel strategy for biofluid analysis. In this review, we delineate four experimental SERS protocols that are frequently used for the profiling of biofluids: 1) liquid SERS for the detection of purine metabolites; 2) iodide-modified liquid SERS for the detection of proteins; 3) dried SERS for the detection of both purine metabolites and proteins; 4) resonant Raman for the detection of carotenoids. To explain the selectivity of each experimental SERS protocol, we introduce a heuristic model for the chemisorption of analytes mediated by adsorbed ions (adions) onto the SERS substrate. Next, we show that the promising results of SERS liquid biopsy stem from the fact that the concentration levels of purine metabolites, proteins and carotenoids are informative of the cellular turnover rate, inflammation, and oxidative stress, respectively. These processes are perturbed in virtually every disease, from cancer to autoimmune maladies. Finally, we review recent SERS liquid biopsy studies and discuss future steps that are required for translating SERS in the clinical setting.
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Affiliation(s)
- Vlad Moisoiu
- Faculty of Physics, Babeș-Bolyai University, 400084, Cluj-Napoca, Romania
| | - Stefania D Iancu
- Faculty of Physics, Babeș-Bolyai University, 400084, Cluj-Napoca, Romania
| | - Andrei Stefancu
- Faculty of Physics, Babeș-Bolyai University, 400084, Cluj-Napoca, Romania
| | - Tudor Moisoiu
- Clinical Institute of Urology and Renal Transplant, 400006, Cluj-Napoca, Romania; Biomed Data Analytics SRL, 400696, Cluj-Napoca, Romania; Department of Urology, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania
| | - Barbara Pardini
- Candiolo Cancer Institute, FPO-IRCCS, 10060, Candiolo, Italy; Italian Institute of Genomic Medicine (IIGM), 10060, Candiolo, Italy
| | - Mihnea P Dragomir
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany
| | - Nicolae Crisan
- Department of Urology, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania; Clinical Municipal Hospital, 400139, Cluj-Napoca, Romania
| | - Lucretia Avram
- Clinical Municipal Hospital, 400139, Cluj-Napoca, Romania; Department of Geriatrics, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania
| | - Dana Crisan
- Clinical Municipal Hospital, 400139, Cluj-Napoca, Romania; 5th Internal Medicine Department, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania
| | - Iulia Andras
- Department of Urology, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania; Clinical Municipal Hospital, 400139, Cluj-Napoca, Romania
| | - Daniela Fodor
- 2nd Internal Medicine Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400006, Cluj-Napoca, Romania
| | - Loredana F Leopold
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine, 400372, Cluj-Napoca, Romania
| | - Carmen Socaciu
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine, 400372, Cluj-Napoca, Romania; BIODIATECH Research Centre for Applied Biotechnology, SC Proplanta, 400478, Cluj-Napoca, Romania
| | - Zoltán Bálint
- Faculty of Physics, Babeș-Bolyai University, 400084, Cluj-Napoca, Romania
| | - Ciprian Tomuleasa
- Department of Hematology, Iuliu Hatieganu University of Medicine and Pharmacy, 400124, Cluj-Napoca, Romania; Department of Hematology, Ion Chiricuta Clinical Cancer Center, 400124, Cluj-Napoca, Romania; Medfuture Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400349, Cluj-Napoca, Romania
| | - Florin Elec
- Clinical Institute of Urology and Renal Transplant, 400006, Cluj-Napoca, Romania; Department of Urology, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania.
| | - Nicolae Leopold
- Faculty of Physics, Babeș-Bolyai University, 400084, Cluj-Napoca, Romania; Biomed Data Analytics SRL, 400696, Cluj-Napoca, Romania.
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Establishment of a reliable scheme for obtaining highly stable SERS signal of biological serum. Biosens Bioelectron 2021; 189:113315. [PMID: 34049082 DOI: 10.1016/j.bios.2021.113315] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/27/2021] [Accepted: 05/05/2021] [Indexed: 12/20/2022]
Abstract
As a rapid and non-destructive biological serum detection method, SERS technology was widely used in the screening and medical diagnosis of various diseases by combining the analysis of serum SERS spectrum and multivariate statistical algorithm. Because of the high complexity of serum components and the variability of SERS spectra, which often resulted in the phenomenon that the SERS spectrum of the same biological serum was significantly different due to the different test conditions. In this experiment, through the dilution treatment of the serum and the systematic test of the serum of all concentration gradients with lasers of wavelength of 785, 633 and 532 nm, the most suitable conditions for detecting the serum were investigated. The experimental results showed that only when the serum is diluted to low concentration (10 ppm), the SERS spectrum with high reproducibility and stability could be obtained, furthermore, the low concentration serum had weak tolerance to laser, and 532 nm laser was not suitable for serum detection. In this paper, a set of test scheme for obtaining highly stable serum SERS spectra was established by using high-performance gold nanoparticles (Au NPs) as the active substrate of SERS. Through comparative analysis of SERS spectrum of serum of normal people and cervical cancer, the reliability of the established low-concentration serum test program was verified, as well as its great potential advantages in disease screening and diagnosis.
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Siebe HS, Chen Q, Li X, Xu Y, Browne WR, Bell SEJ. Filter paper based SERS substrate for the direct detection of analytes in complex matrices. Analyst 2021; 146:1281-1288. [PMID: 33426548 DOI: 10.1039/d0an02103b] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Surface-enhanced Raman spectroscopy (SERS) is an emerging analytical technique for chemical analysis, which is favourable due to its combination of short measurement time, high sensitivity and molecular specificity. However, the application of SERS is still limited, largely because in real samples the analyte is often present in a complex matrix that contains micro/macro particles that block the probe laser, as well as molecular contaminants that compete for the enhancing surface. Here, we show a simple and scalable spray-deposition technique to fabricate SERS-active paper substrates which combine sample filtration and enhancement in a single material. Unlike previous spray-deposition methods, in which simple colloidal nanoparticles were sprayed onto solid surfaces, here the colloidal nanoparticles are mixed with hydroxyethyl cellulose (HEC) polymer before application. This leads to significantly improved uniformity in the distribution of enhancing particles as the film dries on the substrate surface. Importantly, the polymer matrix also protects the enhancing particles from air-oxidation during storage but releases them to provide SERS enhancement when the film is rehydrated. These SERS-paper substrates are highly active and a model analyte, crystal violet, was detected down to 4 ng in 10 μL of sample with less than 20% point-by-point signal deviation. The filter paper and HEC effectively filter out both interfering micro/macro particles and molecular (protein) contaminants, allowing the SERS-paper substrates to be used for SERS detection of thiram in mud and melamine in the presence of protein down to nanogram levels without sample pre-treatment or purification.
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Affiliation(s)
- Harmke S Siebe
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG, Groningen, The Netherlands
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Fornasaro S, Berton F, Stacchi C, Farina F, Esposito A, Sergo V, Di Lenarda R, Bonifacio A. Label-free analysis of gingival crevicular fluid (GCF) by surface enhanced Raman scattering (SERS). Analyst 2021; 146:1464-1471. [PMID: 33427826 DOI: 10.1039/d0an01997f] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Gingival crevicular fluid (GCF) is an interesting biofluid reflecting the physiological and pathological states of a single dental element. Due to this unique feature, in recent years, metabolomic analysis of GCF has gained attention as a biometric tool for the diagnosis and therapy of periodontal disease. Traditional methods are, however, too slow, cumbersome and expensive for a health-care routine. Surface enhanced Raman scattering (SERS) can offer rapid and label-free detailed molecular fingerprints that can be used for biofluid analysis. Here we report the first SERS characterization of GCF using an easy and quick sample preparation. The dominant features in the SERS spectrum of GCF are ascribed to very few metabolites, in particular to uric acid, hypoxanthine, glutathione and ergothioneine. Additionally, we succeeded in differentiating between the SERS signal of GCF collected from healthy volunteers and the one collected from patients with periodontal disease.
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Affiliation(s)
- Stefano Fornasaro
- Raman Spectroscopy Lab, Department of Engineering and Architecture, University of Trieste, 34100 Trieste, Italy.
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Ito H, Uragami N, Miyazaki T, Yang W, Issha K, Matsuo K, Kimura S, Arai Y, Tokunaga H, Okada S, Kawamura M, Yokoyama N, Kushima M, Inoue H, Fukagai T, Kamijo Y. Highly accurate colorectal cancer prediction model based on Raman spectroscopy using patient serum. World J Gastrointest Oncol 2020; 12:1311-1324. [PMID: 33250963 PMCID: PMC7667458 DOI: 10.4251/wjgo.v12.i11.1311] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/28/2020] [Accepted: 10/19/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is an important disease worldwide, accounting for the second highest number of cancer-related deaths and the third highest number of new cancer cases. The blood test is a simple and minimally invasive diagnostic test. However, there is currently no blood test that can accurately diagnose CRC.
AIM To develop a comprehensive, spontaneous, minimally invasive, label-free, blood-based CRC screening technique based on Raman spectroscopy.
METHODS We used Raman spectra recorded using 184 serum samples obtained from patients undergoing colonoscopies. Patients with malignant tumor histories as well as those with cancers in organs other than the large intestine were excluded. Consequently, the specific diseases of 184 patients were CRC (12), rectal neuroendocrine tumor (2), colorectal adenoma (68), colorectal hyperplastic polyp (18), and others (84). We used the 1064-nm wavelength laser for excitation. The power of the laser was set to 200 mW.
RESULTS Use of the recorded Raman spectra as training data allowed the construction of a boosted tree CRC prediction model based on machine learning. Therefore, the generalized R2 values for CRC, adenomas, hyperplastic polyps, and neuroendocrine tumors were 0.9982, 0.9630, 0.9962, and 0.9986, respectively.
CONCLUSION For machine learning using Raman spectral data, a highly accurate CRC prediction model with a high R2 value was constructed. We are currently planning studies to demonstrate the accuracy of this model with a large amount of additional data.
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Affiliation(s)
- Hiroaki Ito
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Naoyuki Uragami
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | | | | | - Kenji Issha
- Fuji Technical Research Inc., Yokohama 220-6215, Japan
| | - Kai Matsuo
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Satoshi Kimura
- Department of Laboratory Medicine and Central Clinical Laboratory, Showa University Northern Yokohama Hospital, Yokohama 224-8503, Japan
| | - Yuji Arai
- Department of Clinical Laboratory, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Hiromasa Tokunaga
- Department of Clinical Laboratory, Showa University Hospital, Tokyo 142-8555, Japan, BML Inc., Tokyo 151-0051, Japan
| | - Saiko Okada
- Department of Clinical Laboratory, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Machiko Kawamura
- Department of Hematology, Saitama Cancer Center, Inamachi, Saitama 362-0806, Japan
| | - Noboru Yokoyama
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Miki Kushima
- Department of Pathology, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Haruhiro Inoue
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Takashi Fukagai
- Department of Urology, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Yumi Kamijo
- Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
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Lin T, Song YL, Liao J, Liu F, Zeng TT. Applications of surface-enhanced Raman spectroscopy in detection fields. Nanomedicine (Lond) 2020; 15:2971-2989. [PMID: 33140686 DOI: 10.2217/nnm-2020-0361] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a Raman spectroscopy technique that has been widely used in food safety, environmental monitoring, medical diagnosis and treatment and drug monitoring because of its high selectivity, sensitivity, rapidness, simplicity and specificity in identifying molecular structures. This review introduces the detection mechanism of SERS and summarizes the most recent progress concerning the use of SERS for the detection and characterization of molecules, providing references for the later research of SERS in detection fields.
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Affiliation(s)
- Ting Lin
- Department of Hematology, Institute of Hematology, West China Hospital, Sichuan University, Chengdu, 610041, PR China
| | - Ya-Li Song
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, PR China
| | - Juan Liao
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, PR China
| | - Fang Liu
- Department of Laboratory Pathology, Xijing Hospital, Fourth Military Medical University, Xian, 710054, PR China
| | - Ting-Ting Zeng
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, PR China
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