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Cialla-May D, Bonifacio A, Bocklitz T, Markin A, Markina N, Fornasaro S, Dwivedi A, Dib T, Farnesi E, Liu C, Ghosh A, Popp J. Biomedical SERS - the current state and future trends. Chem Soc Rev 2024; 53:8957-8979. [PMID: 39109571 DOI: 10.1039/d4cs00090k] [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: 09/17/2024]
Abstract
Surface enhanced Raman spectroscopy (SERS) is meeting the requirements in biomedical science being a highly sensitive and specific analytical tool. By employing portable Raman systems in combination with customized sample pre-treatment, point-of-care-testing (POCT) becomes feasible. Powerful SERS-active sensing surfaces with high stability and modification layers if required are available for testing and application in complex biological matrices such as body fluids, cells or tissues. This review summarizes the current state in sample collection and pretreatment in SERS detection protocols, SERS detection schemes, i.e. direct and indirect SERS as well as targeted and non-targeted SERS, and SERS-active sensing surfaces. Moreover, the recent developments and advances of SERS in biomedical application scenarios, such as infectious diseases, cancer diagnostics and therapeutic drug monitoring is given, which enables the readers to identify the sample collection and preparation protocols, SERS substrates and detection strategies that are best-suited for their specific applications in biomedicine.
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Affiliation(s)
- Dana Cialla-May
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany.
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
| | - Alois Bonifacio
- Department of Engineering and Architecture, University of Trieste, Via Alfonso Valerio 6, 34127 Trieste (TS), Italy
| | - Thomas Bocklitz
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany.
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
- Faculty of Mathematics, Physics and Computer Science, University of Bayreuth (UBT), Nürnberger Straße 38, 95440 Bayreuth, Germany
| | - Alexey Markin
- Institute of Chemistry, Saratov State University, Astrakhanskaya Street 83, 410012 Saratov, Russia
| | - Natalia Markina
- Institute of Chemistry, Saratov State University, Astrakhanskaya Street 83, 410012 Saratov, Russia
| | - Stefano Fornasaro
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via Licio Giorgieri 1, 34127 Trieste (TS), Italy
| | - Aradhana Dwivedi
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany.
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
| | - Tony Dib
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany.
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
| | - Edoardo Farnesi
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
| | - Chen Liu
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany.
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
| | - Arna Ghosh
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany.
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
| | - Juergen Popp
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745 Jena, Germany.
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany
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Ye J, Bi X, Deng S, Wang X, Liu Z, Suo Q, Wu J, Chen H, Wang Y, Qian K, Shi R, Zhao J, Yang GY, Ye J, Tang Y. Hypoxanthine is a metabolic biomarker for inducing GSDME-dependent pyroptosis of endothelial cells during ischemic stroke. Theranostics 2024; 14:6071-6087. [PMID: 39346547 PMCID: PMC11426240 DOI: 10.7150/thno.100090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 09/06/2024] [Indexed: 10/01/2024] Open
Abstract
Rationale: Stroke induces metabolic changes in the body, and metabolites have become potential biomarkers for stroke. However, the specific metabolites involved in stroke and the mechanisms underlying brain injury during stroke remain unclear. Methods: Surface-enhanced Raman spectroscopy (SERS) and liquid chromatography-mass spectrometry (LC‒MS) analysis of clinical serum samples from 69 controls and 51 ischemic stroke patients who underwent reperfusion within 24 hours were performed to identify differentially abundant metabolites. Mice were subjected to transient middle cerebral artery occlusion (tMCAO) and then intravenously injected with hypoxanthine. The infarct area was evaluated via tetrazolium chloride (TTC) staining, and behavior tests were conducted. Blood-brain barrier (BBB) leakage was assessed by Evans blue and IgG staining. Human blood vessel organoids were used to investigate the mechanism of hypoxanthine-induced pyroptosis of endothelial cells. Results: SERS and LC‒MS revealed the metabolic profiles of serum from stroke patients and controls with high sensitivity, speed and accuracy. Hypoxanthine levels were significantly elevated in the acute stage of ischemic stroke in both patients and mice (p < 0.001 after Bonferroni correction). In addition, increasing hypoxanthine increased the infarct area and aggravated BBB leakage and neurobehavioral deficits in mice after ischemic stroke. Further mechanistic studies using endothelial cells, human blood vessel organoids, and stroke mice demonstrated that hypoxanthine-mediated gasdermin E (GSDME)-dependent pyroptosis of endothelial cells occurs through intracellular Ca2+ overload. Conclusion: Our study identified hypoxanthine as an important metabolite that induces vascular injury and BBB disruption in stroke through triggering GSDME-dependent pyroptosis of endothelial cells.
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Affiliation(s)
- Jing Ye
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Xinyuan Bi
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Shiyu Deng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Xianghui Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Ze Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Qian Suo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Jiao Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Haoran Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Yong Wang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai 200030, China
| | - Kun Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Rubing Shi
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Jing Zhao
- Department of Neurology, Minhang Hospital, Fudan University, Shanghai, 201102, China
| | - Guo-Yuan Yang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Jian Ye
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200127, China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Yaohui Tang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China
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Gobbato R, Fornasaro S, Sergo V, Bonifacio A. Direct comparison of different protocols to obtain surface enhanced Raman spectra of human serum. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 317:124390. [PMID: 38749203 DOI: 10.1016/j.saa.2024.124390] [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: 03/12/2024] [Revised: 04/21/2024] [Accepted: 04/29/2024] [Indexed: 05/31/2024]
Abstract
Label-free Surface Enhanced Raman Spectroscopy (SERS) is a rapid technique that has been extensively applied in clinical diagnosis and biomedicine for the analysis of biofluids. The purpose of this approach relies on the ability to detect specific "metabolic fingerprints" of complex biological samples, but the full potential of this technique in diagnostics is yet to be exploited, mainly because of the lack of common analytical protocols for sample preparation and analysis. Variation of experimental parameters, such as substrate type, laser wavelength and sample processing can greatly influence spectral patterns, making results from different research groups difficult to compare. This study aims at making a step toward a standardization of the protocols in the analysis of human serum samples with Ag nanoparticles, by directly comparing the SERS spectra obtained from five different methods in which parameters like laser power, nanoparticle concentration, incubation/deproteinization steps and type of substrate used vary. Two protocols are the most used in the literature, and the other three are "in-house" protocols proposed by our group; all of them are employed to analyze the same human serum sample. The experimental results show that all protocols yield spectra that share the same overall spectral pattern, conveying the same biochemical information, but they significantly differ in terms of overall spectral intensity, repeatability, and preparation steps of the sample. A Principal Component Analysis (PCA) was performed revealing that protocol 3 and protocol 1 have the least variability in the dataset, while protocol 2 and 4 are the least repeatable.
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Affiliation(s)
- Roberto Gobbato
- Raman Spectroscopy Laboratory, Department of Engineering and Architecture, University of Trieste, Via Valerio 6a, 34127 Trieste, TS, Italy.
| | - Stefano Fornasaro
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via Licio Giorgieri 1, 34127 Trieste, TS, Italy.
| | - Valter Sergo
- Raman Spectroscopy Laboratory, Department of Engineering and Architecture, University of Trieste, Via Valerio 6a, 34127 Trieste, TS, Italy.
| | - Alois Bonifacio
- Raman Spectroscopy Laboratory, Department of Engineering and Architecture, University of Trieste, Via Valerio 6a, 34127 Trieste, TS, Italy.
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Khosroshahi ME, Patel Y, Umashanker V. Targeted FT-NIR and SERS Detection of Breast Cancer HER-II Biomarkers in Blood Serum Using PCB-Based Plasmonic Active Nanostructured Thin Film Label-Free Immunosensor Immobilized with Directional GNU-Conjugated Antibody. SENSORS (BASEL, SWITZERLAND) 2024; 24:5378. [PMID: 39205071 PMCID: PMC11358943 DOI: 10.3390/s24165378] [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: 07/04/2024] [Revised: 08/14/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024]
Abstract
This work describes our recent PCB-based plasmonic nanostructured platform patent (US 11,828,747B2) for the detection of biomarkers in breast cancer serum (BCS). A 50 nm thin gold film (TGF) was immersion-coated on PCB (i.e., PCB-TGF) and immobilized covalently with gold nanourchin (GNU) via a 1,6-Hexanedithiol (HDT) linkage to produce a plasmonic activated nanostructured thin film (PANTF) platform. A label-free SERS immunosensor was fabricated by conjugating the platform with monoclonal HER-II antibodies (mAb) in a directional orientation via adipic acid dihydrazide (ADH) to provide higher accessibility to overexpressed HER-II biomarkers (i.e., 2+ (early), 3+ (locally advanced), and positive (meta) in BCS. An enhancement factor (EF) of 0.3 × 105 was achieved for PANTF using Rhodamine (R6G), and the morphology was studied by scanning electron microscopy (SEM) and atomic force microscope (AFM). UV-vis spectroscopy showed the peaks at 222, 231, and 213 nm corresponding to ADH, mAb, and HER-II biomarkers, respectively. The functionalization and conjugation were investigated by Fourier Transform Near Infrared (FT-NIR) where the most dominant overlapped spectra of 2+, 3+, and Pos correspond to OH-combination of carbohydrate, RNH2 1st overtone, and aromatic CH 1st overtone of mAb, respectively. SERS data were filtered using the filtfilt filter from scipy.signals, baseline corrected using the Improved Asymmetric Least Squares (isals) function from the pybaselines.Whittaker library. The results showed the common peaks at 867, 1312, 2894, 3026, and 3258 cm-1 corresponding to glycine, alanine ν (C-N-C) assigned to the symmetric C-N-C stretch mode; tryptophan and α helix; C-H antisymmetric and symmetric stretching; NH3+ in amino acids; and N-H stretch primary amide, respectively, with the intensity of Pos > 3+ > 2+. This trend is justifiable considering the stage of each sample. Principal Component Analysis (PCA) and Linear Discrimination Analysis (LDA) were employed for the statistical analysis of data.
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Affiliation(s)
- Mohammad E. Khosroshahi
- Nanobiophotonics & Biomedical Research Laboratory, M.I.S. Electronics Inc., Richmond Hill, ON L4B 1B4, Canada
- Institute for Advanced Non-Destructive and Non-Invasive Diagnostic Technologies (IANDIT), University of Toronto, Toronto, ON M5S 3G8, Canada
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada
| | - Yesha Patel
- Nanobiophotonics & Biomedical Research Laboratory, M.I.S. Electronics Inc., Richmond Hill, ON L4B 1B4, Canada
- Department of Biochemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Vithurshan Umashanker
- Nanobiophotonics & Biomedical Research Laboratory, M.I.S. Electronics Inc., Richmond Hill, ON L4B 1B4, Canada
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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Xiong C, Zhong Q, Yan D, Zhang B, Yao Y, Qian W, Zheng C, Mei X, Zhu S. Multi-branch attention Raman network and surface-enhanced Raman spectroscopy for the classification of neurological disorders. BIOMEDICAL OPTICS EXPRESS 2024; 15:3523-3540. [PMID: 38867772 PMCID: PMC11166416 DOI: 10.1364/boe.514196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/21/2024] [Accepted: 04/23/2024] [Indexed: 06/14/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS), a rapid, low-cost, non-invasive, ultrasensitive, and label-free technique, has been widely used in-situ and ex-situ biomedical diagnostics questions. However, analyzing and interpreting the untargeted spectral data remains challenging due to the difficulty of designing an optimal data pre-processing and modelling procedure. In this paper, we propose a Multi-branch Attention Raman Network (MBA-RamanNet) with a multi-branch attention module, including the convolutional block attention module (CBAM) branch, deep convolution module (DCM) branch, and branch weights, to extract more global and local information of characteristic Raman peaks which are more distinctive for classification tasks. CBAM, including channel and spatial aspects, is adopted to enhance the distinctive global information on Raman peaks. DCM is used to supplement local information of Raman peaks. Autonomously trained branch weights are applied to fuse the features of each branch, thereby optimizing the global and local information of the characteristic Raman peaks for identifying diseases. Extensive experiments are performed for two different neurological disorders classification tasks via untargeted serum SERS data. The results demonstrate that MBA-RamanNet outperforms commonly used CNN methods with an accuracy of 88.24% for the classification of healthy controls, mild cognitive impairment, Alzheimer's disease, and Non-Alzheimer's dementia; an accuracy of 90% for the classification of healthy controls, elderly depression, and elderly anxiety.
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Affiliation(s)
- Changchun Xiong
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
| | - Qingshan Zhong
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
| | - Denghui Yan
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
| | - Baihua Zhang
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
| | - Yudong Yao
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
| | - Wei Qian
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
| | - Chengying Zheng
- Department of Psychiatry, Ningbo Kangning Hospital and Affiliated Mental Health Centre, Ningbo Key Laboratory for Physical Diagnosis and Treatment of Mental and Psychological Disorders, Ningbo University, Ningbo 315211, China
| | - Xi Mei
- Department of Psychiatry, Ningbo Kangning Hospital and Affiliated Mental Health Centre, Ningbo Key Laboratory for Physical Diagnosis and Treatment of Mental and Psychological Disorders, Ningbo University, Ningbo 315211, China
| | - Shanshan Zhu
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology , Fujian Normal University, Fuzhou 350117, China
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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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Aswathy R, Sumathi S. The Evolving Landscape of Cervical Cancer: Breakthroughs in Screening and Therapy Through Integrating Biotechnology and Artificial Intelligence. Mol Biotechnol 2024:10.1007/s12033-024-01124-7. [PMID: 38573545 DOI: 10.1007/s12033-024-01124-7] [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: 12/19/2023] [Accepted: 02/15/2024] [Indexed: 04/05/2024]
Abstract
Cervical cancer (CC) continues to be a major worldwide health concern, profoundly impacting the lives of countless females worldwide. In low- and middle-income countries (LMICs), where CC prevalence is high, innovative, and cost-effective approaches for prevention, diagnosis, and treatment are vital. These approaches must ensure high response rates with minimal side effects to improve outcomes. The study aims to compile the latest developments in the field of CC, providing insights into the promising future of CC management along with the research gaps and challenges. Integrating biotechnology and artificial intelligence (AI) holds immense potential to revolutionize CC care, from MobileODT screening to precision medicine and innovative therapies. AI enhances healthcare accuracy and improves patient outcomes, especially in CC screening, where its use has increased over the years, showing promising results. Also, combining newly developed strategies with conventional treatment options presents an optimal approach to address the limitations associated with conventional methods. However, further clinical studies are essential for practically implementing these advancements in society. By leveraging these cutting-edge technologies and approaches, there is a substantial opportunity to reduce the global burden of this preventable malignancy, ultimately improving the lives of women in LMICs and beyond.
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Affiliation(s)
- Raghu Aswathy
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Bharathi Park Rd, Near Forest College Campus, Saibaba Colony, Coimbatore, Tamil Nadu, 641043, India
| | - Sundaravadivelu Sumathi
- Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Bharathi Park Rd, Near Forest College Campus, Saibaba Colony, Coimbatore, Tamil Nadu, 641043, India.
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Zheng X, Li J, Lü G, Li X, Lü X, Wu G, Xu L. Machine learning-assisted serum SERS strategy for rapid and non-invasive screening of early cystic echinococcosis. JOURNAL OF BIOPHOTONICS 2024; 17:e202300376. [PMID: 38163898 DOI: 10.1002/jbio.202300376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/11/2023] [Accepted: 12/17/2023] [Indexed: 01/03/2024]
Abstract
Early and accurate diagnosis of cystic echinococcosis (CE) with existing technologies is still challenging. Herein, we proposed a novel strategy based on the combination of label-free serum surface-enhanced Raman scattering (SERS) spectroscopy and machine learning for rapid and non-invasive diagnosis of early-stage CE. Specifically, by establishing early- and middle-stage mouse models, the corresponding CE-infected and normal control serum samples were collected, and silver nanoparticles (AgNPs) were utilized as the substrate to obtain SERS spectra. The early- and middle-stage discriminant models were developed using a support vector machine, with diagnostic accuracies of 91.7% and 95.7%, respectively. Furthermore, by analyzing the serum SERS spectra, some biomarkers that may be related to early CE were found, including purine metabolites and protein-related amide bands, which was consistent with other biochemical studies. Thus, our findings indicate that label-free serum SERS analysis is a potential early-stage CE detection method that is promising for clinical translation.
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Affiliation(s)
- Xiangxiang Zheng
- Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, China
| | - Jintian Li
- School of Public Healthy, Xinjiang Medical University, Urumqi, China
| | - Guodong Lü
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiaojing Li
- Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, China
| | - Xiaoyi Lü
- School of Software, Xinjiang University, Urumqi, China
| | - Guohua Wu
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Liang Xu
- Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, China
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Xu Q, Li T, Lin J, Wu X. Label-free screening of common urinary system tumors from blood plasma based on surface-enhanced Raman spectroscopy. Photodiagnosis Photodyn Ther 2024; 45:103900. [PMID: 38081568 DOI: 10.1016/j.pdpdt.2023.103900] [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: 10/08/2023] [Revised: 11/05/2023] [Accepted: 11/17/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND The incidence of common urinary system tumors has been rising rapidly in recent years, and most urinary system-derived tumors lack specific biomarkers. OBJECTIVES To explore the efficacy of surface-enhanced Raman spectroscopy (SERS) of blood plasma in screening three common urinary system tumors, including bladder cancer (BC), prostate cancer (PCa), and renal cell carcinoma (RCC). METHODS SERS plasma spectra from 125 plasma samples, including 25 PCa, 38 RCC, 24 BC patients, and 38 normal volunteers, were collected. All candidates had no other comorbidities. The Diagnosis was based on the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and the effectiveness of the diagnostic algorithms was verified using the Receiver Operating Characteristic Curve (ROC). RESULTS There are significant differences in SERS signals between PCa, BC, RCC, and normal plasma, especially at 639, 889, 1010, 1136, and 1205 cm-1. The PCA-LDA results show that high sensitivity (100 %), specificity (100 %), and accuracy (100 %) could be achieved for screening the PCa, RCC, BC group vs. the normal group, the PCa group vs. the BC and RCC group, respectively. The diagnostic sensitivity, specificity, and accuracy for the BC group vs. the RCC group are 79.2 %, 71.1 %, and 75.15 %, respectively. The integrated area under the ROC curve (AUC) is 1.0, 1.0, and 1.0 for the PCa, RCC, and BC group vs. the normal group, respectively. The AUC of the PCa group vs. the BC group and RCC group and the BC group vs. the RCC group are 1.0, 1.0, and 0.842, respectively. CONCLUSIONS Label-free plasma-SERS technology with PCA-LDA analysis could be a useful screening method for detecting urinary system tumors (PCa, RCC, and BC) in this exploratory study.
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Affiliation(s)
- Qingjiang Xu
- Provincial Clinical Medical College of Fujian Medical University, Fuzhou 350001, China; Department of Urology, Fujian Provincial Hospital, Fuzhou 350001, China
| | - Tao Li
- Provincial Clinical Medical College of Fujian Medical University, Fuzhou 350001, China; Department of Urology, Fujian Provincial Hospital, Fuzhou 350001, China
| | - Juqiang Lin
- MOE Key Laboratory of OptoElectronic Science and Technology for Medicine, and Affiliated Hospital, Fujian Normal University, Fuzhou, China; School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.
| | - Xiang Wu
- Provincial Clinical Medical College of Fujian Medical University, Fuzhou 350001, China; Department of Urology, Fujian Provincial Hospital, Fuzhou 350001, China.
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10
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Elsheikh S, Coles NP, Achadu OJ, Filippou PS, Khundakar AA. Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects. BIOSENSORS 2024; 14:33. [PMID: 38248410 PMCID: PMC10813143 DOI: 10.3390/bios14010033] [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: 11/21/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer's and Parkinson's diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research.
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Affiliation(s)
- Suzan Elsheikh
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
| | - Nathan P. Coles
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
| | - Ojodomo J. Achadu
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
- School of Health and Life Science, Teesside University, Campus Heart, Southfield Rd, Middlesbrough TS1 3BX, UK
| | - Panagiota S. Filippou
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
- School of Health and Life Science, Teesside University, Campus Heart, Southfield Rd, Middlesbrough TS1 3BX, UK
| | - Ahmad A. Khundakar
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
- School of Health and Life Science, Teesside University, Campus Heart, Southfield Rd, Middlesbrough TS1 3BX, UK
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
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11
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Demishkevich E, Zyubin A, Seteikin A, Samusev I, Park I, Hwangbo CK, Choi EH, Lee GJ. Synthesis Methods and Optical Sensing Applications of Plasmonic Metal Nanoparticles Made from Rhodium, Platinum, Gold, or Silver. MATERIALS (BASEL, SWITZERLAND) 2023; 16:3342. [PMID: 37176223 PMCID: PMC10180225 DOI: 10.3390/ma16093342] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/15/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023]
Abstract
The purpose of this paper is to provide an in-depth review of plasmonic metal nanoparticles made from rhodium, platinum, gold, or silver. We describe fundamental concepts, synthesis methods, and optical sensing applications of these nanoparticles. Plasmonic metal nanoparticles have received a lot of interest due to various applications, such as optical sensors, single-molecule detection, single-cell detection, pathogen detection, environmental contaminant monitoring, cancer diagnostics, biomedicine, and food and health safety monitoring. They provide a promising platform for highly sensitive detection of various analytes. Due to strongly localized optical fields in the hot-spot region near metal nanoparticles, they have the potential for plasmon-enhanced optical sensing applications, including metal-enhanced fluorescence (MEF), surface-enhanced Raman scattering (SERS), and biomedical imaging. We explain the plasmonic enhancement through electromagnetic theory and confirm it with finite-difference time-domain numerical simulations. Moreover, we examine how the localized surface plasmon resonance effects of gold and silver nanoparticles have been utilized for the detection and biosensing of various analytes. Specifically, we discuss the syntheses and applications of rhodium and platinum nanoparticles for the UV plasmonics such as UV-MEF and UV-SERS. Finally, we provide an overview of chemical, physical, and green methods for synthesizing these nanoparticles. We hope that this paper will promote further interest in the optical sensing applications of plasmonic metal nanoparticles in the UV and visible ranges.
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Affiliation(s)
- Elizaveta Demishkevich
- Research and Educational Center, Fundamental and Applied Photonics, Nanophotonics, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Andrey Zyubin
- Research and Educational Center, Fundamental and Applied Photonics, Nanophotonics, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Alexey Seteikin
- Research and Educational Center, Fundamental and Applied Photonics, Nanophotonics, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Department of Physics, Amur State University, 675021 Blagoveshchensk, Russia
| | - Ilia Samusev
- Research and Educational Center, Fundamental and Applied Photonics, Nanophotonics, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Inkyu Park
- Department of Physics, University of Seoul, Seoul 02504, Republic of Korea
| | - Chang Kwon Hwangbo
- Department of Physics, Inha University, Incheon 22212, Republic of Korea
| | - Eun Ha Choi
- Department of Electrical and Biological Physics, Kwangwoon University, Seoul 01897, Republic of Korea
- Plasma Bioscience Research Center, Kwangwoon University, Seoul 01897, Republic of Korea
| | - Geon Joon Lee
- Department of Electrical and Biological Physics, Kwangwoon University, Seoul 01897, Republic of Korea
- Plasma Bioscience Research Center, Kwangwoon University, Seoul 01897, Republic of Korea
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12
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Beeram R, Vepa KR, Soma VR. Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques. BIOSENSORS 2023; 13:328. [PMID: 36979540 PMCID: PMC10046859 DOI: 10.3390/bios13030328] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/20/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.
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Affiliation(s)
| | | | - Venugopal Rao Soma
- Advanced Centre of Research in High Energy Materials (ACRHEM), DRDO Industry Academia—Centre of Excellence (DIA-COE), University of Hyderabad, Hyderabad 500046, Telangana, India
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13
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Chisanga M, Williams H, Boudreau D, Pelletier JN, Trottier S, Masson JF. Label-Free SERS for Rapid Differentiation of SARS-CoV-2-Induced Serum Metabolic Profiles in Non-Hospitalized Adults. Anal Chem 2023; 95:3638-3646. [PMID: 36763490 PMCID: PMC9940618 DOI: 10.1021/acs.analchem.2c04514] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
COVID-19 represents a multi-system infectious disease with broad-spectrum manifestations, including changes in host metabolic processes connected to the disease pathogenesis. Understanding biochemical dysregulation patterns as a consequence of COVID-19 illness promises to be crucial for tracking disease course and clinical outcomes. Surface-enhanced Raman scattering (SERS) has attracted considerable interest in biomedical diagnostics for the sensitive detection of intrinsic profiles of unique fingerprints of serum biomolecules indicative of SARS-CoV-2 infection in a label-free format. Here, we applied label-free SERS and chemometrics for rapid interrogation of temporal metabolic dynamics in longitudinal sera of mildly infected non-hospitalized patients (n = 22), at 4 and 16 weeks post PCR-positive diagnosis, and compared them with negative controls (n = 8). SERS spectral markers revealed distinct metabolic profiles in patient sera that significantly deviated from the healthy metabolic state at the two sampling time intervals. Multivariate and univariate analyses of the spectral data identified abundance dynamics in amino acids, lipids, and protein vibrations as the key spectral features underlying the metabolic differences detected in convalescent samples and perhaps associated with patient recovery progression. A validation study performed using spontaneous Raman spectroscopy yielded spectral data results that corroborated SERS spectral findings and confirmed the detected disease-specific molecular phenotypes in clinical samples. Label-free SERS promises to be a valuable analytical technique for rapid screening of the metabolic phenotype induced by SARS-CoV-2 infection to allow appropriate healthcare intervention.
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Affiliation(s)
- Malama Chisanga
- Department
of Chemistry, Québec Centre for Advanced Materials (QCAM),
Regroupement Québécois sur les Matériaux de Pointe
(RQMP), and Centre Interdisciplinaire de Recherche sur le Cerveau
et l’Apprentissage (CIRCA), Université
de Montréal, CP 6128 Succ. Centre-Ville, Montreal, Québec H3C 3J7, Canada
| | - Hannah Williams
- Department
of Chemistry, Québec Centre for Advanced Materials (QCAM),
Regroupement Québécois sur les Matériaux de Pointe
(RQMP), and Centre Interdisciplinaire de Recherche sur le Cerveau
et l’Apprentissage (CIRCA), Université
de Montréal, CP 6128 Succ. Centre-Ville, Montreal, Québec H3C 3J7, Canada
| | - Denis Boudreau
- Department
of Chemistry and Centre for Optics, Photonics and Lasers (COPL), Université Laval, 1045, av. de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Joelle N. Pelletier
- Department
of Chemistry, Department of Biochemistry and PROTEO, Québec
Network for Research on Protein Function, Engineering and Applications, Université de Montréal, CP 6128 Succ. Centre-Ville, Montreal, Québec H3C 3J7, Canada
| | - Sylvie Trottier
- Centre
de Recherche du Centre Hospitalier Universitaire de Québec
and Département de Microbiologie-Infectiologie et d’Immunologie, Université Laval, 2705, boulevard Laurier, Québec, Québec G1V 4G2, Canada
| | - Jean-Francois Masson
- Department
of Chemistry, Québec Centre for Advanced Materials (QCAM),
Regroupement Québécois sur les Matériaux de Pointe
(RQMP), and Centre Interdisciplinaire de Recherche sur le Cerveau
et l’Apprentissage (CIRCA), Université
de Montréal, CP 6128 Succ. Centre-Ville, Montreal, Québec H3C 3J7, Canada,. Phone: +1-514-343-7342
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