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Xu S, Wu XH, Wu L, Zhai JM, Li SJ, Kou Y, Peng W, Zheng QN, Tian JH, Zhang YJ, Li JF. Systematic Optimization of Universal Real-Time Hypersensitive Fast Detection Method for HBsAg in Serum Based on SERS. Anal Chem 2024; 96:6784-6793. [PMID: 38632870 DOI: 10.1021/acs.analchem.4c00668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Hepatitis B virus (HBV) is a major cause of liver cirrhosis and hepatocellular carcinoma, with HBV surface antigen (HBsAg) being a crucial marker in the clinical detection of HBV. Due to the significant harm and ease of transmission associated with HBV, HBsAg testing has become an essential part of preoperative assessments, particularly for emergency surgeries where healthcare professionals face exposure risks. Therefore, a timely and accurate detection method for HBsAg is urgently needed. In this study, a surface-enhanced Raman scattering (SERS) sensor with a sandwich structure was developed for HBsAg detection. Leveraging the ultrasensitive and rapid detection capabilities of SERS, this sensor enables quick detection results, significantly reducing waiting times. By systematically optimizing critical factors in the detection process, such as the composition and concentration of the incubation solution as well as the modification conditions and amount of probe particles, the sensitivity of the SERS immune assay system was improved. Ultimately, the sensor achieved a sensitivity of 0.00576 IU/mL within 12 min, surpassing the clinical requirement of 0.05 IU/mL by an order of magnitude. In clinical serum assay validation, the issue of false positives was effectively addressed by adding a blocker. The final sensor demonstrated 100% specificity and sensitivity at the threshold of 0.05 IU/mL. Therefore, this study not only designed an ultrasensitive SERS sensor for detecting HBsAg in actual clinical serum samples but also provided theoretical support for similar systems, filling the knowledge gap in existing literature.
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Affiliation(s)
- Shanshan Xu
- College of Materials, College of Energy, College of Physical Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Department of Clinical Laboratory, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361005, China
| | - Xiao-Hang Wu
- College of Materials, College of Energy, College of Physical Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Department of Clinical Laboratory, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361005, China
| | - Lin Wu
- College of Materials, College of Energy, College of Physical Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Department of Clinical Laboratory, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361005, China
| | - Jia-Min Zhai
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China
| | - Shi-Jun Li
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China
| | - Yichuan Kou
- College of Materials, College of Energy, College of Physical Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Department of Clinical Laboratory, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361005, China
| | - Wei Peng
- College of Materials, College of Energy, College of Physical Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Department of Clinical Laboratory, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361005, China
| | - Qing-Na Zheng
- College of Materials, College of Energy, College of Physical Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Department of Clinical Laboratory, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361005, China
| | - Jing-Hua Tian
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China
| | - Yue-Jiao Zhang
- College of Materials, College of Energy, College of Physical Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Department of Clinical Laboratory, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361005, China
| | - Jian-Feng Li
- College of Materials, College of Energy, College of Physical Science and Technology, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Department of Clinical Laboratory, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361005, China
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China
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Nawaz MZ, Nawaz H, Majeed MI, Rashid N, Javed MR, Naz S, Ali MZ, Sabir A, Sadaf N, Rafiq N, Shakeel M, Ali Z, Amin I. Comparison of surface-enhanced Raman spectral data sets of filtrate portions of serum samples of hepatitis B and Hepatitis C infected patients obtained by centrifugal filtration. Photodiagnosis Photodyn Ther 2023; 42:103532. [PMID: 36963645 DOI: 10.1016/j.pdpdt.2023.103532] [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: 12/09/2022] [Revised: 03/13/2023] [Accepted: 03/21/2023] [Indexed: 03/26/2023]
Abstract
BACKGROUND Surface-enhanced Raman spectroscopy (SERS) is an efficient technique which has been used for the analysis of filtrate portions of serum samples of Hepatitis B (HBV) and Hepatitis C (HCV) virus. OBJECTIVES The main reason for this study is to differentiate and compare HBV and HCV serum samples for disease diagnosis through SERS. Hepatitis B and hepatitis C disease biomarkers are more predictable in their centrifuged form as compared in their uncentrifuged form. For differentiation of SERS spectral data sets of hepatitis B, hepatitis C and healthy person principal component analysis (PCA) proved to be a helpful. Centrifugally filtered serum samples of hepatitis B and hepatitis C are clearly differentiated from centrifugally filtered serum samples of healthy individuals by using partial least square discriminant analysis (PLS-DA). METHODOLOGY Serum sample of HBV, HCV and healthy patients were centrifugally filtered to separate filtrate portion for studying biochemical changes in serum sample. The SERS of these samples is performed using silver nanoparticles as substrates to identify specific spectral features of both viral diseases which can be used for the diagnosis and differentiation of these diseases. The purpose of centrifugal filtration of the serum samples of HBV and HCV positive and control samples by using filter membranes of 50 KDa size is to eliminate the proteins bigger than 50 KDa so that their contribution in the SERS spectrum is removed and disease related smaller proteins may be observed. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are statistical tools which were used for the further validation of SERS. RESULTS HBV and HCV centrifugally filtered serum sample were compared and biomarkers including (uracil, phenylalanine, methionine, adenine, phosphodiester, proline, tyrosine, tryptophan, amino acid, thymine, fatty acid, nucleic acid, triglyceride, guanine and hydroxyproline) were identified through PCA and PLS-DA. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used as a multivariate data analysis tool for the diagnosis of the characteristic SERS spectral features associated with both types of viral diseases. For the classification and differentiation of centrifugally filtered HBV, HCV, and control serum samples, Principal component analysis is found helpful. Moreover, PLS-DA can classify these two distinct sets of SERS spectral data with 0.90 percent specificity, 0.85 percent precision, and 0.83 percent accuracy. CONCLUSIONS Surface enhanced Raman spectroscopy along with chemometric analysis like PCA and PLS-DA have been successfully differentiated HBV and HCV and healthy individuals' serum samples.
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Affiliation(s)
- Muhammad Zaman Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan.
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan.
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad (38000), Pakistan.
| | - Muhammad Rizwan Javed
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad (38000), Pakistan
| | - Saima Naz
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad (38000), Pakistan
| | - Muhammad Zeeshan Ali
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Amina Sabir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Nimra Sadaf
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Nighat Rafiq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Muhammad Shakeel
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Zain Ali
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad (38000), Pakistan
| | - Imran Amin
- PCR Laboratory, PINUM Hospital, Faisalabad (38000), Pakistan
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Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends. Anal Bioanal Chem 2023:10.1007/s00216-023-04620-y. [PMID: 36864313 PMCID: PMC9981450 DOI: 10.1007/s00216-023-04620-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [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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Akram M, Majeed MI, Nawaz H, Rashid N, Javed MR, Ali MZ, Raza A, Shakeel M, Hasan HMU, Ali Z, Ehsan U, Shahid M. Surface-enhanced Raman spectroscopy for characterization of filtrate portions of blood serum samples of typhoid patients. Photodiagnosis Photodyn Ther 2022; 40:103199. [PMID: 36371020 DOI: 10.1016/j.pdpdt.2022.103199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/02/2022] [Accepted: 11/08/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND Surface-enhanced Raman spectroscopy (SERS) is explored to design a rapid screening method for the characterization and diagnosis of typhoid fever by employing filtrate fractions of blood serum samples obtained by centrifugal filtration with 50 KDa filters. OBJECTIVES The purpose of this study, to separate the filtrate portions of blood serum samples in this way contain proteins smaller than 50 kDa and removal of bigger size protein which allows to acquire the SERS spectral features of smaller proteins more effectively which are probably associated with typhoid disease. Disease caused by Salmonella typhi diagnose more effectively by using surface-enhanced Raman spectroscopy (SERS) and multivariate data analysis tools. METHODS SERS was used as a diagnostic tool for typhoid fever by comparison between healthy and diseased samples. For this purpose, all the samples were analyzed by comparing their SERS spectral features. Over the spectral range of 400-1800cm-1, multivariate data analysis techniques such as Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) are applied to diagnose and differentiate different filtrate fractions of blood serum samples of patients of typhoid fever and healthy ones. RESULTS By comparing SERS spectra of healthy filtrate with that of filtrate of typhoid sample, the SERS spectral features associated with disease development are identified including PCA is found to be efficient for the qualitative differentiation of all of the samples analyzed. Moreover, PLS-DA successfully identified and classified healthy and typhoid positive blood serum samples with 97 % accuracy, 99 % specificity, 91 % sensitivity and 0.78 area under the receiver operating characteristic (AUROC) curve. CONCLUSIONS Surface enhanced Raman spectroscopy using silver nanoparticles SERS substrate, is found to be useful technique for the quick identification and evaluation of filtrate fractions of the blood serum samples of healthy and typhoid samples for disease diagnosis.
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Affiliation(s)
- Maria Akram
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad 38000, Pakistan.
| | - Muhammad Rizwan Javed
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad 38000, Pakistan
| | - Muhammad Zeeshan Ali
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Ali Raza
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Shakeel
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Hafiz Mahmood Ul Hasan
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Zain Ali
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Usama Ehsan
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Shahid
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
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Bari RZA, Nawaz H, Majeed MI, Rashid N, Tahir M, ul Hasan HM, Ishtiaq S, Sadaf N, Raza A, Zulfiqar A, Rehman AU, Shahid M. Characterization of Bacteria Inducing Chronic Sinusitis Using Surface-Enhanced Raman Spectroscopy (SERS) with Multivariate Data Analysis. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2130349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Rana Zaki Abdul Bari
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | | | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad, Pakistan
| | - Muhammad Tahir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | | | - Shazra Ishtiaq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Nimra Sadaf
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Ali Raza
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Anam Zulfiqar
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Aziz ur Rehman
- Department of Chemistry, Government College University Lahore, Lahore, Pakistan
| | - Muhammad Shahid
- Department of Biochemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
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Shakeel S, Nawaz H, Majeed MI, Rashid N, Javed MR, Tariq A, Majeed B, Sehar A, Murtaza S, Sadaf N, Rimsha G, Amin I. Surface-enhanced Raman spectroscopic analysis of the centrifugally filtered blood serum samples of the hepatitis C patients. Photodiagnosis Photodyn Ther 2022; 39:102949. [PMID: 35661826 DOI: 10.1016/j.pdpdt.2022.102949] [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: 03/11/2022] [Revised: 05/12/2022] [Accepted: 06/01/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Previously Raman spectroscopy technique is a use to analyze non-invasive disease related to body fluids. OBJECTIVES For the qualitative and quantitative analysis of HCV serum samples surface-enhanced Raman spectroscopy (SERS) based method is developed. METHOD Surface-enhanced Raman spectroscopy (SERS) technique is employed for analysis of filtrate portions of blood serum samples of hepatitis C virus (HCV) infected patients and healthy ones by using 50 kDa centrifugal filter device. The filtrate portions of the serum obtained in this way contain proteins smaller than 50 kDa and removal of bigger size protein which allows to acquire SERS spectral features of smaller proteins more effectively which are probably associated with Hepatitis C infection. Moreover, SERS spectral features of the filtrates of different level of viral load including low, medium and high viral loads are compared with SERS spectral features of the filtrate portions of healthy/control serum samples. SERS spectral data sets of different samples are further analyzed by using multivariate data analysis techniques such as principal component analysis (PCA) and partial least square regression (PLSR). Some SERS spectral features are solely observed in the filtrate portions of the serum samples of hepatitis C and their intensities are increased as the level of viral load increases and might be used for HCV diagnosis. RESULTS PCA was found helpful for differentiation of SERS spectral data sets of filtrate portions of the serum samples of hepatitis C and healthy persons. The PLSR model helped for the quantification of viral loads in the unknown serum samples with 99 % accuracy.
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Affiliation(s)
- Samra Shakeel
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Muhammad Irfan Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad 38000, Pakistan.
| | - Muhammad Rizwan Javed
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad 38000, Pakistan
| | - Ayesha Tariq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Beenish Majeed
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Aafia Sehar
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Sania Murtaza
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Nimra Sadaf
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Gull Rimsha
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan
| | - Imran Amin
- PCR Laboratory, PINUM Hospital, Faisalabad 38000, Pakistan
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