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Usman M, Tang JW, Li F, Lai JX, Liu QH, Liu W, Wang L. Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications. J Adv Res 2023; 51:91-107. [PMID: 36549439 PMCID: PMC10491996 DOI: 10.1016/j.jare.2022.11.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/15/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The rapid and reliable detection of pathogenic bacteria at an early stage is a highly significant research field for public health. However, most traditional approaches for pathogen identification are time-consuming and labour-intensive, which may cause physicians making inappropriate treatment decisions based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of bacterial detection and identification. In particular, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate detection of bacterial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential. AIM OF REVIEW Recent advances in pathogen detection and diagnosis of bacterial infections were discussed with focuses on the development of the SERS approaches and its applications in complex clinical settings. KEY SCIENTIFIC CONCEPTS OF REVIEW The current review describes bacterial classification using surface enhanced Raman spectroscopy (SERS) for developing a rapid and more accurate method for the identification of bacterial pathogens in clinical diagnosis. The initial part of this review gives a brief overview of the mechanism of SERS technology and development of the SERS approach to detect bacterial pathogens in complex samples. The development of the label-based and label-free SERS strategies and several novel SERS-compatible technologies in clinical applications, as well as the analytical procedures and examples of chemometric methods for SERS, are introduced. The computational challenges of pre-processing spectra and the highlights of the limitations and perspectives of the SERS technique are also discussed.Taken together, this systematic review provides an overall summary of the SERS technique and its application potential for direct bacterial diagnosis in clinical samples such as blood, urine and sputum, etc.
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Affiliation(s)
- Muhammad Usman
- Department of Intelligent Medical Engineering, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Jia-Wei Tang
- Department of Intelligent Medical Engineering, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Fen Li
- Laboratory Medicine, Huai'an Fifth People's Hospital, Huai'an, Jiangsu Province, China
| | - Jin-Xin Lai
- Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, China
| | - Qing-Hua Liu
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macao, Macau SAR, China
| | - Wei Liu
- Department of Intelligent Medical Engineering, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu Province, China.
| | - Liang Wang
- Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong Province, China.
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2
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Ali S, Naveed A, Hussain I, Qazi J. Use of ATR-FTIR spectroscopy to differentiate between cirrhotic/non-cirrhotic HCV patients. Photodiagnosis Photodyn Ther 2023; 42:103529. [PMID: 37059162 DOI: 10.1016/j.pdpdt.2023.103529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/18/2023] [Accepted: 03/21/2023] [Indexed: 04/16/2023]
Abstract
BACKGROUND Conventional techniques to diagnose (HCV) and assess non-cirrhotic/cirrhotic status of the patient for appropriate treatment regime are expensive and invasive. Present available diagnostic tests are expensive as they include multiple screening steps. Therefore, there is a need of cost-effective, less time consuming and minimally invasive alternative diagnostic approaches can be used for effective screening. We propose that (ATR-FTIR) in conjunction with (PCA-LDA),(PCA-QDA) and (SVM) multivariate algorithms can be used as a sensitive tool for detection of HCV infection and to assess non-cirrhotic/cirrhotic status of patients. METHODS We used 105 sera samples, of which, 55 were from healthy and 50 were from HCV positive individuals. These 50 HCV positive patients were further classified into cirrhotic and non-cirrhotic categories using serum markers and imaging techniques. These samples were freeze dried prior to spectral acquisition then multivariate data classification algorithms were employed to classify these sample types. RESULTS PCA-LDA and SVM model computed the diagnostic accuracy of 100% for detection of HCV infection. To further classify the non-cirrhotic/cirrhotic status of a patient, diagnostic accuracy of 90.91% for PCA-QDA and 100% for SVM was observed. Internal and external validation for SVM based classifications observed 100% sensitivity and specificity. The confusion matrix generated by PCA-LDA model computed the validation and calibration accuracy showed 100% sensitivity and specificity, by using 2 PCs for HCV infected and healthy individuals. However, when the PCA QDA analysis was done to classify the non-cirrhotic sera samples from cirrhotic sera samples the diagnostic accuracy achieved was 90.91 % based on 7 PC's. SVM was also employed for classification and developed model showed the best results with 100% sensitivity and specificity when external validation was applied. CONCLUSIONS This study provides an initial insight that ATR-FTIR spectroscopy in conjugation with multivariate data classification tools holds a potentialnot onlytoeffectively diagnosis HCV infection but also to assess non-cirrhotic/cirrhotic status of patients.
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Affiliation(s)
- Salmann Ali
- Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad
| | - Ammara Naveed
- Department of gastroenterology and hepatology, Pakistan Kidney and Liver Institute, Lahore, Pakistan
| | - Irshad Hussain
- Department of Chemistry &Chemical Engineering, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS), DHA, Lahore Cantt 54792, Pakistan
| | - Javaria Qazi
- Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad.
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Alkhuder K. Raman Scattering-Based Optical Sensing Of Chronic Liver Diseases. Photodiagnosis Photodyn Ther 2023; 42:103505. [PMID: 36965755 DOI: 10.1016/j.pdpdt.2023.103505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/26/2023] [Accepted: 03/07/2023] [Indexed: 03/27/2023]
Abstract
Chronic liver diseases (CLDs) are a major public health problem. Despite the progress achieved in fighting against viral hepatitis, the emergence of non-alcoholic fatty liver disease might pose a serious challenge to the public's health in the coming decades. Medical management of CLDs represents a substantial burden on the public health infrastructures. The health care cost of these diseases is an additional burden that weighs heavily on the economies of developing countries. Effective management of CLDs requires the adoption of reliable and cost-effective screening and diagnosing methods to ensure early detection and accurate clinical assessment of these diseases. Vibrational spectroscopies have emerged as universal analytical methods with promising applications in various industrial and biomedical fields. These revolutionary analytical techniques rely on analyzing the interaction between a light beam and the test sample to generate a spectral fingerprint. This latter is defined by the analyte's chemical structure and the molecular vibrations of its functional groups. Raman spectroscopy and surface-enhanced Raman spectroscopy have been used in combination with various chemometric tests to diagnose a wide range of malignant, metabolic and infectious diseases. The aim of the current review is to cast light on the use of these optical sensing methods in the diagnosis of CLDs. The vast majority of research works that investigated the potential application of these spectroscopic techniques in screening and detecting CLDs were discussed here. The advantages and limitations of these modern analytical methods, as compared with the routine and gold standard diagnostic approaches, were also reviewed in details.
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4
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Zhang XD, Gu B, Usman M, Tang JW, Li ZK, Zhang XQ, Yan JW, Wang L. Recent Progress in the Diagnosis of Staphylococcus in Clinical Settings. Infect Dis (Lond) 2022. [DOI: 10.5772/intechopen.108524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Staphylococci are mainly found on the skin or in the nose. These bacteria are typically friendly, causing no harm to healthy individuals or resulting in only minor issues that can go away on their own. However, under certain circumstances, staphylococcal bacteria could invade the bloodstream, affect the entire body, and lead to life-threatening problems like septic shock. In addition, antibiotic-resistant Staphylococcus is another issue because of its difficulty in the treatment of infections, such as the notorious methicillin-resistant Staphylococcus aureus (MRSA) which is resistant to most of the currently known antibiotics. Therefore, rapid and accurate diagnosis of Staphylococcus and characterization of the antibiotic resistance profiles are essential in clinical settings for efficient prevention, control, and treatment of the bacteria. This chapter highlights recent advances in the diagnosis of Staphylococci in clinical settings with a focus on the advanced technique of surface-enhanced Raman spectroscopy (SERS), which will provide a framework for the real-world applications of novel diagnostic techniques in medical laboratories via bench-top instruments and at the bedside through point-of-care devices.
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5
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Saleem M, Ali S, Bilal M, Safdar K, Hassan M. Development of multivariate classification models for the diagnosis of dengue virus infection. Photodiagnosis Photodyn Ther 2022; 40:103136. [PMID: 36195260 DOI: 10.1016/j.pdpdt.2022.103136] [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: 08/20/2022] [Accepted: 09/26/2022] [Indexed: 12/14/2022]
Abstract
The dengue virus (DENV) infection is a worldwide cause of serious illness and death. Early and efficient prediction of disease may help in proper medical management to control disease. Keeping this in view, multivariate classification models by combining with Raman spectroscopy have been developed for the diagnosis of DENV infection in human blood sera. For study design, a statistical analysis is performed to select the sample size for training of models. Total 1240 Raman spectra have been acquired from 39 DENV infected and 23 healthy sera samples. Prior to model development, Raman spectra were examined using ANOVA test for significant differences present in the intensities of newly appeared Raman bands at 622, 645, 700, 746, 800, 814, 873, 890, 948, 1002, 1018, 1080, 1235, 1250, 1272, 1386, 1404, 1446, 1609 and 1645 cm-1. The significant differences and characteristic patterns of Raman bands induced by disease played decisive role and are exploited for development of multivariate model. Classification models are developed by utilizing principal component analysis (PCA) to extract discriminant features from multidimensional Raman spectral dataset and followed by support vector machines (SVM) with Polynomial of 5, RBF, and liner kernels. The proposed model for this study is built using 10-fold cross validation technique and evaluated on independent dataset to demonstrate its robustness. PCA-SVM (poly-5) model successfully yielded high diagnostic accuracy of 99.52%, sensitivity of 99.75%, specificity of 99.09% for classification of unknown suspected samples. For comparison, PCA discriminant analysis (PCA-DA), partial least squares regression (PLSR) are PLS-DA have been compared. It is found that PCA-SVM (poly-5) approach is more effective and robust compared to other state-of-the-art approaches and it can be used for clinical prediction of DENV infection in human blood sera.
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Affiliation(s)
- M Saleem
- National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan.
| | - Safdar Ali
- Directorate General National Repository, Islamabad, Pakistan.
| | - M Bilal
- Federal Medical College, Hanna Road, G-8/4, Islamabad, Pakistan
| | | | - Mehdi Hassan
- Air University, PAF Complex Sector E-9, Islamabad Pakistan
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Tariq M, Shoukat AB, Akbar S, Hameed S, Naqvi MZ, Azher A, Saad M, Rizwan M, Nadeem M, Javed A, Ali A, Aziz S. Epidemiology, risk factors, and pathogenesis associated with a superbug: A comprehensive literature review on hepatitis C virus infection. SAGE Open Med 2022; 10:20503121221105957. [PMID: 35795865 PMCID: PMC9252020 DOI: 10.1177/20503121221105957] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 05/20/2022] [Indexed: 12/20/2022] Open
Abstract
Viral hepatitis is a major public health concern. It is associated with life threatening conditions including liver cirrhosis and hepatocellular carcinoma. Hepatitis C virus infects around 71 million people annually, resultantly 700,000 deaths worldwide. Extrahepatic associated chronic hepatitis C virus accounts for one fourth of total healthcare load. This review included a total of 150 studies that revealed almost 19 million people are infected with hepatitis C virus and 240,000 new cases are being reported each year. This trend is continually rising in developing countries like Pakistan where intravenous drug abuse, street barbers, unsafe blood transfusions, use of unsterilized surgical instruments and recycled syringes plays a major role in virus transmission. Almost 123–180 million people are found to be hepatitis C virus infected or carrier that accounts for 2%–3% of world’s population. The general symptoms of hepatitis C virus infection include fatigue, jaundice, dark urine, anorexia, fever malaise, nausea and constipation varying on severity and chronicity of infection. More than 90% of hepatitis C virus infected patients are treated with direct-acting antiviral agents that prevent progression of liver disease, decreasing the elevation of hepatocellular carcinoma. Standardizing the healthcare techniques, minimizing the street practices, and screening for viral hepatitis on mass levels for early diagnosis and prompt treatment may help in decreasing the burden on already fragmented healthcare system. However, more advanced studies on larger populations focusing on mode of transmission and treatment protocols are warranted to understand and minimize the overall infection and death stigma among masses.
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Affiliation(s)
- Mehlayl Tariq
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Abu Bakar Shoukat
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Sedrah Akbar
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Samaia Hameed
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Muniba Zainab Naqvi
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Ayesha Azher
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Muhammad Saad
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan.,BreathMAT Lab, IAD, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad, Pakistan
| | - Muhammad Rizwan
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Muhammad Nadeem
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Anum Javed
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Asad Ali
- Institute of Microbiology and Molecular Genetics, University of the Punjab, Lahore, Punjab, Pakistan
| | - Shahid Aziz
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, Pakistan.,BreathMAT Lab, IAD, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad, Pakistan
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Hassan M, Ali S, Saleem M, Sanaullah M, Fahad LG, Kim JY, Alquhayz H, Tahir SF. Diagnosis of dengue virus infection using spectroscopic images and deep learning. PeerJ Comput Sci 2022; 8:e985. [PMID: 35721412 PMCID: PMC9202626 DOI: 10.7717/peerj-cs.985] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 04/26/2022] [Indexed: 06/15/2023]
Abstract
Dengue virus (DENV) infection is one of the major health issues and a substantial epidemic infectious human disease. More than two billion humans are living in dengue susceptible regions with annual infection mortality rate is about 5%-20%. At initial stages, it is difficult to differentiate dengue virus symptoms with other similar diseases. The main objective of this research is to diagnose dengue virus infection in human blood sera for better treatment and rehabilitation process. A novel and robust approach is proposed based on Raman spectroscopy and deep learning. In this regard, the ResNet101 deep learning model is modified by exploiting transfer learning (TL) concept on Raman spectroscopic data of human blood sera. Sample size was selected using standard statistical tests. The proposed model is evaluated on 2,000 Raman spectra images in which 1,200 are DENV-infected of human blood sera samples, and 800 are healthy ones. It offers 96.0% accuracy on testing data for DENV infection diagnosis. Moreover, the developed approach demonstrated minimum improvement of 6.0% and 7.0% in terms of AUC and Kappa index respectively over the other state-of-the-art techniques. The developed model offers superior performance to capture minute Raman spectral variations due to the better residual learning capability and generalization ability compared to others deep learning models. The developed model revealed that it might be applied for diagnosis of DENV infection to save precious human lives.
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Affiliation(s)
- Mehdi Hassan
- Department of Computer Science, Air University, Islamabad, Pakistan
- Department of ICT Convergence System Engineering, Chonnam National University, Gwangju, South Korea
| | - Safdar Ali
- Directorate of National Repository, Islamabad, Pakistan
| | - Muhammad Saleem
- Agriculture & Biophotonics Division, National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences (NILOP-C, PIEAS), Lehtrar Road, Nilore, Islamabad, Pakistan
| | - Muhammad Sanaullah
- Department of Computer Science, Bahaudian Zakaria University, Multan, Pakistan
| | - Labiba Gillani Fahad
- Department of Computer Science, National University of Computing and Emerging Sciences, FAST-NUCES, Islamabad, Pakistan
| | - Jin Young Kim
- Department of ICT Convergence System Engineering, Chonnam National University, Gwangju, South Korea
| | - Hani Alquhayz
- Department of Computer Science and Information, College of Science in Zulfi, Majmaah University, Al-Majmaah, Saudi Arabia
| | - Syed Fahad Tahir
- Department of Computer Science, Air University, Islamabad, Pakistan
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Ramoji A, Pahlow S, Pistiki A, Rueger J, Shaik TA, Shen H, Wichmann C, Krafft C, Popp J. Understanding Viruses and Viral Infections by Biophotonic Methods. TRANSLATIONAL BIOPHOTONICS 2022. [DOI: 10.1002/tbio.202100008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Anuradha Ramoji
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4 Jena Germany
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
- Center for Sepsis Control and Care Jena University Hospital, Am Klinikum 1, 07747 Jena Germany
| | - Susanne Pahlow
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4 Jena Germany
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
- InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena Germany
| | - Aikaterini Pistiki
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4 Jena Germany
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
| | - Jan Rueger
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
| | - Tanveer Ahmed Shaik
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
| | - Haodong Shen
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4 Jena Germany
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
- InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena Germany
| | - Christina Wichmann
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4 Jena Germany
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
- InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena Germany
| | - Christoph Krafft
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
| | - Juergen Popp
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4 Jena Germany
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
- Center for Sepsis Control and Care Jena University Hospital, Am Klinikum 1, 07747 Jena Germany
- InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena Germany
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9
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Rafiq S, Majeed MI, Nawaz H, Rashid N, Yaqoob U, Batool F, Bashir S, Akbar S, Abubakar M, Ahmad S, Ali S, Kashif M, Amin I. Surface-enhanced Raman spectroscopy for analysis of PCR products of viral RNA of hepatitis C patients. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 259:119908. [PMID: 33989976 DOI: 10.1016/j.saa.2021.119908] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/22/2021] [Accepted: 05/02/2021] [Indexed: 06/12/2023]
Abstract
In the current study, for a qualitative and quantitative study of Polymerase Chain Reaction (PCR) products of viral RNA of Hepatitis C virus (HCV) infection, surface-enhanced Raman spectroscopy (SERS) methodology has been developed. SERS was used to identify the spectral features associated with the PCR products of viral RNA of Hepatitis C in various samples of HCV-infected patients with predetermined viral loads. The measurements for SERS were performed on 30 samples of PCR products, which included three PCR products of RNA of healthy individuals, six negative controls, and twenty-one HCV positive samples of varying viral loads (VLs) using Silver nanoparticles (Ag NPs) as a SERS substrates. Additionally, on SERS spectral data, the multivariate data analysis methods including Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) were also carried out which help to illustrate the diagnostic capabilities of this method. The PLSR model is designed to predict HCV viral loads based on biochemical changes observed as SERS spectral features which can be associated directly with HCV RNA. Several SERS characteristic features are observed in the RNA of HCV which are not detected in the spectra of healthy RNA/controls. PCA is found helpful to differentiate the SERS spectral data sets of HCV RNA samples from healthy and negative controls. The PLSR model is found to be 99% accurate in predicting VLs of HCV RNA samples of unknown samples based on SERS spectral changes associated with the Hepatitis C development.
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Affiliation(s)
- Sidra Rafiq
- Department of Chemistry, University of Agriculture, Faisalabad 38040, Pakistan
| | | | - Haq Nawaz
- Department of Chemistry, University of Agriculture, Faisalabad 38040, Pakistan.
| | - Nosheen Rashid
- Department of Chemistry, University of Central Punjab, Faisalabad Campus, Pakistan
| | - Umer Yaqoob
- Department of Chemistry, University of Agriculture, Faisalabad 38040, Pakistan
| | - Fatima Batool
- Department of Chemistry, University of Agriculture, Faisalabad 38040, Pakistan
| | - Saba Bashir
- Department of Chemistry, University of Agriculture, Faisalabad 38040, Pakistan
| | - Saba Akbar
- Department of Chemistry, University of Agriculture, Faisalabad 38040, Pakistan
| | - Muhammad Abubakar
- Department of Chemistry, University of Agriculture, Faisalabad 38040, Pakistan
| | - Shamsheer Ahmad
- Department of Chemistry, University of Agriculture, Faisalabad 38040, Pakistan
| | - Saqib Ali
- Department of Chemistry, University of Agriculture, Faisalabad 38040, Pakistan
| | - Muhammad Kashif
- Department of Chemistry, University of Agriculture, Faisalabad 38040, Pakistan
| | - Imran Amin
- PCR Laboratory, PINUM Hospital, Faisalabad, Pakistan
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10
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Gogone ICVP, Ferreira GH, Gava D, Schaefer R, de Paula-Lopes FF, Rocha RDA, de Barros FRO. Applicability of Raman spectroscopy on porcine parvovirus and porcine circovirus type 2 detection. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 249:119336. [PMID: 33385972 DOI: 10.1016/j.saa.2020.119336] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 12/07/2020] [Accepted: 12/08/2020] [Indexed: 06/12/2023]
Abstract
Porcine parvovirus (PPV) is one of the major infectious causes of reproductive failure of swine. This disease is characterized by embryonic and fetal infection and death, responsible for important economic losses. PPV is also implicated as a trigger in the development of post-weaning multisystemic wasting syndrome (PMWS) caused by Porcine circovirus type 2 (PCV2). Their detection is PCR-based, which is quite sensitive and specific, but laborious, costly and time-demanding. Therefore, this study aimed to assess Raman spectroscopy (RS) as a diagnostic tool for PPV and PCV2 due to its label-free properties and unique ability to search and identify molecular fingerprints. Briefly, swine testis (ST) cells were inoculated with PPV or PCV2 and in vitro cultured (37 °C, 5% CO2) for four days. Fixed cells were then submitted to RS investigation using a 633 nm laser. A total of 225 spectra centered at 1300 cm-1 was obtained for each sample (5 spectra/cell; 15 cells/replicate; 3 replicates) of PPV-, PCV2-infected and uninfected (control) ST cells. Clear statistical discrimination between samples from both virus-infected cells was achieved with a Principal Component - Linear Discriminant Analysis (PCA-LDA) model, reaching sensitivity rates from 95.55% to 97.77%, respectively to PCV2- and PPV-infected cells. These results were then submitted to a Leave-One-Out (LOO) validation algorithm resulting in 99.97% of accuracy. Extensive band assignment was analyzed and compiled for better understanding of PPV and PCV2 virus-cell interaction, demonstrating that specific protein, lipids and DNA/RNA bands are the most important assignments related to discrimination of virus-infected from uninfected cells. In conclusion, these results represent promising bases for RS application on PCV2 and PPV detection for future diagnostic applications.
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Affiliation(s)
| | | | | | | | | | - Raquel de A Rocha
- Universidade Tecnológica Federal do Paraná, Dois Vizinhos, PR, Brazil
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11
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Zheng X, Wu G, Lv G, Yin L, Luo B, Lv X, Chen C. Combining derivative Raman with autofluorescence to improve the diagnosis performance of echinococcosis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 247:119083. [PMID: 33137629 DOI: 10.1016/j.saa.2020.119083] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 09/25/2020] [Accepted: 10/12/2020] [Indexed: 05/22/2023]
Abstract
Echinococcosis is a zoonotic parasitic disease transmitted by animals and distributed all over the world. There is no standardized and widely accepted treatment method, and early and accurate diagnosis is crucial for the prevention and cure of echinococcosis. Here, we explored the feasibility of using derivative Raman in combination with autofluorescence (AF) to improve the diagnosis performance of echinococcosis. The spectra of serum samples from patients with echinococcosis, as well as healthy volunteers, were recorded at 633 nm excitation. The normalized mean Raman spectra showed that there is a decrease in the relative amounts of β carotene and phenylalanine and an increase in the percentage of tryptophan, tyrosine, and glutamic acid contents in the serum of echinococcosis patients as compared to that of healthy subjects. Then, principal components analysis (PCA), combined with linear discriminant analysis (LDA), were adopted to distinguish echinococcosis patients from healthy volunteers. Based on the area under the ROC curve (AUC) value, the derivative Raman + AF spectral data set achieved the optimal results. The AUC value was improved by 0.08 for derivative Raman + AF (AUC = 0.98), compared to Raman alone. The results demonstrated that the fusion of derivative Raman and AF could effectively improve the performance of the diagnostic model, and this technique has great application potential in the clinical screening of echinococcosis.
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Affiliation(s)
- Xiangxiang Zheng
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Guohua Wu
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
| | - Guodong Lv
- 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 830000, China
| | - Longfei Yin
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Bin Luo
- State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xiaoyi Lv
- School of Software, Xinjiang University, Urumqi 830046, China; College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
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Zhao Y, Tian S, Yu L, Zhang Z, Zhang W. Analysis and Classification of Hepatitis Infections Using Raman Spectroscopy and Multiscale Convolutional Neural Networks. JOURNAL OF APPLIED SPECTROSCOPY 2021; 88:441-451. [PMID: 33972806 PMCID: PMC8099702 DOI: 10.1007/s10812-021-01192-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Hepatitis infections represent a major health concern worldwide. Numerous computer-aided approaches have been devised for the early detection of hepatitis. In this study, we propose a method for the analysis and classification of cases of hepatitis-B virus ( HBV), hepatitis-C virus (HCV), and healthy subjects using Raman spectroscopy and a multiscale convolutional neural network (MSCNN). In particular, serum samples of HBV-infected patients (435 cases), HCV-infected patients (374 cases), and healthy persons (499 cases) are analyzed via Raman spectroscopy. The differences between Raman peaks in the measured serum spectra indicate specific biomolecular differences among the three classes. The dimensionality of the spectral data is reduced through principal component analysis. Subsequently, features are extracted, and then feature normalization is applied. Next, the extracted features are used to train different classifiers, namely MSCNN, a single-scale convolutional neural network, and other traditional classifiers. Among these classifiers, the MSCNN model achieved the best outcomes with a precision of 98.89%, sensitivity of 97.44%, specificity of 94.54%, and accuracy of 94.92%. Overall, the results demonstrate that Raman spectral analysis and MSCNN can be effectively utilized for rapid screening of hepatitis B and C cases.
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Affiliation(s)
- Y. Zhao
- Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000 China
| | - Sh. Tian
- Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000 China
| | - L. Yu
- College of Software Engineering at Xin Jiang University, Urumqi, 830000 China
| | - Zh. Zhang
- The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000 China
| | - W. Zhang
- Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000 China
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