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Udensi J, Loskutova E, Loughman J, Byrne HJ. Raman spectroscopic analysis of human blood serum of glaucoma patients supplemented with macular pigment carotenoids. JOURNAL OF BIOPHOTONICS 2024:e202400060. [PMID: 38937976 DOI: 10.1002/jbio.202400060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/17/2024] [Accepted: 04/30/2024] [Indexed: 06/29/2024]
Abstract
As all major dietary carotenoids are contained in blood, it is a suitable substrate to evaluate their content, in vivo. Following 18-month supplementation of open-angle glaucoma patients with macula-pigment carotenoids (Lutein, Zeaxanthin and Meso-Zeaxanthin) in the European Nutrition in Glaucoma Management trial, Raman spectroscopic analysis of the carotenoid content of pre- and post-supplementation participant blood serum was carried out, to investigate the systemic impact of the supplementation regimen and explore a more direct way of quantifying this impact using routine blood tests. Using a 532 nm laser source for optimal response, a consistent increase in serum carotenoid concentration was observed in the supplemented serum, highest in patients with initial high baseline carotenoid content. A shift in the 1519 cm-1 carotenoid peak also revealed differences in the carotenoid structural profile of the two groups. The findings highlight the potential of Raman spectroscopy toquantify and differentiate carotenoids directly in blood serum.
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Affiliation(s)
- Joy Udensi
- FOCAS Research Institute, Technological University Dublin, Dublin, Ireland
- School of Physics and Clinical and Optometric Sciences, Technological University Dublin, Dublin, Ireland
- Centre for Eye Research, Ireland, Technological University Dublin, Dublin, Ireland
| | - Ekaterina Loskutova
- School of Physics and Clinical and Optometric Sciences, Technological University Dublin, Dublin, Ireland
- Centre for Eye Research, Ireland, Technological University Dublin, Dublin, Ireland
| | - James Loughman
- School of Physics and Clinical and Optometric Sciences, Technological University Dublin, Dublin, Ireland
- Centre for Eye Research, Ireland, Technological University Dublin, Dublin, Ireland
| | - Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, Dublin, Ireland
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2
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Rehan I, Ullah R, Khan S. Non-invasive Characterization of Glycosuria and Identification of Biomarkers in Diabetic Urine Using Fluorescence Spectroscopy and Machine Learning Algorithm. J Fluoresc 2024; 34:1391-1399. [PMID: 37535232 DOI: 10.1007/s10895-023-03366-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 07/24/2023] [Indexed: 08/04/2023]
Abstract
The current study presents a steadfast, simple, and efficient approach for the non-invasive determination of glycosuria of diabetes mellitus using fluorescence spectroscopy. A Xenon arc lamp emitting light in the range of 200-950 nm was used as an excitation source for recording the fluorescent spectra from the urine samples. A consistent fluorescence emission peak of glucose at 450 nm was found in all samples for an excitation wavelength of 370 nm. For confirmation and comparison, the fluorescence spectra of non-diabetic (healthy controls) were also acquired in the same spectral range. It was found that fluorescence emission intensity at 450 nm increases with increasing glucose concentration in urine. In addition, optimized synchronous fluorescence emission at 357 nm was used for simultaneously determining a potential diabetes biomarker, Tryptophan (Trp) in urine. It was also found that the level of tryptophan decreases with the increase in urinary glucose concentration. The quantitative estimation of urinary glucose can be demonstrated based on the intensity of emission light carried by fluorescence light. Moreover, the dissimilarities were further emphasized using the hierarchical cluster analysis (HCA) algorithm. HCA gives an obvious separation in terms of dendrogram between the two data sets based on characteristic peaks acquired from their fluorescence emission signatures. These results recommend that urinary glucose and tryptophan fluorescence emission can be used as potential biomarkers for the non-invasive analysis of diabetes.
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Affiliation(s)
- Imran Rehan
- National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences, Islamabad, 45650, Pakistan
- Department of Physics, Islamia College Peshawar, Peshawar, Khyber Pakhtunkhwa, 25120, Pakistan
| | - Rahat Ullah
- National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences, Islamabad, 45650, Pakistan.
| | - Saranjam Khan
- Department of Physics, Islamia College Peshawar, Peshawar, Khyber Pakhtunkhwa, 25120, Pakistan
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3
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Chu HO, Buchan E, Smith D, Goldberg Oppenheimer P. Development and application of an optimised Bayesian shrinkage prior for spectroscopic biomedical diagnostics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 245:108014. [PMID: 38246097 DOI: 10.1016/j.cmpb.2024.108014] [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: 11/08/2023] [Revised: 01/06/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024]
Abstract
BACKGROUND AND OBJECTIVE Classification of vibrational spectra is often challenging for biological substances containing similar molecular bonds, interfering with spectral outputs. To address this, various approaches are widely studied. However, whilst providing powerful estimations, these techniques are computationally extensive and frequently overfit the data. Shrinkage priors, which favour models with relatively few predictor variables, are often applied in Bayesian penalisation techniques to avoid overfitting. METHODS Using the logit-normal continuous analogue of the spike-and-slab (LN-CASS) as the shrinkage prior and modelling, we have established classification for accurate analysis, with the established system found to be faster than conventional least absolute shrinkage and selection operator, horseshoe or spike-and-slab. These were examined versus coefficient data based on a linear regression model and vibrational spectra produced via density functional theory calculations. Then applied to Raman spectra from saliva to classify the sample sex. RESULTS Subsequently applied to the acquired spectra from saliva, the evaluated models exhibited high accuracy (AUC>90 %) even when number of parameters was higher than the number of observations. Analyses of spectra for all Bayesian models yielded high-classification accuracy upon cross-validation. Further, for saliva sensing, LN-CASS was found to be the only classifier with 100 %-accuracy in predicting the output based on a leave-one-out cross validation. CONCLUSIONS With potential applications in aiding diagnosis from small spectroscopic datasets and are compatible with a range of spectroscopic data formats. As seen with the classification of IR and Raman spectra. These results are highly promising for emerging developments of spectroscopic platforms for biomedical diagnostic sensing systems.
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Affiliation(s)
- Hin On Chu
- School of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, UK
| | - Emma Buchan
- School of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, UK
| | - David Smith
- School of Mathematics, Watson Building, University of Birmingham, Birmingham B15 2TT, UK
| | - Pola Goldberg Oppenheimer
- School of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, UK; Healthcare Technologies Institute, Institute of Translational Medicine, Mindelsohn Way, Birmingham B15 2TH, UK.
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4
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Zhou L, Vestri A, Marchesano V, Rippa M, Sagnelli D, Picazio G, Fusco G, Han J, Zhou J, Petti L. The Label-Free Detection and Identification of SARS-CoV-2 Using Surface-Enhanced Raman Spectroscopy and Principal Component Analysis. BIOSENSORS 2023; 13:1014. [PMID: 38131774 PMCID: PMC10741931 DOI: 10.3390/bios13121014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/24/2023] [Accepted: 12/03/2023] [Indexed: 12/23/2023]
Abstract
The World Health Organization (WHO) declared in a May 2023 announcement that the COVID-19 illness is no longer categorized as a Public Health Emergency of International Concern (PHEIC); nevertheless, it is still considered an actual threat to world health, social welfare and economic stability. Consequently, the development of a convenient, reliable and affordable approach for detecting and identifying SARS-CoV-2 and its emerging new variants is crucial. The fingerprint and signal amplification characteristics of surface-enhanced Raman spectroscopy (SERS) could serve as an assay scheme for SARS-CoV-2. Here, we report a machine learning-based label-free SERS technique for the rapid and accurate detection and identification of SARS-CoV-2. The SERS spectra collected from samples of four types of coronaviruses on gold nanoparticles film, fabricated using a Langmuir-Blodgett self-assembly, can provide more spectroscopic signatures of the viruses and exhibit low limits of detection (<100 TCID50/mL or even <10 TCID50/mL). Furthermore, the key Raman bands of the SERS spectra were systematically captured by principal component analysis (PCA), which effectively distinguished SARS-CoV-2 and its variant from other coronaviruses. These results demonstrate that the combined use of SERS technology and PCA analysis has great potential for the rapid analysis and discrimination of multiple viruses and even newly emerging viruses without the need for a virus-specific probe.
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Affiliation(s)
- Lu Zhou
- Institute of Applied Sciences and Intelligent Systems of CNR, 80072 Pozzuoli, Italy; (L.Z.); (A.V.); (V.M.); (M.R.); (D.S.)
- Center for Terahertz Waves and College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China;
| | - Ambra Vestri
- Institute of Applied Sciences and Intelligent Systems of CNR, 80072 Pozzuoli, Italy; (L.Z.); (A.V.); (V.M.); (M.R.); (D.S.)
| | - Valentina Marchesano
- Institute of Applied Sciences and Intelligent Systems of CNR, 80072 Pozzuoli, Italy; (L.Z.); (A.V.); (V.M.); (M.R.); (D.S.)
| | - Massimo Rippa
- Institute of Applied Sciences and Intelligent Systems of CNR, 80072 Pozzuoli, Italy; (L.Z.); (A.V.); (V.M.); (M.R.); (D.S.)
| | - Domenico Sagnelli
- Institute of Applied Sciences and Intelligent Systems of CNR, 80072 Pozzuoli, Italy; (L.Z.); (A.V.); (V.M.); (M.R.); (D.S.)
| | - Gerardo Picazio
- Istituto Zooprofilattico Sperimentale del Mezzogiorno, 80055 Portici, Italy; (G.P.); (G.F.)
| | - Giovanna Fusco
- Istituto Zooprofilattico Sperimentale del Mezzogiorno, 80055 Portici, Italy; (G.P.); (G.F.)
| | - Jiaguang Han
- Center for Terahertz Waves and College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China;
| | - Jun Zhou
- Department of Microelectronic Science and Engineering, School of Physical Science and Technology, Ningbo University, Ningbo 315211, China
| | - Lucia Petti
- Institute of Applied Sciences and Intelligent Systems of CNR, 80072 Pozzuoli, Italy; (L.Z.); (A.V.); (V.M.); (M.R.); (D.S.)
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Yan S, Li J, Wu W. Artificial intelligence in breast cancer: application and future perspectives. J Cancer Res Clin Oncol 2023; 149:16179-16190. [PMID: 37656245 DOI: 10.1007/s00432-023-05337-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/24/2023] [Indexed: 09/02/2023]
Abstract
Breast cancer is one of the most common cancers and is one of the leading causes of cancer-related deaths in women worldwide. Early diagnosis and treatment are the key for a favorable prognosis. The application of artificial intelligence technology in the medical field is increasingly extensive, including image analysis, automated diagnosis, intelligent pharmaceutical system, personalized treatment and so on. AI-based breast cancer imaging, pathology and adjuvant therapy technology cannot only reduce the workload of clinicians, but also continuously improve the accuracy and sensitivity of breast cancer diagnosis and treatment. This paper reviews the application of AI in breast cancer, as well as looks ahead and poses challenges to the future development of AI for breast cancer detection and therapeutic, so as to provide ideas for future research.
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Affiliation(s)
- Shuixin Yan
- The Affiliated Lihuili Hospital of Ningbo University, Ningbo, 315000, Zhejiang, China
| | - Jiadi Li
- The Affiliated Lihuili Hospital of Ningbo University, Ningbo, 315000, Zhejiang, China
| | - Weizhu Wu
- The Affiliated Lihuili Hospital of Ningbo University, Ningbo, 315000, Zhejiang, China.
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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: 0] [Impact Index Per Article: 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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Acri G, Testagrossa B, Piccione G, Arfuso F, Giudice E, Giannetto C. Central and Peripheral Fatigue Evaluation during Physical Exercise in Athletic Horses by Means of Raman Spectroscopy. Animals (Basel) 2023; 13:2201. [PMID: 37443998 DOI: 10.3390/ani13132201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/23/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
The evaluation of the performance levels in athletic horses is of major importance to prevent sports injuries. Raman spectroscopy is an innovative technique that allows for a rapid evaluation of biomolecules in biological fluids. It also permits qualitative and quantitative sample analyses, which lead to the simultaneous determination of the components of the examined biological fluids. On the basis of this, the Raman spectroscopy technique was applied on serum samples collected from five Italian Saddle horses subjected to a standardized obstacle course preceded by a warm-up to evaluate the applicability of this technique for the assessment of central and peripheral fatigue in athletic horses. Blood samples were collected via jugular venipuncture in a vacutainer tube with a clot activator before exercise, immediately after exercise, and 30 min and 1 h after the end of the obstacle course. Observing the obtained Raman spectra, the major changes due to the experimental conditions appeared in the (1300-1360) cm-1 and (1385-1520) cm-1 bands. In the (1300-1360) cm-1 band, lipids and tryptophan were identified; in the (1385-1520) cm-1 band, leucine, glycine, isoleucine, lactic acid, tripeptide, adenosine, and beta carotene were identified. A significant effect of exercise was recorded on all the sub-bands. In particular, a change immediately after exercise versus before exercise was found. Moreover, the mean lactic concentration was positively correlated with the Raman area of the sub-band assigned to lactic acid. In this context, the application of Raman spectroscopy on blood serum samples represents a useful technique for secondary-structure protein identification to investigate the metabolic changes that occur in athletic horses during physical exercise.
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Affiliation(s)
- Giuseppe Acri
- Department of Biomedical, Dental and Morphological and Functional Imaging Sciences, University of Messina, Via Consolare Valeria, 98125 Messina, Italy
| | - Barbara Testagrossa
- Department of Biomedical, Dental and Morphological and Functional Imaging Sciences, University of Messina, Via Consolare Valeria, 98125 Messina, Italy
| | - Giuseppe Piccione
- Department of Veterinary Sciences, University of Messina, Via Palatucci n 13, 98168 Messina, Italy
| | - Francesca Arfuso
- Department of Veterinary Sciences, University of Messina, Via Palatucci n 13, 98168 Messina, Italy
| | - Elisabetta Giudice
- Department of Veterinary Sciences, University of Messina, Via Palatucci n 13, 98168 Messina, Italy
| | - Claudia Giannetto
- Department of Veterinary Sciences, University of Messina, Via Palatucci n 13, 98168 Messina, Italy
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8
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Zhang S, Qi Y, Tan SPH, Bi R, Olivo M. Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review. BIOSENSORS 2023; 13:bios13050557. [PMID: 37232918 DOI: 10.3390/bios13050557] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/12/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.
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Affiliation(s)
- Shuyan Zhang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
| | - Yi Qi
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
| | - Sonia Peng Hwee Tan
- Department of Biomedical Engineering, National University of Singapore (NUS), 4 Engineering Drive 3 Block 4, #04-08, Singapore 117583, Singapore
| | - Renzhe Bi
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
| | - Malini Olivo
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
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9
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Yang Z, Arakawa H. A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics. MARINE POLLUTION BULLETIN 2023; 190:114887. [PMID: 37023548 DOI: 10.1016/j.marpolbul.2023.114887] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/19/2023] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.
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Affiliation(s)
- Zijiang Yang
- Tokyo University of Marine Science and Technology, Konan 4-5-7, Minato-Ku, Tokyo 108-8477, Japan.
| | - Hisayuki Arakawa
- Tokyo University of Marine Science and Technology, Konan 4-5-7, Minato-Ku, Tokyo 108-8477, Japan.
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Udensi J, Loughman J, Loskutova E, Byrne HJ. Raman Spectroscopy of Carotenoid Compounds for Clinical Applications-A Review. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27249017. [PMID: 36558154 PMCID: PMC9784873 DOI: 10.3390/molecules27249017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
Carotenoid compounds are ubiquitous in nature, providing the characteristic colouring of many algae, bacteria, fruits and vegetables. They are a critical component of the human diet and play a key role in human nutrition, health and disease. Therefore, the clinical importance of qualitative and quantitative carotene content analysis is increasingly recognised. In this review, the structural and optical properties of carotenoid compounds are reviewed, differentiating between those of carotenes and xanthophylls. The strong non-resonant and resonant Raman spectroscopic signatures of carotenoids are described, and advances in the use of Raman spectroscopy to identify carotenoids in biological environments are reviewed. Focus is drawn to applications in nutritional analysis, optometry and serology, based on in vitro and ex vivo measurements in skin, retina and blood, and progress towards establishing the technique in a clinical environment, as well as challenges and future perspectives, are explored.
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Affiliation(s)
- Joy Udensi
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, D08 CKP1 Dublin, Ireland
- School of Physics and Clinical and Optometric Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, D07 EWV4 Dublin, Ireland
- Centre for Eye Research, Ireland, Technological University Dublin, City Campus, Grangegorman, Dublin 7, D07 EWV4 Dublin, Ireland
- Correspondence:
| | - James Loughman
- School of Physics and Clinical and Optometric Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, D07 EWV4 Dublin, Ireland
- Centre for Eye Research, Ireland, Technological University Dublin, City Campus, Grangegorman, Dublin 7, D07 EWV4 Dublin, Ireland
| | - Ekaterina Loskutova
- School of Physics and Clinical and Optometric Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, D07 EWV4 Dublin, Ireland
- Centre for Eye Research, Ireland, Technological University Dublin, City Campus, Grangegorman, Dublin 7, D07 EWV4 Dublin, Ireland
| | - Hugh J. Byrne
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, D08 CKP1 Dublin, Ireland
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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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Lunter D, Klang V, Kocsis D, Varga-Medveczky Z, Berkó S, Erdő F. Novel aspects of Raman spectroscopy in skin research. Exp Dermatol 2022; 31:1311-1329. [PMID: 35837832 PMCID: PMC9545633 DOI: 10.1111/exd.14645] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/07/2022] [Accepted: 07/12/2022] [Indexed: 11/27/2022]
Abstract
The analytical technology of Raman spectroscopy has an almost 100‐year history. During this period, many modifications and developments happened in the method like discovery of laser, improvements in optical elements and sensitivity of spectrometer and also more advanced light detection systems. Many types of the innovative techniques appeared (e.g. Transmittance Raman spectroscopy, Coherent Raman Scattering microscopy, Surface‐Enhanced Raman scattering and Confocal Raman spectroscopy/microscopy). This review article gives a short description about these different Raman techniques and their possible applications. Then, a short statistical part is coming about the appearance of Raman spectroscopy in the scientific literature from the beginnings to these days. The third part of the paper shows the main application options of the technique (especially confocal Raman spectroscopy) in skin research, including skin composition analysis, drug penetration monitoring and analysis, diagnostic utilizations in dermatology and cosmeto‐scientific applications. At the end, the possible role of artificial intelligence in Raman data analysis and the regulatory aspect of these techniques in dermatology are briefly summarized. For the future of Raman Spectroscopy, increasing clinical relevance and in vivo applications can be predicted with spreading of non‐destructive methods and appearance with the most advanced instruments with rapid analysis time.
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Affiliation(s)
- Dominique Lunter
- University of Tübingen, Department of Pharmaceutical Technology, Institute of Pharmacy and Biochemistry, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Victoria Klang
- University of Vienna, Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology and Biopharmaceutics, Faculty of Life Sciences, Vienna, Austria
| | - Dorottya Kocsis
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Zsófia Varga-Medveczky
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Szilvia Berkó
- University of Szeged, Faculty of Pharmacy, Institute of Pharmaceutical Technology and Regulatory Affairs, Szeged, Hungary
| | - Franciska Erdő
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary.,University of Tours EA 6295 Nanomédicaments et Nanosondes, Tours, France
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Dastgir G, Majeed MI, Nawaz H, Rashid N, Raza A, Ali MZ, Shakeel M, Javed M, Ehsan U, Ishtiaq S, Fatima R, Abdulraheem A. Surface-enhanced Raman spectroscopy of polymerase chain reaction (PCR) products of Rifampin resistant and susceptible tuberculosis patients. Photodiagnosis Photodyn Ther 2022; 38:102758. [DOI: 10.1016/j.pdpdt.2022.102758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/25/2022] [Accepted: 02/10/2022] [Indexed: 10/19/2022]
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Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:8459-8486. [PMID: 35039756 PMCID: PMC8754556 DOI: 10.1007/s12652-021-03612-z] [Citation(s) in RCA: 117] [Impact Index Per Article: 58.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 11/18/2021] [Indexed: 05/03/2023]
Abstract
Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Engineering, Indus Institute of Technology and Engineering, Indus University, Ahmedabad, 382115 India
| | | | - Ruchi Singla
- Department of Research, Innovations, Sponsored Projects and Entrepreneurship, CGC Landran, Mohali, India
| | - Muhammad Fazal Ijaz
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006 South Korea
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Surface-enhanced Raman spectroscopy for comparison of serum samples of typhoid and tuberculosis patients of different stages. Photodiagnosis Photodyn Ther 2021; 35:102426. [PMID: 34217869 DOI: 10.1016/j.pdpdt.2021.102426] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 06/25/2021] [Accepted: 06/28/2021] [Indexed: 11/20/2022]
Abstract
BACKGROUND Surface-enhanced Raman spectroscopy (SERS) is a reliable tool for the identification and differentiation of two different human pathological conditions sharing the same symptomology, typhoid and tuberculosis (TB). OBJECTIVES To explore the potential of surface-enhanced Raman spectroscopy for differentiation of two different diseases showing the same symptoms and analysis by principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA). METHODS Serum samples of clinically diagnosed typhoid and tuberculosis infected individuals were analyzed and differentiated by SERS using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, the collected serum samples were analyzed under the SERS instrument and unique SERS spectra of typhoid and tuberculosis were compared showing notable spectral differences in protein, lipid and carbohydrates features. Different stages of the diseased class of typhoid (Early acute and late acute stage) and tuberculosis (Pulmonary and extra-pulmonary stage) were compared with each other and with healthy human serum samples, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe. RESULTS SERS Spectral data of typhoid and tuberculosis showed clear differences and were significantly separated using PCA. SERS spectral data of both stages of typhoid and tuberculosis were separated according to 1st principle component. Moreover, by analyzing data using partial least square discriminate analysis, differentiation of two disease classes were considered more valid with a 100% value of sensitivity, specificity and accuracy. CONCLUSION SERS can be employed for identification and comparison of two different human pathological conditions sharing same symptomology.
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