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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; 17:e202400060. [PMID: 38937976 DOI: 10.1002/jbio.202400060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 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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Zhang X, Song X, Li W, Chen C, Wusiman M, Zhang L, Zhang J, Lu J, Lu C, Lv X. Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods. Sci Rep 2023; 13:3418. [PMID: 36854769 PMCID: PMC9974944 DOI: 10.1038/s41598-022-22204-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 10/11/2022] [Indexed: 03/02/2023] Open
Abstract
Membranous nephropathy is the main cause of nephrotic syndrome, which has an insidious onset and may progress to end-stage renal disease with a high mortality rate, such as renal failure and uremia. At present, the diagnosis of membranous nephropathy mainly relies on the clinical manifestations of patients and pathological examination of kidney biopsy, which are expensive, time-consuming, and have certain chance and other disadvantages. Therefore, there is an urgent need to find a rapid, accurate and non-invasive diagnostic technique for the diagnosis of membranous nephropathy. In this study, Raman spectra of serum and urine were combined with deep learning methods to diagnose membranous nephropathy. After baseline correction and smoothing of the data, Gaussian white noise of different decibels was added to the training set for data amplification, and the amplified data were imported into ResNet, AlexNet and GoogleNet models to obtain the evaluation results of the models for membranous nephropathy. The experimental results showed that the three deep learning models achieved an accuracy of 1 for the classification of serum data of patients with membranous nephropathy and control group, and the discrimination of urine data was above 0.85, among which AlexNet was the best classification model for both samples. The above experimental results illustrate the great potential of serum- and urine-based Raman spectroscopy combined with deep learning methods for rapid and accurate identification of patients with membranous nephropathy.
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Affiliation(s)
- Xueqin Zhang
- grid.410644.3People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001 China
| | - Xue Song
- grid.410644.3People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001 China
| | - Wenjing Li
- grid.413254.50000 0000 9544 7024College of Software, Xinjiang University, Urumqi, 830046 China
| | - Cheng Chen
- grid.413254.50000 0000 9544 7024College of Software, Xinjiang University, Urumqi, 830046 China
| | - Miriban Wusiman
- grid.13394.3c0000 0004 1799 3993Xinjiang Medical University, Urumqi, 830054 China
| | - Li Zhang
- grid.412631.3The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011 China
| | - Jiahui Zhang
- grid.13394.3c0000 0004 1799 3993Xinjiang Medical University, Urumqi, 830054 China
| | - Jinyu Lu
- grid.410644.3People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001 China
| | - Chen Lu
- The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, 830046, China.
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Udensi J, Loughman J, Loskutova E, Byrne HJ. Raman Spectroscopy of Carotenoid Compounds for Clinical Applications-A Review. Molecules 2022; 27:9017. [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
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
| | - 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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Rapid and sensitive detection of esophageal cancer by FTIR spectroscopy of serum and plasma. Photodiagnosis Photodyn Ther 2022; 40:103177. [PMID: 36602070 DOI: 10.1016/j.pdpdt.2022.103177] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 11/11/2022]
Abstract
Fourier transform infrared (FTIR) spectroscopy, as a platform technology for cancer detection, must be up to the challenge of clinical transformation. To this end, detection of esophageal squamous cell carcinoma (ESCC) was hereby explored using serum and plasma scrape-coated on barium fluoride (BaF2) disk by transmission FTIR method, and the classification model was built using six multivariate statistical analyses, including support vector machine (SVM), principal component linear discriminant analysis (PC-LDA), decision tree (DT), k-nearest neighbor (KNN) classification, ensemble algorithms (EA) and partial least squares for discriminant analysis (PLS-DA). All statistical analyses methods demonstrated that late-stage cancer could be well classified from healthy people employing either serum or plasma with different anticoagulants. Resulting PC-LDA model differentiated late-stage cancer from normal group with an accuracy of 99.26%, a sensitivity of 98.53%, and a specificity of 100%. The accuracy and sensitivity reached 97.08% and 91.43%, respectively for early-stage cancer discrimination from normal group. This pilot exploration demonstrated that transmission FTIR provided a rapid, cost effective and sensitive method for ESCC diagnosis using either serum or plasma.
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Huang W, Shang Q, Xiao X, Zhang H, Gu Y, Yang L, Shi G, Yang Y, Hu Y, Yuan Y, Ji A, Chen L. Raman spectroscopy and machine learning for the classification of esophageal squamous carcinoma. SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 281:121654. [PMID: 35878494 DOI: 10.1016/j.saa.2022.121654] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 07/15/2022] [Accepted: 07/17/2022] [Indexed: 02/05/2023]
Abstract
Early diagnosis of esophageal squamous cell carcinoma (ESCC), a common malignant tumor with a low overall survival rate due to metastasis and recurrence, is critical for effective treatment and improved prognosis. Raman spectroscopy, an advanced detection technology for esophageal cancer, was developed to improve diagnosis sensitivity, specificity, and accuracy. This study proposed a novel, effective, and noninvasive Raman spectroscopy technique to differentiate and classify ESCC cell lines. Seven ESCC cell lines and tissues of an ESCC patient with staging of T3N1M0 and T3N2M0 at low and high differentiation levels were investigated through Raman spectroscopy. Raman spectral data analysis was performed with four machine learning algorithms, namely principal components analysis (PCA)- linear discriminant analysis (LDA), PCA-eXtreme gradient boosting (XGB), PCA- support vector machine (SVM), and PCA- (LDA, XGB, SVM)-stacked Gradient Boosting Machine (GBM). Four machine learning algorithms were able to classifiy ESCC cell subtypes from normal esophageal cells. The PCA-XGB model achieved an overall predictive accuracy of 85% for classifying ESCC and adjacent tissues. Moreover, an overall predictive accuracy of 90.3% was achieved in distinguishing low differentiation and high differentiation ESCC tissues with the same stage when PCA-LDA, XGM, and SVM models were combined. This study illustrated the Raman spectral traits of ESCC cell lines and esophageal tissues related to clinical pathological diagnosis. Future studies should investigate the role of Raman spectral features in ESCC pathogenesis.
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Affiliation(s)
- Wenhua Huang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Qixin Shang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xin Xiao
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hanlu Zhang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yimin Gu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Lin Yang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Guidong Shi
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yushang Yang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yang Hu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yong Yuan
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Aifang Ji
- Heping Hospital Affiliated to Changzhi Medical University, No. 161 Jiefang East Street, Changzhi 046000, China.
| | - Longqi Chen
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.
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