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González-Viveros N, Castro-Ramos J, Gómez-Gil P, Cerecedo-Núñez HH, Gutiérrez-Delgado F, Torres-Rasgado E, Pérez-Fuentes R, Flores-Guerrero JL. Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks. Lasers Med Sci 2022; 37:3537-3549. [PMID: 36063232 PMCID: PMC9708775 DOI: 10.1007/s10103-022-03633-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 08/14/2022] [Indexed: 01/17/2023]
Abstract
Undiagnosed type 2 diabetes (T2D) remains a major public health concern. The global estimation of undiagnosed diabetes is about 46%, being this situation more critical in developing countries. Therefore, we proposed a non-invasive method to quantify glycated hemoglobin (HbA1c) and glucose in vivo. We developed a technique based on Raman spectroscopy, RReliefF as a feature selection method, and regression based on feed-forward artificial neural networks (FFNN). The spectra were obtained from the forearm, wrist, and index finger of 46 individuals. The use of FFNN allowed us to achieve an error in the predictive model of 0.69% for HbA1c and 30.12 mg/dL for glucose. Patients were classified according to HbA1c values into three categories: healthy, prediabetes, and T2D. The proposed method obtained a specificity and sensitivity of 87.50% and 80.77%, respectively. This work demonstrates the benefit of using artificial neural networks and feature selection techniques to enhance Raman spectra processing to determine glycated hemoglobin and glucose in patients with undiagnosed T2D.
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Affiliation(s)
- Naara González-Viveros
- Optics Coordination, National Institute of Astrophysics, Optics and Electronics (INAOE), 72840, Puebla, Mexico
| | - Jorge Castro-Ramos
- Optics Coordination, National Institute of Astrophysics, Optics and Electronics (INAOE), 72840, Puebla, Mexico
| | - Pilar Gómez-Gil
- Computer Science Coordination, National Institute of Astrophysics, Optics and Electronics (INAOE), 72840, Puebla, Mexico
| | | | | | - Enrique Torres-Rasgado
- Faculty of Medicine, Meritorious Autonomous University of Puebla (BUAP), 72589, Puebla, Mexico
| | - Ricardo Pérez-Fuentes
- Department of Chronic Disease Physiopathology, East Center of Biomedical Research, Mexican Social Security Institute (CIBIOR), 74360, Puebla, México
| | - Jose L Flores-Guerrero
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, WC1E 7HB, UK.
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2
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Flores-Guerrero JL, Muñoz-Morales A, Narea-Jimenez F, Perez-Fuentes R, Torres-Rasgado E, Ruiz-Vivanco G, Gonzalez-Viveros N, Castro-Ramos J. Novel Assessment of Urinary Albumin Excretion in Type 2 Diabetes Patients by Raman Spectroscopy. Diagnostics (Basel) 2020; 10:diagnostics10030141. [PMID: 32138353 PMCID: PMC7151048 DOI: 10.3390/diagnostics10030141] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 11/25/2019] [Accepted: 12/24/2019] [Indexed: 11/16/2022] Open
Abstract
Urinary albumin excretion remains the key biomarker to detect renal complications in type 2 diabetes. As diabetes epidemy increases, particularly in low-income countries, efficient and low-cost methods to measure urinary albumin are needed. In this pilot study, we evaluated the performance of Raman spectroscopy in the assessment of urinary albumin in patients with type 2 diabetes. The spectral Raman analysis of albumin was performed using artificial urine, at five concentrations of albumin and 24 h collection urine samples from ten patients with Type 2 Diabetes. The spectra were obtained after removing the background fluorescence and fitting Gaussian curves to spectral regions containing features of such metabolites. In the samples from patients with type 2 diabetes, we identified the presence of albumin in the peaks of the spectrum located at 663.07, 993.43, 1021.43, 1235.28, 1429.91 and 1633.91 cm−1. In artificial urine, there was an increase in the intensity of the Raman signal at 1450 cm−1, which corresponds to the increment of the concentrations of albumin. The highest concentration of albumin was located at 1630 cm−1. The capability of Raman spectroscopy for detection of small concentrations of urinary albumin suggests the feasibility of this method for the screening of type 2 diabetes renal complications.
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Affiliation(s)
- Jose L. Flores-Guerrero
- Department of Internal Medicine, Division of Nephrology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
- Correspondence: ; Tel.:+ 31-50-36-10137
| | - Aaron Muñoz-Morales
- Department of Physics, Faculty of Experimental Sciences and Technology, Center of Medical and Biotechnology Research, University of Carabobo, Valencia 2005, Venezuela;
| | - Freddy Narea-Jimenez
- Optics coordination, Biomedical Optics Group, National Institute of Astrophysics, Optics and Electronics, INAOE, Puebla 72840, Mexico; (F.N.-J.); (N.G.-V.); (J.C.-R.)
| | - Ricardo Perez-Fuentes
- Department of Chronic Disease Physiopathology, East Center of Biomedical Research, Mexican Social Security Institute, CIBIOR, Puebla 74360, Mexico; (R.P.-F.); (G.R.-V.)
| | - Enrique Torres-Rasgado
- Faculty of Medicine, Meritorious Autonomous University of Puebla, BUAP, Puebla 72589, Mexico;
| | - Guadalupe Ruiz-Vivanco
- Department of Chronic Disease Physiopathology, East Center of Biomedical Research, Mexican Social Security Institute, CIBIOR, Puebla 74360, Mexico; (R.P.-F.); (G.R.-V.)
| | - Naara Gonzalez-Viveros
- Optics coordination, Biomedical Optics Group, National Institute of Astrophysics, Optics and Electronics, INAOE, Puebla 72840, Mexico; (F.N.-J.); (N.G.-V.); (J.C.-R.)
| | - Jorge Castro-Ramos
- Optics coordination, Biomedical Optics Group, National Institute of Astrophysics, Optics and Electronics, INAOE, Puebla 72840, Mexico; (F.N.-J.); (N.G.-V.); (J.C.-R.)
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Bai Y, Liu Q. Denoising Raman spectra by Wiener estimation with a numerical calibration dataset. BIOMEDICAL OPTICS EXPRESS 2020; 11:200-214. [PMID: 32010510 PMCID: PMC6968752 DOI: 10.1364/boe.11.000200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 11/18/2019] [Accepted: 11/29/2019] [Indexed: 06/10/2023]
Abstract
Most denoising methods that are currently used in the processing of Raman spectra require significant user interaction in order to optimize their performance across a range of signal-to-noise ratios. In this study, we proposed a method based on the principle of spectral integration followed by Wiener estimation using a numerical calibration dataset, which eliminates the need of experimental measurements for calibration as in the previous Wiener estimation based denoising method. The new method was tested on three types of samples, including a phantom sample, human fingernail and leukemia cells. Compared to two common denoising methods, i.e. moving-average filtering and Savitzky-Golay filtering, the performance of the proposed method is significantly less sensitive to the choices of parameters. Moreover, this method provides comparable or even better denoising performance in the cases with low signal-to-noise ratios.
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4
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Zhu S, Cui X, Xu W, Chen S, Qian W. Weighted spectral reconstruction method for discrimination of bacterial species with low signal-to-noise ratio Raman measurements. RSC Adv 2019; 9:9500-9508. [PMID: 35520730 PMCID: PMC9062122 DOI: 10.1039/c9ra00327d] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 03/08/2019] [Indexed: 01/07/2023] Open
Abstract
Raman spectroscopy is a label-free and non-destructive spectroscopic technique that has been explored for bacterial identification. However, noise often interferes with the interesting Raman peaks because the Raman signal is inherently weak, especially for bacterial samples. Although this problem can be solved by increasing the exposure time or the power of the excitation laser, a longer acquisition time is required or the risk of sample damage is increased. In contrast, short exposure time and low laser power often lead to inadequate acquisition of Raman scattering, in which the Raman spectra with low signal-to-noise ratio (SNR) is difficult to be further analyzed. In order to quickly and accurately characterize biological samples by using low SNR Raman measurements, a weighted spectral reconstruction based method was developed and tested on Raman spectra with low SNR from 20 bacterial samples of two species. Principal component analysis followed by support vector machine was applied on the reference Raman spectra and the spectra recovered from the low SNR Raman measurements by the proposed method, the traditional spectral reconstruction method, and four other commonly used de-noising methods for the discrimination of bacterial species. The results showed that a classification accuracy of 90% was achieved based on our method, which was comparable to that of the reference Raman spectra and showed significant advantages over other spectral recovery methods. Therefore, the weighted spectral reconstruction method can preserve the most biochemical information for the bacterial species' identification while removing the noise from the low SNR Raman spectra, in which the advantages of lesser sample damage and shorter acquisition time would promote wider biomedical applications of Raman spectroscopy. Raman spectra recovered from low SNR Raman measurements by weighted spectral reconstruction method show excellent preservation of information about bacterial identification.![]()
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Affiliation(s)
- Shanshan Zhu
- Sino-Dutch Biomedical and Information Engineering School
- Northeastern University
- Shenyang
- China
| | - Xiaoyu Cui
- Sino-Dutch Biomedical and Information Engineering School
- Northeastern University
- Shenyang
- China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University)
| | - Wenbin Xu
- Science and Technology on Optical Radiation Laboratory
- Beijing
- China
| | - Shuo Chen
- Sino-Dutch Biomedical and Information Engineering School
- Northeastern University
- Shenyang
- China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University)
| | - Wei Qian
- College of Engineering
- University of Texas at El Paso
- El Paso
- USA
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5
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Villa-Manríquez JF, Castro-Ramos J, Gutiérrez-Delgado F, Lopéz-Pacheco MA, Villanueva-Luna AE. Raman spectroscopy and PCA-SVM as a non-invasive diagnostic tool to identify and classify qualitatively glycated hemoglobin levels in vivo. JOURNAL OF BIOPHOTONICS 2017; 10:1074-1079. [PMID: 28009134 DOI: 10.1002/jbio.201600169] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Revised: 11/22/2016] [Accepted: 11/24/2016] [Indexed: 06/06/2023]
Abstract
In this study we identify and classify high and low levels of glycated hemoglobin (HbA1c) in healthy volunteers (HV) and diabetic patients (DP). Overall, 86 subjects were evaluated. The Raman spectrum was measured in three anatomical regions of the body: index fingertip, right ear lobe, and forehead. The measurements were performed to compare the difference between the HV and DP (22 well controlled diabetic patients (WCDP) (HbA1c <6.5%), and 49 not controlled diabetic patients (NCDP) (HbA1c ≥6.5%)). Multivariable methods such as principal components analysis (PCA) combined with support vector machine (SVM) were used to develop effective diagnostic algorithms for classification among these groups. The forehead of HV versus WCDP showed the highest sensitivity (100%) and specificity (100%). Sensitivity (100%) and specificity (60%), were highest in the forehead of WCDP, versus NCDP. In HV versus NCDP, the fingertip had the highest sensitivity (100%) and specificity (80%). The efficacy of the diagnostic algorithm by receiver operating characteristic (ROC) curve was confirmed. Overall, our study demonstrated that the combination of Raman spectroscopy and PCA-SVM are feasible non-invasive diagnostic tool in diabetes to classify qualitatively high and low levels of HbA1c in vivo.
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Affiliation(s)
- J F Villa-Manríquez
- Instituto Nacional de Astrofísica Óptica y Electrónica, apartado postal 51 y 216, Tonantzintla, Puebla, CP 72000, México
| | - J Castro-Ramos
- Instituto Nacional de Astrofísica Óptica y Electrónica, apartado postal 51 y 216, Tonantzintla, Puebla, CP 72000, México
| | - F Gutiérrez-Delgado
- Centro de Estudios y Prevención del Cáncer, Bugambilias 30, Fraccionamiento la Rivera, Juchitan, Oaxaca, CP 70020, México
| | - M A Lopéz-Pacheco
- Instituto Nacional de Astrofísica Óptica y Electrónica, apartado postal 51 y 216, Tonantzintla, Puebla, CP 72000, México
| | - A E Villanueva-Luna
- Universidad Tecnológica de Campeche, Carretera Federal 180 S/N, San Antonio Cárdenas, Carmen, Campeche, CP 24100, México
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Wu Y, Guo P, Chen S, Chen H, Zhang Y. Wind profiling for a coherent wind Doppler lidar by an auto-adaptive background subtraction approach. APPLIED OPTICS 2017; 56:2705-2713. [PMID: 28375232 DOI: 10.1364/ao.56.002705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Auto-adaptive background subtraction (AABS) is proposed as a denoising method for data processing of the coherent Doppler lidar (CDL). The method is proposed specifically for a low-signal-to-noise-ratio regime, in which the drifting power spectral density of CDL data occurs. Unlike the periodogram maximum (PM) and adaptive iteratively reweighted penalized least squares (airPLS), the proposed method presents reliable peaks and is thus advantageous in identifying peak locations. According to the analysis results of simulated and actually measured data, the proposed method outperforms the airPLS method and the PM algorithm in the furthest detectable range. The proposed method improves the detection range approximately up to 16.7% and 40% when compared to the airPLS method and the PM method, respectively. It also has smaller mean wind velocity and standard error values than the airPLS and PM methods. The AABS approach improves the quality of Doppler shift estimates and can be applied to obtain the whole wind profiling by the CDL.
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Xie Y, Yang L, Sun X, Wu D, Chen Q, Zeng Y, Liu G. An auto-adaptive background subtraction method for Raman spectra. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2016; 161:58-63. [PMID: 26950502 DOI: 10.1016/j.saa.2016.02.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Revised: 02/06/2016] [Accepted: 02/22/2016] [Indexed: 05/15/2023]
Abstract
Background subtraction is a crucial step in the preprocessing of Raman spectrum. Usually, parameter manipulating of the background subtraction method is necessary for the efficient removal of the background, which makes the quality of the spectrum empirically dependent. In order to avoid artificial bias, we proposed an auto-adaptive background subtraction method without parameter adjustment. The main procedure is: (1) select the local minima of spectrum while preserving major peaks, (2) apply an interpolation scheme to estimate background, (3) and design an iteration scheme to improve the adaptability of background subtraction. Both simulated data and Raman spectra have been used to evaluate the proposed method. By comparing the backgrounds obtained from three widely applied methods: the polynomial, the Baek's and the airPLS, the auto-adaptive method meets the demand of practical applications in terms of efficiency and accuracy.
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Affiliation(s)
- Yi Xie
- Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, China
| | - Lidong Yang
- Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, China
| | - Xilong Sun
- Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, China
| | - Dewen Wu
- Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, China
| | - Qizhen Chen
- College of Chemistry and Chemical Engineering, Xiamen University, China
| | - Yongming Zeng
- College of Chemistry and Chemical Engineering, Xiamen University, China
| | - Guokun Liu
- State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China.
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