Raman spectroscopy combined with multiple algorithms for analysis and rapid screening of chronic renal failure
•Raman spectroscopy combined with multivariate statistical methods is proposed to screen chronic renal failure (CRF) patients.•The high diagnostic accuracy of PCA-GS-SVM illustrated the potential of using serum Raman spectra for diagnosing CRF patients.•The results demonstrated great potentials for...
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| Vydáno v: | Photodiagnosis and photodynamic therapy Ročník 30; s. 101792 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Netherlands
Elsevier B.V
01.06.2020
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| ISSN: | 1572-1000, 1873-1597, 1873-1597 |
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| Abstract | •Raman spectroscopy combined with multivariate statistical methods is proposed to screen chronic renal failure (CRF) patients.•The high diagnostic accuracy of PCA-GS-SVM illustrated the potential of using serum Raman spectra for diagnosing CRF patients.•The results demonstrated great potentials for developing serum Raman spectroscopyinto a novel clinical tool for rapid and non-invasive screening of CRF disease.
Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine urine tests and medical imaging investigations, have poor sensitivity. Therefore, exploring new and efficient screening methods for CRF such as serum spectroscopy is of great significance. In this study, we first used Raman spectroscopy to classify sera from CRF patients and control subjects. A total of 47 samples from CRF patients and 54 samples from control subjects were acquired. The spectra revealed differences in the phospholipids and proteins between the CRF patients and control subjects. The differences between the CRF patients and control subjects were evaluated by building machine learning models. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP) neural network, extreme learning machine (ELM), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM), grid search-support vector machine (GS-SVM) and simulated annealing particle swarm optimization based on support vector machine (SAPSO-SVM) algorithms were employed to establish diagnostic models; the diagnostic accuracy of the six classifiers was 70.4 %, 71 %, 83.5 %, 86.9 %, 89.7 % and 82.8 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in differentiating between the control subjects and CRF patients. Based on the limitations of current routine diagnostic methods, serum Raman spectroscopy may be an adjunct/replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples. |
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| AbstractList | •Raman spectroscopy combined with multivariate statistical methods is proposed to screen chronic renal failure (CRF) patients.•The high diagnostic accuracy of PCA-GS-SVM illustrated the potential of using serum Raman spectra for diagnosing CRF patients.•The results demonstrated great potentials for developing serum Raman spectroscopyinto a novel clinical tool for rapid and non-invasive screening of CRF disease.
Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine urine tests and medical imaging investigations, have poor sensitivity. Therefore, exploring new and efficient screening methods for CRF such as serum spectroscopy is of great significance. In this study, we first used Raman spectroscopy to classify sera from CRF patients and control subjects. A total of 47 samples from CRF patients and 54 samples from control subjects were acquired. The spectra revealed differences in the phospholipids and proteins between the CRF patients and control subjects. The differences between the CRF patients and control subjects were evaluated by building machine learning models. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP) neural network, extreme learning machine (ELM), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM), grid search-support vector machine (GS-SVM) and simulated annealing particle swarm optimization based on support vector machine (SAPSO-SVM) algorithms were employed to establish diagnostic models; the diagnostic accuracy of the six classifiers was 70.4 %, 71 %, 83.5 %, 86.9 %, 89.7 % and 82.8 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in differentiating between the control subjects and CRF patients. Based on the limitations of current routine diagnostic methods, serum Raman spectroscopy may be an adjunct/replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples. Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine urine tests and medical imaging investigations, have poor sensitivity. Therefore, exploring new and efficient screening methods for CRF such as serum spectroscopy is of great significance. In this study, we first used Raman spectroscopy to classify sera from CRF patients and control subjects. A total of 47 samples from CRF patients and 54 samples from control subjects were acquired. The spectra revealed differences in the phospholipids and proteins between the CRF patients and control subjects. The differences between the CRF patients and control subjects were evaluated by building machine learning models. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP) neural network, extreme learning machine (ELM), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM), grid search-support vector machine (GS-SVM) and simulated annealing particle swarm optimization based on support vector machine (SAPSO-SVM) algorithms were employed to establish diagnostic models; the diagnostic accuracy of the six classifiers was 70.4 %, 71 %, 83.5 %, 86.9 %, 89.7 % and 82.8 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in differentiating between the control subjects and CRF patients. Based on the limitations of current routine diagnostic methods, serum Raman spectroscopy may be an adjunct/replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples.Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine urine tests and medical imaging investigations, have poor sensitivity. Therefore, exploring new and efficient screening methods for CRF such as serum spectroscopy is of great significance. In this study, we first used Raman spectroscopy to classify sera from CRF patients and control subjects. A total of 47 samples from CRF patients and 54 samples from control subjects were acquired. The spectra revealed differences in the phospholipids and proteins between the CRF patients and control subjects. The differences between the CRF patients and control subjects were evaluated by building machine learning models. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP) neural network, extreme learning machine (ELM), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM), grid search-support vector machine (GS-SVM) and simulated annealing particle swarm optimization based on support vector machine (SAPSO-SVM) algorithms were employed to establish diagnostic models; the diagnostic accuracy of the six classifiers was 70.4 %, 71 %, 83.5 %, 86.9 %, 89.7 % and 82.8 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in differentiating between the control subjects and CRF patients. Based on the limitations of current routine diagnostic methods, serum Raman spectroscopy may be an adjunct/replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples. Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly reduces the lifespan of the patient. However, current screening strategies, including routine urine tests and medical imaging investigations, have poor sensitivity. Therefore, exploring new and efficient screening methods for CRF such as serum spectroscopy is of great significance. In this study, we first used Raman spectroscopy to classify sera from CRF patients and control subjects. A total of 47 samples from CRF patients and 54 samples from control subjects were acquired. The spectra revealed differences in the phospholipids and proteins between the CRF patients and control subjects. The differences between the CRF patients and control subjects were evaluated by building machine learning models. Subsequent principal component analysis (PCA) was first used for feature extraction. Then, back propagation (BP) neural network, extreme learning machine (ELM), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM), grid search-support vector machine (GS-SVM) and simulated annealing particle swarm optimization based on support vector machine (SAPSO-SVM) algorithms were employed to establish diagnostic models; the diagnostic accuracy of the six classifiers was 70.4 %, 71 %, 83.5 %, 86.9 %, 89.7 % and 82.8 %, respectively, for control subjects and CRF patients. The results show the potential of Raman spectroscopy in differentiating between the control subjects and CRF patients. Based on the limitations of current routine diagnostic methods, serum Raman spectroscopy may be an adjunct/replaceable method for the clinical diagnosis of CRF with the prospective validation of more samples. |
| ArticleNumber | 101792 |
| Author | Chen, Chen Chen, Cheng Li, Hongyi Chen, Fangfang Yang, Li Gao, Rui Tang, Jun Lv, XY |
| Author_xml | – sequence: 1 givenname: Cheng surname: Chen fullname: Chen, Cheng organization: College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China – sequence: 2 givenname: Li surname: Yang fullname: Yang, Li organization: The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, China – sequence: 3 givenname: Hongyi surname: Li fullname: Li, Hongyi organization: Quality of Products Supervision and Inspection Institute, Urumqi 830011, Xinjiang, China – sequence: 4 givenname: Fangfang surname: Chen fullname: Chen, Fangfang organization: College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China – sequence: 5 givenname: Chen surname: Chen fullname: Chen, Chen organization: College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China – sequence: 6 givenname: Rui surname: Gao fullname: Gao, Rui organization: College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China – sequence: 7 givenname: XY surname: Lv fullname: Lv, XY email: xjuwawj01@163.com organization: College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China – sequence: 8 givenname: Jun surname: Tang fullname: Tang, Jun organization: Physics and Chemistry Detecting Center, Xinjiang University, Urumqi 830046, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32353420$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1681/ASN.2014050423 10.1016/j.bios.2018.04.003 10.1007/s10895-010-0600-x 10.1016/j.saa.2019.117173 10.33549/physiolres.932003 10.1016/j.asoc.2019.04.019 10.1292/jvms.16-0188 10.1145/1961189.1961199 10.1002/jbio.201200191 10.1002/jbio.201400060 10.5812/ircmj.14133 10.1056/NEJMra054415 10.1139/tcsme-2019-0053 10.1016/j.ijleo.2017.11.097 10.1080/05704920701551530 10.1016/j.pdpdt.2018.05.010 10.1016/j.vibspec.2013.06.002 10.1039/C8AN00224J 10.1053/j.jrn.2009.01.028 10.1097/MNH.0000000000000380 10.1002/jssc.201700024 10.1364/OE.22.025895 10.1038/ki.2009.289 10.1016/j.clinbiochem.2008.01.022 10.1109/JPHOT.2018.2876686 10.1002/jrs.4924 10.1016/j.saa.2018.04.018 10.1016/j.saa.2018.08.008 10.1021/acs.analchem.8b05907 10.1364/BOE.7.002249 10.1002/jbio.201900099 10.1155/2016/1603609 10.1364/BOE.9.002041 10.1097/MNH.0b013e3282f2905f 10.1681/ASN.2015050542 10.12669/pjms.35.1.96 10.1002/jcla.22713 10.1016/j.ijleo.2019.164043 |
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| Keywords | Support vector machine (SVM) Chronic renal failure (CRF) Raman spectroscopy serum Principal component analysis (PCA) |
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| References | Sviglerova, Kuncova, Nalos, Tonar, Rajdl, Stengl (bib0010) 2010; 59 Zhu, Dong, Wang, Huang, Jin, Zhou, Shi, Gu, Chen, Xu, Song (bib0050) 2016; 62 Li, Chen, Zhang, Guo, Liu, Xu, Li, Lin (bib0135) 2014; 22 Khan, Ullah, Shahzad, Javaid, Khan (bib0090) 2018; 157 Khan, Ullah, Khan, Wahab, Bilal, Ahmed (bib0070) 2016; 7 Naseer, Amin, Saleem, Qazi (bib0110) 2019; 206 Krishnan, Kamath (bib0160) 2019; 80 Gabbai (bib0180) 2018; 27 Bruck, Stel, Gambaro, Hallan, Volzke, Arnlov, Kastarinen, Guessous, Vinhas, Stengel, Brenner, Chudek, Romundstad, Tomson, Gonzalez, Bello, Ferrieres, Palmieri, Browne, Capuano, Van Biesen, Zoccali, Gansevoort, Navis, Rothenbacher, Ferraro, Nitsch, Wanner, Jager, European (bib0025) 2016; 27 Sohail, Khan, Ullah, Qureshi, Bilal, Khan (bib0120) 2018; 9 Tristao, de Carvalho, Gomes, Miranda, Vequi-Suplicy, Lamy, Schor, Bellini (bib0040) 2010; 20 Demir, Dogantekin, Gurel, Aydin, Celiker (bib0185) 2019; 35 Choi, Moon, Seo, Hyun (bib0055) 2017; 79 Khan, Ullah, Khan, Ashraf, Ali, Bilal, Saleem (bib0085) 2018; 23 Lo, Go, Chertow, McCulloch, Fan, Ordonez, Hsu (bib0005) 2009; 76 Xu, Zhang, Yin (bib0165) 2019; 43 Ditta, Nawaz, Mahmood, Majeed, Tahir, Rashid, Muddassar, Al-Saadi, Byrne (bib0125) 2019; 221 Movasaghi, Rehman, Rehman (bib0190) 2007; 42 Stevens, Coresh, Greene, Levey (bib0030) 2006; 354 Narvaez-Sanchez, Gonzalez, Salamanca, Silva, Rios, Arevalo, Gastelbondo, Sanchez (bib0035) 2008; 41 Sikirzhytskaya, Sikirzhytski, Lednev (bib0060) 2014; 7 Mahmood, Nawaz, Ditta, Majeed, Hanif, Rashid, Bhatti, Nargis, Saleem, Bonnier, Byrne (bib0105) 2018; 200 Patel, Rajora, Kumar, Sahu, Kochar, Krishna, Srivastava (bib0115) 2019; 91 Chang, Lin (bib0150) 2011; 2 Ozkan, Durukan, Kavalci, Duman, Sayhan, Salt, Ipekci (bib0045) 2014; 16 Mehta, Atak, Sahu, Srivastava, C (bib0075) 2018; 143 Kim, Lee, Ahn, Lee, Ryu, Choi, Choi (bib0080) 2018; 111 Schanstra, Zurbig, Alkhalaf, Argiles, Bakker, Beige, Bilo, Chatzikyrkou, Dakna, Dawson, Delles, Haller, Haubitz, Husi, Jankowski, Jerums, Kleefstra, Kuznetsova, Maahs, Menne, Mullen, Ortiz, Persson, Rossing, Ruggenenti, Rychlik, Serra, Siwy, Snell-Bergeon, Spasovski, Staessen, Vlahou, Mischak, Vanholder (bib0020) 2015; 26 Axelsson, Stenvinkel (bib0170) 2008; 17 Rubina, Amita, Kedar, Bharat, Krishna (bib0095) 2013; 68 Wang, Zhang, Lu, Wang (bib0015) 2019; 33 Ramos, Shintani, Himmelfarb, Ikizler (bib0175) 2009; 19 Chen, Yang, Zhao, Yuan, Chen, Tang, Yang, Yan, Wang, Lv (bib0145) 2020; 203 Li, Yang, Li, Wang, Song, Yu (bib0195) 2016; 47 Ryzhikova, Kazakov, Halamkova, Celmins, Malone, Molho, Zimmerman, Lednev (bib0065) 2015; 8 Zheng, Lv, Du, Zhai, Mo, Lv (bib0130) 2018; 10 Liu, Sun, Chen, Jing (bib0140) 2016; 2016 Chen, Du, Tong, Lv, Lv, Si, Tang, Li, Ma, Mo (bib0100) 2020 Hu, Fang, Han, Fu, Tong, Wang (bib0155) 2017; 40 Tristao (10.1016/j.pdpdt.2020.101792_bib0040) 2010; 20 Sohail (10.1016/j.pdpdt.2020.101792_bib0120) 2018; 9 Narvaez-Sanchez (10.1016/j.pdpdt.2020.101792_bib0035) 2008; 41 Mehta (10.1016/j.pdpdt.2020.101792_bib0075) 2018; 143 Xu (10.1016/j.pdpdt.2020.101792_bib0165) 2019; 43 Chen (10.1016/j.pdpdt.2020.101792_bib0100) 2020 Krishnan (10.1016/j.pdpdt.2020.101792_bib0160) 2019; 80 Hu (10.1016/j.pdpdt.2020.101792_bib0155) 2017; 40 Movasaghi (10.1016/j.pdpdt.2020.101792_bib0190) 2007; 42 Wang (10.1016/j.pdpdt.2020.101792_bib0015) 2019; 33 Li (10.1016/j.pdpdt.2020.101792_bib0135) 2014; 22 Axelsson (10.1016/j.pdpdt.2020.101792_bib0170) 2008; 17 Ramos (10.1016/j.pdpdt.2020.101792_bib0175) 2009; 19 Naseer (10.1016/j.pdpdt.2020.101792_bib0110) 2019; 206 Khan (10.1016/j.pdpdt.2020.101792_bib0085) 2018; 23 Gabbai (10.1016/j.pdpdt.2020.101792_bib0180) 2018; 27 Stevens (10.1016/j.pdpdt.2020.101792_bib0030) 2006; 354 Mahmood (10.1016/j.pdpdt.2020.101792_bib0105) 2018; 200 Bruck (10.1016/j.pdpdt.2020.101792_bib0025) 2016; 27 Li (10.1016/j.pdpdt.2020.101792_bib0195) 2016; 47 Choi (10.1016/j.pdpdt.2020.101792_bib0055) 2017; 79 Ditta (10.1016/j.pdpdt.2020.101792_bib0125) 2019; 221 Chang (10.1016/j.pdpdt.2020.101792_bib0150) 2011; 2 Lo (10.1016/j.pdpdt.2020.101792_bib0005) 2009; 76 Schanstra (10.1016/j.pdpdt.2020.101792_bib0020) 2015; 26 Zheng (10.1016/j.pdpdt.2020.101792_bib0130) 2018; 10 Ozkan (10.1016/j.pdpdt.2020.101792_bib0045) 2014; 16 Khan (10.1016/j.pdpdt.2020.101792_bib0070) 2016; 7 Khan (10.1016/j.pdpdt.2020.101792_bib0090) 2018; 157 Liu (10.1016/j.pdpdt.2020.101792_bib0140) 2016; 2016 Kim (10.1016/j.pdpdt.2020.101792_bib0080) 2018; 111 Patel (10.1016/j.pdpdt.2020.101792_bib0115) 2019; 91 Zhu (10.1016/j.pdpdt.2020.101792_bib0050) 2016; 62 Chen (10.1016/j.pdpdt.2020.101792_bib0145) 2020; 203 Sviglerova (10.1016/j.pdpdt.2020.101792_bib0010) 2010; 59 Ryzhikova (10.1016/j.pdpdt.2020.101792_bib0065) 2015; 8 Sikirzhytskaya (10.1016/j.pdpdt.2020.101792_bib0060) 2014; 7 Rubina (10.1016/j.pdpdt.2020.101792_bib0095) 2013; 68 Demir (10.1016/j.pdpdt.2020.101792_bib0185) 2019; 35 |
| References_xml | – volume: 68 start-page: 115 year: 2013 end-page: 121 ident: bib0095 article-title: Raman spectroscopic study on classification of cervical cell specimens publication-title: Vib. Spectrosc. – volume: 7 start-page: 2249 year: 2016 end-page: 2256 ident: bib0070 article-title: Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM) publication-title: Biomed. Opt. Express – volume: 157 start-page: 565 year: 2018 end-page: 570 ident: bib0090 article-title: Optical screening of nasopharyngeal cancer using Raman spectroscopy and support vector machine publication-title: Optik – volume: 203 year: 2020 ident: bib0145 article-title: Urine Raman spectroscopy for rapid and inexpensive diagnosis of chronic renal failure (CRF) using multiple classification algorithms publication-title: Optik – volume: 40 start-page: 2506 year: 2017 end-page: 2514 ident: bib0155 article-title: Rapid detection of six phosphodiesterase type 5 enzyme inhibitors in healthcare products using thin-layer chromatography and surface enhanced Raman spectroscopy combined with BP neural network publication-title: J. Sep. Sci. – volume: 22 start-page: 25895 year: 2014 end-page: 25908 ident: bib0135 article-title: Identification and characterization of colorectal cancer using Raman spectroscopy and feature selection techniques publication-title: Opt. Express – volume: 206 start-page: 197 year: 2019 end-page: 201 ident: bib0110 article-title: Raman spectroscopy based differentiation of typhoid and dengue fever in infected human sera publication-title: Spectrochim. Acta Part A-Molecular and Biomolecular Spectroscopy – volume: 76 start-page: 893 year: 2009 end-page: 899 ident: bib0005 article-title: Dialysis-requiring acute renal failure increases the risk of progressive chronic kidney disease publication-title: Kidney Int. – volume: 7 start-page: 59 year: 2014 end-page: 67 ident: bib0060 article-title: Raman spectroscopy coupled with advanced statistics for differentiating menstrual and peripheral blood publication-title: J. Biophotonics – volume: 200 start-page: 136 year: 2018 end-page: 142 ident: bib0105 article-title: Raman spectral analysis for rapid screening of dengue infection publication-title: Spectrochim. Acta Part A-Molecular and Biomolecular Spectroscopy – volume: 35 start-page: 230 year: 2019 end-page: 235 ident: bib0185 article-title: Is there a relationship between serum vaspin levels and insulin resistance in chronic renal failure? publication-title: Pak. J. Med. Sci. – volume: 59 start-page: S81 year: 2010 end-page: S88 ident: bib0010 article-title: Cardiovascular parameters in rat model of chronic renal failure induced by subtotal nephrectomy publication-title: Physiol. Res. – volume: 33 year: 2019 ident: bib0015 article-title: Efficacy of different hemodialysis methods on dendritic cell marker CD40 and CD80 and platelet activation marker CD62P and P10 in patients with chronic renal failure publication-title: J. Clin. Lab. Anal. – volume: 8 start-page: 584 year: 2015 end-page: 596 ident: bib0065 article-title: Raman spectroscopy of blood serum for Alzheimer’s disease diagnostics: specificity relative to other types of dementia publication-title: J. Biophotonics – volume: 17 start-page: 25 year: 2008 end-page: 31 ident: bib0170 article-title: Role of fat mass and adipokines in chronic kidney disease publication-title: Curr. Opin. Nephrol. Hypertens. – volume: 62 start-page: 1265 year: 2016 end-page: 1270 ident: bib0050 article-title: Serum lipase as clinical laboratory index for chronic renal failure diagnosis publication-title: Clin. Lab. – volume: 19 start-page: 197 year: 2009 end-page: 203 ident: bib0175 article-title: Determinants of plasma adiponectin levels in nondiabetic subjects with moderate to severe chronic kidney disease publication-title: J. Ren. Nutr. – volume: 111 start-page: 59 year: 2018 end-page: 65 ident: bib0080 article-title: A label-free cellulose SERS biosensor chip with improvement of nanoparticle-enhanced LSPR effects for early diagnosis of subarachnoid hemorrhage-induced complications publication-title: Biosens. Bioelectron. – volume: 23 start-page: 89 year: 2018 end-page: 93 ident: bib0085 article-title: Analysis of hepatitis B virus infection in blood sera using Raman spectroscopy and machine learning publication-title: Photodiagnosis Photodyn. Ther. – volume: 20 start-page: 665 year: 2010 end-page: 669 ident: bib0040 article-title: Study of blood porphyrin spectral profile for diagnosis of chronic renal failure publication-title: J. Fluoresc. – volume: 16 year: 2014 ident: bib0045 article-title: Importance of neutrophil gelatinase-associated lipocalin in differential diagnosis of acute and chronic renal failure publication-title: Iran. Red Crescent Med. J. – volume: 79 start-page: 41 year: 2017 end-page: 46 ident: bib0055 article-title: Evaluation of serum cystatin-C and symmetric dimethylarginine concentrations in dogs with heart failure from chronic mitral valvular insufficiency publication-title: J. Vet. Med. Sci. – volume: 354 start-page: 2473 year: 2006 end-page: 2483 ident: bib0030 article-title: Medical progress - assessing kidney function - measured and estimated glomerular filtration rate publication-title: N. Engl. J. Med. – volume: 80 start-page: 525 year: 2019 end-page: 533 ident: bib0160 article-title: A novel GA-ELM model for patient-specific mortality prediction over large-scale lab event data publication-title: Appl. Soft Comput. – volume: 143 start-page: 1916 year: 2018 end-page: 1923 ident: bib0075 article-title: An early investigative serum Raman spectroscopy study of meningioma publication-title: Analyst – volume: 91 start-page: 7054 year: 2019 end-page: 7062 ident: bib0115 article-title: Rapid discrimination of malaria- and dengue-infected patients sera using raman spectroscopy publication-title: Anal. Chem. – year: 2020 ident: bib0100 article-title: Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction publication-title: J. Biophotonics – volume: 27 start-page: 23 year: 2018 end-page: 29 ident: bib0180 article-title: The role of renal response to amino acid infusion and oral protein load in normal kidneys and kidney with acute and chronic disease publication-title: Curr. Opin. Nephrol. Hypertens. – volume: 9 start-page: 2041 year: 2018 end-page: 2055 ident: bib0120 article-title: Analysis of hepatitis C infection using Raman spectroscopy and proximity based classification in the transformed domain publication-title: Biomed. Opt. Express – volume: 2 start-page: 1 year: 2011 end-page: 27 ident: bib0150 article-title: Libsvm publication-title: ACM Trans. Intell. Syst. Technol. – volume: 10 start-page: 1 year: 2018 end-page: 12 ident: bib0130 article-title: Rapid and low-cost detection of thyroid dysfunction using raman spectroscopy and an improved support vector machine publication-title: IEEE Photonics J. – volume: 41 start-page: 498 year: 2008 end-page: 503 ident: bib0035 article-title: Cystatin C could be a replacement to serum creatinine for diagnosing and monitoring kidney function in children publication-title: Clin. Biochem. – volume: 43 year: 2019 ident: bib0165 article-title: RETRACTION: early bearing fault diagnosis based on improved SFLA and ELM network (Retraction Article, vol 42, pg 187, 2018) (Retraction of Vol 42, Pg 187, 2018) publication-title: Trans. Can. Soc. Mech. Eng. – volume: 2016 start-page: 1 year: 2016 end-page: 6 ident: bib0140 article-title: Raman spectroscopy in colorectal Cancer diagnostics: comparison of PCA-LDA and PLS-DA models publication-title: J. Spectrosc. – volume: 221 year: 2019 ident: bib0125 article-title: Principal components analysis of Raman spectral data for screening of Hepatitis C infection publication-title: Spectrochim. Acta Part A-Molecular and Biomolecular Spectroscopy – volume: 27 start-page: 2135 year: 2016 end-page: 2147 ident: bib0025 article-title: CKD prevalence varies across the european general population publication-title: J. Am. Soc. Nephrol. – volume: 26 start-page: 1999 year: 2015 end-page: 2010 ident: bib0020 article-title: Diagnosis and prediction of CKD progression by assessment of urinary peptides publication-title: J. Am. Soc. Nephrol. – volume: 42 start-page: 493 year: 2007 end-page: 541 ident: bib0190 article-title: Raman spectroscopy of biological tissues publication-title: Appl. Spectrosc. Rev. – volume: 47 start-page: 917 year: 2016 end-page: 925 ident: bib0195 article-title: Different classification algorithms and serum surface enhanced Raman spectroscopy for noninvasive discrimination of gastric diseases publication-title: J. Raman Spectrosc. – volume: 26 start-page: 1999 issue: 8 year: 2015 ident: 10.1016/j.pdpdt.2020.101792_bib0020 article-title: Diagnosis and prediction of CKD progression by assessment of urinary peptides publication-title: J. Am. Soc. Nephrol. doi: 10.1681/ASN.2014050423 – volume: 111 start-page: 59 year: 2018 ident: 10.1016/j.pdpdt.2020.101792_bib0080 article-title: A label-free cellulose SERS biosensor chip with improvement of nanoparticle-enhanced LSPR effects for early diagnosis of subarachnoid hemorrhage-induced complications publication-title: Biosens. Bioelectron. doi: 10.1016/j.bios.2018.04.003 – volume: 20 start-page: 665 issue: 3 year: 2010 ident: 10.1016/j.pdpdt.2020.101792_bib0040 article-title: Study of blood porphyrin spectral profile for diagnosis of chronic renal failure publication-title: J. Fluoresc. doi: 10.1007/s10895-010-0600-x – volume: 221 year: 2019 ident: 10.1016/j.pdpdt.2020.101792_bib0125 article-title: Principal components analysis of Raman spectral data for screening of Hepatitis C infection publication-title: Spectrochim. Acta Part A-Molecular and Biomolecular Spectroscopy doi: 10.1016/j.saa.2019.117173 – volume: 59 start-page: S81 year: 2010 ident: 10.1016/j.pdpdt.2020.101792_bib0010 article-title: Cardiovascular parameters in rat model of chronic renal failure induced by subtotal nephrectomy publication-title: Physiol. Res. doi: 10.33549/physiolres.932003 – volume: 80 start-page: 525 year: 2019 ident: 10.1016/j.pdpdt.2020.101792_bib0160 article-title: A novel GA-ELM model for patient-specific mortality prediction over large-scale lab event data publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.04.019 – volume: 79 start-page: 41 issue: 1 year: 2017 ident: 10.1016/j.pdpdt.2020.101792_bib0055 article-title: Evaluation of serum cystatin-C and symmetric dimethylarginine concentrations in dogs with heart failure from chronic mitral valvular insufficiency publication-title: J. Vet. Med. Sci. doi: 10.1292/jvms.16-0188 – volume: 2 start-page: 1 issue: 3 year: 2011 ident: 10.1016/j.pdpdt.2020.101792_bib0150 article-title: Libsvm publication-title: ACM Trans. Intell. Syst. Technol. doi: 10.1145/1961189.1961199 – volume: 7 start-page: 59 issue: 1–2 year: 2014 ident: 10.1016/j.pdpdt.2020.101792_bib0060 article-title: Raman spectroscopy coupled with advanced statistics for differentiating menstrual and peripheral blood publication-title: J. Biophotonics doi: 10.1002/jbio.201200191 – volume: 8 start-page: 584 issue: 7 year: 2015 ident: 10.1016/j.pdpdt.2020.101792_bib0065 article-title: Raman spectroscopy of blood serum for Alzheimer’s disease diagnostics: specificity relative to other types of dementia publication-title: J. Biophotonics doi: 10.1002/jbio.201400060 – volume: 16 issue: 8 year: 2014 ident: 10.1016/j.pdpdt.2020.101792_bib0045 article-title: Importance of neutrophil gelatinase-associated lipocalin in differential diagnosis of acute and chronic renal failure publication-title: Iran. Red Crescent Med. J. doi: 10.5812/ircmj.14133 – volume: 354 start-page: 2473 issue: 23 year: 2006 ident: 10.1016/j.pdpdt.2020.101792_bib0030 article-title: Medical progress - assessing kidney function - measured and estimated glomerular filtration rate publication-title: N. Engl. J. Med. doi: 10.1056/NEJMra054415 – volume: 62 start-page: 1265 issue: 7 year: 2016 ident: 10.1016/j.pdpdt.2020.101792_bib0050 article-title: Serum lipase as clinical laboratory index for chronic renal failure diagnosis publication-title: Clin. Lab. – volume: 43 issue: 2 year: 2019 ident: 10.1016/j.pdpdt.2020.101792_bib0165 article-title: RETRACTION: early bearing fault diagnosis based on improved SFLA and ELM network (Retraction Article, vol 42, pg 187, 2018) (Retraction of Vol 42, Pg 187, 2018) publication-title: Trans. Can. Soc. Mech. Eng. doi: 10.1139/tcsme-2019-0053 – volume: 157 start-page: 565 year: 2018 ident: 10.1016/j.pdpdt.2020.101792_bib0090 article-title: Optical screening of nasopharyngeal cancer using Raman spectroscopy and support vector machine publication-title: Optik doi: 10.1016/j.ijleo.2017.11.097 – volume: 42 start-page: 493 issue: 5 year: 2007 ident: 10.1016/j.pdpdt.2020.101792_bib0190 article-title: Raman spectroscopy of biological tissues publication-title: Appl. Spectrosc. Rev. doi: 10.1080/05704920701551530 – volume: 23 start-page: 89 year: 2018 ident: 10.1016/j.pdpdt.2020.101792_bib0085 article-title: Analysis of hepatitis B virus infection in blood sera using Raman spectroscopy and machine learning publication-title: Photodiagnosis Photodyn. Ther. doi: 10.1016/j.pdpdt.2018.05.010 – volume: 68 start-page: 115 year: 2013 ident: 10.1016/j.pdpdt.2020.101792_bib0095 article-title: Raman spectroscopic study on classification of cervical cell specimens publication-title: Vib. Spectrosc. doi: 10.1016/j.vibspec.2013.06.002 – volume: 143 start-page: 1916 issue: 8 year: 2018 ident: 10.1016/j.pdpdt.2020.101792_bib0075 article-title: An early investigative serum Raman spectroscopy study of meningioma publication-title: Analyst doi: 10.1039/C8AN00224J – volume: 19 start-page: 197 issue: 3 year: 2009 ident: 10.1016/j.pdpdt.2020.101792_bib0175 article-title: Determinants of plasma adiponectin levels in nondiabetic subjects with moderate to severe chronic kidney disease publication-title: J. Ren. Nutr. doi: 10.1053/j.jrn.2009.01.028 – volume: 27 start-page: 23 issue: 1 year: 2018 ident: 10.1016/j.pdpdt.2020.101792_bib0180 article-title: The role of renal response to amino acid infusion and oral protein load in normal kidneys and kidney with acute and chronic disease publication-title: Curr. Opin. Nephrol. Hypertens. doi: 10.1097/MNH.0000000000000380 – volume: 40 start-page: 2506 issue: 11 year: 2017 ident: 10.1016/j.pdpdt.2020.101792_bib0155 article-title: Rapid detection of six phosphodiesterase type 5 enzyme inhibitors in healthcare products using thin-layer chromatography and surface enhanced Raman spectroscopy combined with BP neural network publication-title: J. Sep. Sci. doi: 10.1002/jssc.201700024 – volume: 22 start-page: 25895 issue: 21 year: 2014 ident: 10.1016/j.pdpdt.2020.101792_bib0135 article-title: Identification and characterization of colorectal cancer using Raman spectroscopy and feature selection techniques publication-title: Opt. Express doi: 10.1364/OE.22.025895 – volume: 76 start-page: 893 issue: 8 year: 2009 ident: 10.1016/j.pdpdt.2020.101792_bib0005 article-title: Dialysis-requiring acute renal failure increases the risk of progressive chronic kidney disease publication-title: Kidney Int. doi: 10.1038/ki.2009.289 – volume: 41 start-page: 498 issue: 7–8 year: 2008 ident: 10.1016/j.pdpdt.2020.101792_bib0035 article-title: Cystatin C could be a replacement to serum creatinine for diagnosing and monitoring kidney function in children publication-title: Clin. Biochem. doi: 10.1016/j.clinbiochem.2008.01.022 – volume: 10 start-page: 1 issue: 6 year: 2018 ident: 10.1016/j.pdpdt.2020.101792_bib0130 article-title: Rapid and low-cost detection of thyroid dysfunction using raman spectroscopy and an improved support vector machine publication-title: IEEE Photonics J. doi: 10.1109/JPHOT.2018.2876686 – volume: 47 start-page: 917 issue: 8 year: 2016 ident: 10.1016/j.pdpdt.2020.101792_bib0195 article-title: Different classification algorithms and serum surface enhanced Raman spectroscopy for noninvasive discrimination of gastric diseases publication-title: J. Raman Spectrosc. doi: 10.1002/jrs.4924 – volume: 200 start-page: 136 year: 2018 ident: 10.1016/j.pdpdt.2020.101792_bib0105 article-title: Raman spectral analysis for rapid screening of dengue infection publication-title: Spectrochim. Acta Part A-Molecular and Biomolecular Spectroscopy doi: 10.1016/j.saa.2018.04.018 – volume: 206 start-page: 197 year: 2019 ident: 10.1016/j.pdpdt.2020.101792_bib0110 article-title: Raman spectroscopy based differentiation of typhoid and dengue fever in infected human sera publication-title: Spectrochim. Acta Part A-Molecular and Biomolecular Spectroscopy doi: 10.1016/j.saa.2018.08.008 – volume: 91 start-page: 7054 issue: 11 year: 2019 ident: 10.1016/j.pdpdt.2020.101792_bib0115 article-title: Rapid discrimination of malaria- and dengue-infected patients sera using raman spectroscopy publication-title: Anal. Chem. doi: 10.1021/acs.analchem.8b05907 – volume: 7 start-page: 2249 issue: 6 year: 2016 ident: 10.1016/j.pdpdt.2020.101792_bib0070 article-title: Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM) publication-title: Biomed. Opt. Express doi: 10.1364/BOE.7.002249 – year: 2020 ident: 10.1016/j.pdpdt.2020.101792_bib0100 article-title: Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction publication-title: J. Biophotonics doi: 10.1002/jbio.201900099 – volume: 2016 start-page: 1 year: 2016 ident: 10.1016/j.pdpdt.2020.101792_bib0140 article-title: Raman spectroscopy in colorectal Cancer diagnostics: comparison of PCA-LDA and PLS-DA models publication-title: J. Spectrosc. doi: 10.1155/2016/1603609 – volume: 9 start-page: 2041 issue: 5 year: 2018 ident: 10.1016/j.pdpdt.2020.101792_bib0120 article-title: Analysis of hepatitis C infection using Raman spectroscopy and proximity based classification in the transformed domain publication-title: Biomed. Opt. Express doi: 10.1364/BOE.9.002041 – volume: 17 start-page: 25 issue: 1 year: 2008 ident: 10.1016/j.pdpdt.2020.101792_bib0170 article-title: Role of fat mass and adipokines in chronic kidney disease publication-title: Curr. Opin. Nephrol. Hypertens. doi: 10.1097/MNH.0b013e3282f2905f – volume: 27 start-page: 2135 issue: 7 year: 2016 ident: 10.1016/j.pdpdt.2020.101792_bib0025 article-title: CKD prevalence varies across the european general population publication-title: J. Am. Soc. Nephrol. doi: 10.1681/ASN.2015050542 – volume: 35 start-page: 230 issue: 1 year: 2019 ident: 10.1016/j.pdpdt.2020.101792_bib0185 article-title: Is there a relationship between serum vaspin levels and insulin resistance in chronic renal failure? publication-title: Pak. J. Med. Sci. doi: 10.12669/pjms.35.1.96 – volume: 33 issue: 3 year: 2019 ident: 10.1016/j.pdpdt.2020.101792_bib0015 article-title: Efficacy of different hemodialysis methods on dendritic cell marker CD40 and CD80 and platelet activation marker CD62P and P10 in patients with chronic renal failure publication-title: J. Clin. Lab. Anal. doi: 10.1002/jcla.22713 – volume: 203 year: 2020 ident: 10.1016/j.pdpdt.2020.101792_bib0145 article-title: Urine Raman spectroscopy for rapid and inexpensive diagnosis of chronic renal failure (CRF) using multiple classification algorithms publication-title: Optik doi: 10.1016/j.ijleo.2019.164043 |
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| Snippet | •Raman spectroscopy combined with multivariate statistical methods is proposed to screen chronic renal failure (CRF) patients.•The high diagnostic accuracy of... Chronic renal failure (CRF) is a symptom of kidney damage in the terminal stages. If a patient is not treated, then CRF will progress to uremia, which greatly... |
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| SubjectTerms | Algorithms Chronic renal failure (CRF) Hematologic Tests - methods Humans Kidney Failure, Chronic - diagnosis Machine Learning Neural Networks, Computer Principal Component Analysis Principal component analysis (PCA) Raman spectroscopy Sensitivity and Specificity serum Spectrum Analysis, Raman - methods Support vector machine (SVM) |
| Title | Raman spectroscopy combined with multiple algorithms for analysis and rapid screening of chronic renal failure |
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