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Sharma M, Tsai CL, Li YC, Lee CC, Hsieh YL, Chang CH, Chen SW, Chang LB. Utilizing Raman spectroscopy for urinalysis to diagnose acute kidney injury stages in cardiac surgery patients. Ren Fail 2024; 46:2375741. [PMID: 38994782 PMCID: PMC11249162 DOI: 10.1080/0886022x.2024.2375741] [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: 01/02/2024] [Accepted: 06/29/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND The successful treatment and improvement of acute kidney injury (AKI) depend on early-stage diagnosis. However, no study has differentiated between the three stages of AKI and non-AKI patients following heart surgery. This study will fill this gap in the literature and help to improve kidney disease management in the future. METHODS In this study, we applied Raman spectroscopy (RS) to uncover unique urine biomarkers distinguishing heart surgery patients with and without AKI. Given the amplified risk of renal complications post-cardiac surgery, this approach is of paramount importance. Further, we employed the partial least squares-support vector machine (PLS-SVM) model to distinguish between all three stages of AKI and non-AKI patients. RESULTS We noted significant metabolic disparities among the groups. Each AKI stage presented a distinct metabolic profile: stage 1 had elevated uric acid and reduced creatinine levels; stage 2 demonstrated increased tryptophan and nitrogenous compounds with diminished uric acid; stage 3 displayed the highest neopterin and the lowest creatinine levels. We utilized the PLS-SVM model for discriminant analysis, achieving over 90% identification rate in distinguishing AKI patients, encompassing all stages, from non-AKI subjects. CONCLUSIONS This study characterizes the incidence and risk factors for AKI after cardiac surgery. The unique spectral information garnered from this study can also pave the way for developing an in vivo RS method to detect and monitor AKI effectively.
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Affiliation(s)
- Mukta Sharma
- Graduate Institute, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taiwan
| | - Chia-Lung Tsai
- Department of Electronic Engineering, Chang Gung University, Taoyuan, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Electronic Engineering, Ming Chi University of Technology, New Taipei City, Taiwan
| | - Ying-Chang Li
- Graduate Institute, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taiwan
| | - Cheng-Chia Lee
- Department of Nephrology, Kidney Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yu-Li Hsieh
- Department of Electrical and Electronic Engineering, Chung Cheng Institute of Technology, National Defense University, Taoyuan, Taiwan
| | - Chih-Hsiang Chang
- Department of Nephrology, Kidney Research Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Shao-Wei Chen
- Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Cardiothoracic and Vascular Surgery, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Liann-Be Chang
- Department of Electronic Engineering, Chang Gung University, Taoyuan, Taiwan
- Green Technology Research Center, Chang Gung University, Taoyuan, Taiwan
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2
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Su N, Dawuti W, Hu Y, Zhao H. Noninvasive cholangitis and cholangiocarcinoma screening based on serum Raman spectroscopy and support vector machine. Photodiagnosis Photodyn Ther 2022; 40:103156. [PMID: 36252780 DOI: 10.1016/j.pdpdt.2022.103156] [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: 07/07/2022] [Revised: 09/17/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
The feasibility of serum Raman spectroscopy for rapid screening of cholangitis and cholangiocarcinoma (CCA) was explored Raman spectra were collected from 49 patients with cholangitis, 38 patients with CCA, and 55 healthy volunteers. Normalized mean Raman spectra and spectral attributions reveal disease-specific biomolecular differences. Support vector machine (SVM) was used to establish the two-way (cholangitis vs healthy, CCA vs healthy etc.) and 3-way (cholangitis vs CCA vs healthy) classification model, and leave-one-out cross-validation (LOOCV) was used to verify these models' performance. Based on the support vector machine algorithm, serum Raman spectroscopy could identify cholangitis and CCA. Its diagnostic sensitivity, and specificity were 89.80%, 94.55%, and 89.50%, 98.18%, respectively. This study demonstrates that label-free serum Raman spectroscopy analysis technique combined with SVM diagnostic algorithm has great potential for noninvasive cholangitis and CCA screening.
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Affiliation(s)
- Na Su
- Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Wubulitalifu Dawuti
- School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Yan Hu
- Science and Technology Talent Development, Center of Xinjiang Uygur Autonomous Region, Urumqi, China.
| | - Hui Zhao
- Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
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3
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Jeng MJ, Sharma M, Lee CC, Lu YS, Tsai CL, Chang CH, Chen SW, Lin RM, Chang LB. Raman Spectral Characterization of Urine for Rapid Diagnosis of Acute Kidney Injury. J Clin Med 2022; 11:jcm11164829. [PMID: 36013069 PMCID: PMC9410447 DOI: 10.3390/jcm11164829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/08/2022] [Accepted: 08/14/2022] [Indexed: 11/17/2022] Open
Abstract
Acute kidney injury (AKI) is a common syndrome characterized by various etiologies and pathophysiologic processes that deteriorate kidney function. The aim of this study is to identify potential biomarkers in the urine of non-acute kidney injury (non-AKI) and AKI patients through Raman spectroscopy (RS) to predict the advancement in complications and kidney failure. Selected spectral regions containing prominent peaks of renal biomarkers were subjected to partial least squares linear discriminant analysis (PLS-LDA). This discriminant analysis classified the AKI patients from non-AKI subjects with a sensitivity and specificity of 97% and 100%, respectively. In this study, the RS measurements of urine specimens demonstrated that AKI had significantly higher nitrogenous compounds, porphyrin, tryptophan and neopterin when compared with non-AKI. This study’s specific spectral information can be used to design an in vivo RS approach for the detection of AKI diseases.
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Affiliation(s)
- Ming-Jer Jeng
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
- Kidney Research Center, Department of Nephrology, Change Gung Memorial Hospital, Linkou Branch, Taoyuan 244, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan, Taoyuan 333, Taiwan
| | - Mukta Sharma
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
| | - Cheng-Chia Lee
- Kidney Research Center, Department of Nephrology, Change Gung Memorial Hospital, Linkou Branch, Taoyuan 244, Taiwan
| | - Yu-Sheng Lu
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
| | - Chia-Lung Tsai
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
- Kidney Research Center, Department of Nephrology, Change Gung Memorial Hospital, Linkou Branch, Taoyuan 244, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan, Taoyuan 333, Taiwan
- Correspondence:
| | - Chih-Hsiang Chang
- Kidney Research Center, Department of Nephrology, Change Gung Memorial Hospital, Linkou Branch, Taoyuan 244, Taiwan
| | - Shao-Wei Chen
- Department of Cardiothoracic and Vascular Surgery, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan 244, Taiwan
| | - Ray-Ming Lin
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
| | - Liann-Be Chang
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan, Taoyuan 333, Taiwan
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4
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Liu S, Zhang M, Lai Z, Tian H, Qiu Y, Li Z. Coral-like Magnetic Particles for Chemoselective Extraction of Anionic Metabolites. ACS APPLIED MATERIALS & INTERFACES 2022; 14:32890-32900. [PMID: 35819264 DOI: 10.1021/acsami.2c06922] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
To date, advanced chemical biology tools for chemoselective extraction of metabolites are limited. In this study, unique coral-like polymer particles were synthesized via high concentrations of 1-ethyl-3-(3-(dimethylamino)propyl) carbodiimide hydrochloride (EDC)/N-hydroxysuccinimide (NHS), which are usually used as condensation agents. The polymers can wrap or adhere Fe3O4 nanoparticles (Fe3O4-NPs) to form polymer magnetic microparticles (PMMPs). With abundant NHS-activated moieties on their surface, the coral-like PMMPs could be modified by cystamine for the chemoselective extraction of phosphate/carboxylate anion metabolites from complex biological samples. Finally, 97 metabolites including nucleotides, phosphates, phosphate sugars, carboxylate sugars, and organic acids were extracted and identified from serum, tissues, and cells. These metabolites are involved in four major metabolic pathways including glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, and nucleotide metabolism. This study has provided a cost-effective and easy-to-implement preparation of PMMPs with a robust chemoselective extraction ability and versatile applications.
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Affiliation(s)
- Shuai Liu
- Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, 5 Dongdan San Tiao, Beijing 100005, China
| | - Mo Zhang
- Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, 5 Dongdan San Tiao, Beijing 100005, China
| | - Zhizhen Lai
- Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, 5 Dongdan San Tiao, Beijing 100005, China
| | - Hongtao Tian
- Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, 5 Dongdan San Tiao, Beijing 100005, China
| | - Yuming Qiu
- Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, 5 Dongdan San Tiao, Beijing 100005, China
| | - Zhili Li
- Department of Biophysics and Structural Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, 5 Dongdan San Tiao, Beijing 100005, China
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5
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Dawuti W, Zheng X, Liu H, Zhao H, Dou J, Sun L, Chu J, Lin R, Lü G. Urine surface-enhanced Raman spectroscopy combined with SVM algorithm for rapid diagnosis of liver cirrhosis and hepatocellular carcinoma. Photodiagnosis Photodyn Ther 2022; 38:102811. [PMID: 35304310 DOI: 10.1016/j.pdpdt.2022.102811] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/09/2022] [Accepted: 03/14/2022] [Indexed: 12/12/2022]
Abstract
In this paper, we investigated the feasibility of using urine surface-enhanced Raman spectroscopy (SERS) for the rapid screening of patients with liver cirrhosis and hepatocellular carcinoma (HCC). The SERS spectra were recorded from the urine of 49 liver cirrhosis, 55 HCC, and 50 healthy volunteers using a Raman spectrometer. The normalized mean Raman spectra showed the difference of specific biomolecules associated with the illnesses, and the metabolism of specific nucleic acids and amino acids is abnormal in patients with liver cirrhosis and HCC. Based on the SVM algorithm, the urine SERS method could identify liver cirrhosis (sensitivity 88.9%, specificity 83.3%, and accuracy 85.9%) and HCC (sensitivity 85.5%, specificity 84.0%, and accuracy 84.8%). It has a higher diagnostic sensitivity for HCC than serum Alpha fetoprotein (AFP). This exploratory study showed that the urine SERS spectra combined with the SVM algorithm has indicated great potential in the noninvasive identification of liver cirrhosis and HCC.
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Affiliation(s)
- Wubulitalifu Dawuti
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China; School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xiangxiang Zheng
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Hui Liu
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Hui Zhao
- Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Jingrui Dou
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China; School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Li Sun
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Jin Chu
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Renyong Lin
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
| | - Guodong Lü
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
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6
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Carswell W, Robertson JL, Senger RS. Raman Spectroscopic Detection and Quantification of Macro- and Microhematuria in Human Urine. APPLIED SPECTROSCOPY 2022; 76:273-283. [PMID: 35102755 DOI: 10.1177/00037028211060853] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Hematuria refers to the presence of blood in urine. Even in small amounts, it may be indicative of disease, ranging from urinary tract infection to cancer. Here, Raman spectroscopy was used to detect and quantify macro- and microhematuria in human urine samples. Anticoagulated whole blood was mixed with freshly collected urine to achieve concentrations of 0, 0.25, 0.5, 1, 2, 6, 10, and 20% blood/urine (v/v). Raman spectra were obtained at 785 nm and data analyzed using chemometric methods and statistical tests with the Rametrix toolboxes for Matlab. Goldindec and iterative smoothing splines with root error adjustment (ISREA) baselining algorithms were used in processing and normalization of Raman spectra. Rametrix was used to apply principal component analysis (PCA), develop discriminate analysis of principal component (DAPC) models, and to validate these models using external leave-one-out cross-validation (LOOCV). Discriminate analysis of principal component models were capable of detecting various levels of microhematuria in unknown urine samples, with prediction accuracies of 91% (using Goldindec spectral baselining) and 94% (using ISREA baselining). Partial least squares regression (PLSR) was then used to estimate/quantify the amount of blood (v/v) in a urine sample, based on its Raman spectrum. Comparing actual and predicted (from Raman spectral computations) hematuria levels, a coefficient of determination (R2) of 0.91 was obtained over all hematuria levels (0-20% v/v), and an R2 of 0.92 was obtained for microhematuria (0-1% v/v) specifically. Overall, the results of this preliminary study suggest that Raman spectroscopy and chemometric analyses can be used to detect and quantify macro- and microhematuria in unprocessed, clinically relevant urine specimens.
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Affiliation(s)
- William Carswell
- Department of Biological Systems Engineering, 1757Virginia Tech, Blacksburg, Virginia, USA
| | - John L Robertson
- Department of Biomedical Engineering and Mechanics, 1757Virginia Tech, Blacksburg, Virginia, USA
- DialySensors, Inc., Blacksburg, Virginia, USA
| | - Ryan S Senger
- Department of Biological Systems Engineering, 1757Virginia Tech, Blacksburg, Virginia, USA
- DialySensors, Inc., Blacksburg, Virginia, USA
- Department of Chemical Engineering, 1757Virginia Tech, Blacksburg, Virginia, USA
- Department of Surgery, Virginia Commonwealth University, Richmond, Virginia, USA
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7
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Nakar A, Pistiki A, Ryabchykov O, Bocklitz T, Rösch P, Popp J. Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy. Anal Bioanal Chem 2022; 414:1481-1492. [PMID: 34982178 PMCID: PMC8761712 DOI: 10.1007/s00216-021-03800-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/05/2021] [Accepted: 11/22/2021] [Indexed: 01/08/2023]
Abstract
In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli. Our focus was on clinical multi-resistant (extended-spectrum β-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health.
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Affiliation(s)
- Amir Nakar
- Leibniz Institute of Photonic Technology Jena (a Member of Leibniz Health Technologies), Albert-Einstein-Straße 9, 07745, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743, Jena, Germany
- Research Campus Infectognostics Jena E.V, Philosophenweg 7, 07743, Jena, Germany
| | - Aikaterini Pistiki
- Leibniz Institute of Photonic Technology Jena (a Member of Leibniz Health Technologies), Albert-Einstein-Straße 9, 07745, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743, Jena, Germany
- Research Campus Infectognostics Jena E.V, Philosophenweg 7, 07743, Jena, Germany
| | - Oleg Ryabchykov
- Leibniz Institute of Photonic Technology Jena (a Member of Leibniz Health Technologies), Albert-Einstein-Straße 9, 07745, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743, Jena, Germany
| | - Thomas Bocklitz
- Leibniz Institute of Photonic Technology Jena (a Member of Leibniz Health Technologies), Albert-Einstein-Straße 9, 07745, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743, Jena, Germany
- Research Campus Infectognostics Jena E.V, Philosophenweg 7, 07743, Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743, Jena, Germany.
- Research Campus Infectognostics Jena E.V, Philosophenweg 7, 07743, Jena, Germany.
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology Jena (a Member of Leibniz Health Technologies), Albert-Einstein-Straße 9, 07745, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743, Jena, Germany
- Research Campus Infectognostics Jena E.V, Philosophenweg 7, 07743, Jena, Germany
- Jena Biophotonics and Imaging Laboratory, Albert-Einstein-Straße 9, 07745, Jena, Germany
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8
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Abstract
Regular health monitoring can result in early detection of disease, accelerate the delivery of medical care and, therefore, considerably improve patient outcomes for countless medical conditions that affect public health. A substantial unmet need remains for technologies that can transform the status quo of reactive health care to preventive, evidence-based, person-centred care. With this goal in mind, platforms that can be easily integrated into people's daily lives and identify a range of biomarkers for health and disease are desirable. However, urine - a biological fluid that is produced in large volumes every day and can be obtained with zero pain, without affecting the daily routine of individuals, and has the most biologically rich content - is discarded into sewers on a regular basis without being processed or monitored. Toilet-based health-monitoring tools in the form of smart toilets could offer preventive home-based continuous health monitoring for early diagnosis of diseases while being connected to data servers (using the Internet of Things) to enable collection of the health status of users. In addition, machine learning methods can assist clinicians to classify, quantify and interpret collected data more rapidly and accurately than they were able to previously. Meanwhile, challenges associated with user acceptance, privacy and test frequency optimization should be considered to facilitate the acceptance of smart toilets in society.
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Affiliation(s)
- Savas Tasoglu
- Department of Mechanical Engineering, Koc University, Istanbul, Turkey. .,Koç University Translational Medicine Research Center (KUTTAM), Koç University, Sarıyer, Istanbul, Turkey. .,Boğaziçi Institute of Biomedical Engineering, Boğaziçi University, Çengelköy, Istanbul, Turkey. .,Physical Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, Germany.
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9
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Scholtz A, Ramoji A, Silge A, Jansson JR, de Moura IG, Popp J, Sram JP, Armani AM. COVID-19 Diagnostics: Past, Present, and Future. ACS PHOTONICS 2021; 8:2827-2838. [PMID: 37556281 PMCID: PMC8482784 DOI: 10.1021/acsphotonics.1c01052] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/02/2021] [Accepted: 09/02/2021] [Indexed: 05/25/2023]
Abstract
In winter of 2020, SARS-CoV-2 emerged as a global threat, impacting not only health but also financial and political stability. To address the societal need for monitoring the spread of SARS-CoV-2, many existing diagnostic technologies were quickly adapted to detect SARS-CoV-2 RNA and antigens as well as the immune response, and new testing strategies were developed to accelerate time-to-decision. In parallel, the infusion of research support accelerated the development of new spectroscopic methods. While these methods have significantly reduced the impact of SARS-CoV-2 on society when coupled with behavioral changes, they also lay the groundwork for a new generation of platform technologies. With several epidemics on the horizon, such as the rise of antibiotic-resistant bacteria, the ability to quickly pivot the target pathogen of this diagnostic toolset will continue to have an impact.
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Affiliation(s)
- Alexis Scholtz
- Department of Biomedical Engineering,
University of Southern California, Los Angeles, California
90089, United States of America
| | - Anuradha Ramoji
- Institute of Physical Chemistry (IPC) and
Abbe Center of Photonics, Helmholtzweg 4, 07743 Jena,
Germany
- Leibniz Institute of Photonic Technology
(IPHT) Jena, Member of the Leibniz Research Alliance - Leibniz Health
Technologies, Albert-Einstein-Straße 9, 07745 Jena, Germany
| | - Anja Silge
- Institute of Physical Chemistry (IPC) and
Abbe Center of Photonics, Helmholtzweg 4, 07743 Jena,
Germany
- Leibniz Institute of Photonic Technology
(IPHT) Jena, Member of the Leibniz Research Alliance - Leibniz Health
Technologies, Albert-Einstein-Straße 9, 07745 Jena, Germany
- InfectoGnostics Research Campus
Jena, Centre of Applied Research, Philosophenweg 7, D-07743 Jena,
Germany
| | - Jakob R. Jansson
- Fulgent Genetics, Temple
City, California 91780, United States of America
| | - Ian G. de Moura
- Fulgent Genetics, Temple
City, California 91780, United States of America
| | - Jürgen Popp
- Institute of Physical Chemistry (IPC) and
Abbe Center of Photonics, Helmholtzweg 4, 07743 Jena,
Germany
- Leibniz Institute of Photonic Technology
(IPHT) Jena, Member of the Leibniz Research Alliance - Leibniz Health
Technologies, Albert-Einstein-Straße 9, 07745 Jena, Germany
- InfectoGnostics Research Campus
Jena, Centre of Applied Research, Philosophenweg 7, D-07743 Jena,
Germany
| | - Jakub P. Sram
- Fulgent Genetics, Temple
City, California 91780, United States of America
| | - Andrea M. Armani
- Department of Biomedical Engineering,
University of Southern California, Los Angeles, California
90089, United States of America
- Mork Family Department of Chemical Engineering,
University of Southern California, Los Angeles, California
90089, United States of America
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10
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Vuckovic I, Denic A, Charlesworth MC, Šuvakov M, Bobart S, Lieske JC, Fervenza FC, Macura S. 1H Nuclear Magnetic Resonance Spectroscopy-Based Methods for the Quantification of Proteins in Urine. Anal Chem 2021; 93:13177-13186. [PMID: 34546699 DOI: 10.1021/acs.analchem.1c01618] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
We described several postprocessing methods to measure protein concentrations in human urine from existing 1H nuclear magnetic resonance (NMR) metabolomic spectra: (1) direct spectral integration, (2) integration of NCD spectra (NCD = 1D NOESY-CPMG), (3) integration of SMolESY-filtered 1D NOESY spectra (SMolESY = Small Molecule Enhancement SpectroscopY), (4) matching protein patterns, and (5) TSP line integral and TSP linewidth. Postprocessing consists of (a) removal of the metabolite signals (demetabolization) and (b) extraction of the protein integral from the demetabolized spectra. For demetabolization, we tested subtraction of the spin-echo 1D spectrum (CPMG) from the regular 1D spectrum and low-pass filtering of 1D NOESY by its derivatives (c-SMolESY). Because of imperfections in the demetabolization, in addition to direct integration, we extracted protein integrals by the piecewise comparison of demetabolized spectra with the reference spectrum of albumin. We analyzed 42 urine samples with protein content known from the bicinchoninic acid (BCA) assay. We found excellent correlation between the BCA assay and the demetabolized NMR integrals. We have provided conversion factors for calculating protein concentrations in mg/mL from spectral integrals in mM. Additionally, we found the trimethylsilyl propionate (TSP, NMR standard) spectral linewidth and the TSP integral to be good indicators of protein concentration. The described methods increase the information content of urine NMR metabolomics spectra by informing clinical studies of protein concentration.
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Affiliation(s)
- Ivan Vuckovic
- Metabolomics Core, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Aleksandar Denic
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota 55905, United States
| | | | - Milovan Šuvakov
- Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Shane Bobart
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - John C Lieske
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Fernando C Fervenza
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Slobodan Macura
- Metabolomics Core, Mayo Clinic, Rochester, Minnesota 55905, United States.,Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota 55905, United States
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11
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Houhou R, Rösch P, Popp J, Bocklitz T. Comparison of functional and discrete data analysis regimes for Raman spectra. Anal Bioanal Chem 2021; 413:5633-5644. [PMID: 33990853 PMCID: PMC8410698 DOI: 10.1007/s00216-021-03360-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/13/2021] [Accepted: 04/16/2021] [Indexed: 11/28/2022]
Abstract
Raman spectral data are best described by mathematical functions; however, due to the spectroscopic measurement setup, only discrete points of these functions are measured. Therefore, we investigated the Raman spectral data for the first time in the functional framework. First, we approximated the Raman spectra by using B-spline basis functions. Afterwards, we applied the functional principal component analysis followed by the linear discriminant analysis (FPCA-LDA) and compared the results with those of the classical principal component analysis followed by the linear discriminant analysis (PCA-LDA). In this context, simulation and experimental Raman spectra were used. In the simulated Raman spectra, normal and abnormal spectra were used for a classification model, where the abnormal spectra were built by shifting one peak position. We showed that the mean sensitivities of the FPCA-LDA method were higher than the mean sensitivities of the PCA-LDA method, especially when the signal-to-noise ratio is low and the shift of the peak position is small. However, for a higher signal-to-noise ratio, both methods performed equally. Additionally, a slight improvement of the mean sensitivity could be shown if the FPCA-LDA method was applied to experimental Raman data.
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Affiliation(s)
- Rola Houhou
- Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743, Jena, Germany.,Department of Photonic Data Science, Leibniz Institute of Photonic Technologies, Member of Leibniz Research Alliance "Leibniz-Health Technologies", Albert-Einstein-Str. 9, 07745, Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743, Jena, Germany
| | - Jürgen Popp
- Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743, Jena, Germany.,Department of Photonic Data Science, Leibniz Institute of Photonic Technologies, Member of Leibniz Research Alliance "Leibniz-Health Technologies", Albert-Einstein-Str. 9, 07745, Jena, Germany
| | - Thomas Bocklitz
- Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743, Jena, Germany. .,Department of Photonic Data Science, Leibniz Institute of Photonic Technologies, Member of Leibniz Research Alliance "Leibniz-Health Technologies", Albert-Einstein-Str. 9, 07745, Jena, Germany.
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Resolving complex phenotypes with Raman spectroscopy and chemometrics. Curr Opin Biotechnol 2020; 66:277-282. [DOI: 10.1016/j.copbio.2020.09.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 09/10/2020] [Accepted: 09/15/2020] [Indexed: 12/30/2022]
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Rapid detection of the aspergillosis biomarker triacetylfusarinine C using interference-enhanced Raman spectroscopy. Anal Bioanal Chem 2020; 412:6351-6360. [PMID: 32170382 PMCID: PMC7442771 DOI: 10.1007/s00216-020-02571-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 02/20/2020] [Accepted: 03/02/2020] [Indexed: 11/02/2022]
Abstract
Triacetylfusarinine C (TAFC) is a siderophore produced by certain fungal species and might serve as a highly useful biomarker for the fast diagnosis of invasive aspergillosis. Due to its renal elimination, the biomarker is found in urine samples of patients suffering from Aspergillus infections. Accordingly, non-invasive diagnosis from this easily obtainable body fluid is possible. Within our contribution, we demonstrate how Raman microspectroscopy enables a sensitive and specific detection of TAFC. We characterized the TAFC iron complex and its iron-free form using conventional and interference-enhanced Raman spectroscopy (IERS) and compared the spectra with the related compound ferrioxamine B, which is produced by bacterial species. Even though IERS only offers a moderate enhancement of the Raman signal, the employment of respective substrates allowed lowering the detection limit to reach the clinically relevant range. The achieved limit of detection using IERS was 0.5 ng of TAFC, which is already well within the clinically relevant range. By using an extraction protocol, we were able to detect 1.4 μg/mL TAFC via IERS from urine within less than 3 h including sample preparation and data analysis. We could further show that TAFC and ferrioxamine B can be clearly distinguished by means of their Raman spectra even in very low concentrations.
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