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Miao Y, Wu L, Qiang J, Qi J, Li Y, Li R, Kong X, Zhang Q. The application of Raman spectroscopy for the diagnosis and monitoring of lung tumors. Front Bioeng Biotechnol 2024; 12:1385552. [PMID: 38699434 PMCID: PMC11063270 DOI: 10.3389/fbioe.2024.1385552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/09/2024] [Indexed: 05/05/2024] Open
Abstract
Raman spectroscopy is an optical technique that uses inelastic light scattering in response to vibrating molecules to produce chemical fingerprints of tissues, cells, and biofluids. Raman spectroscopy strategies produce high levels of chemical specificity without requiring extensive sample preparation, allowing for the use of advanced optical tools such as microscopes, fiber optics, and lasers that operate in the visible and near-infrared spectral range, making them increasingly suitable for a wide range of medical diagnostic applications. Metal nanoparticles and nonlinear optical effects can improve Raman signals, and optimized fiber optic Raman probes can make real-time, in vivo, single-point observations. Furthermore, diagnostic speed and spatial accuracy can be improved through the multimodal integration of Raman measurements and other technologies. Recent studies have significantly contributed to the improvement of diagnostic speed and accuracy, making them suitable for clinical application. Lung cancer is a prevalent type of respiratory malignancy. However, the use of computed tomography for detection and screening frequently reveals numerous smaller lung nodules, which makes the diagnostic process more challenging from a clinical perspective. While the majority of small nodules detected are benign, there are currently no direct methods for identifying which nodules represent very early-stage lung cancer. Positron emission tomography and other auxiliary diagnostic methods for non-surgical biopsy samples from these small nodules yield low detection rates, which might result in significant expenses and the possibility of complications for patients. While certain subsets of patients can undergo curative treatment, other individuals have a less favorable prognosis and need alternative therapeutic interventions. With the emergence of new methods for treating cancer, such as immunotherapies, which can potentially extend patient survival and even lead to a complete cure in certain instances, it is crucial to determine the most suitable biomarkers and metrics for assessing the effectiveness of these novel compounds. This will ensure that significant treatment outcomes are accurately measured. This review provides a comprehensive overview of the prospects of Raman spectroscopy and its applications in the diagnosis and analysis of lung tumors.
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Affiliation(s)
| | | | | | | | | | | | | | - Qiang Zhang
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
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Bao H, Hackshaw KV, Castellvi SDL, Wu Y, Gonzalez CM, Nuguri SM, Yao S, Goetzman CM, Schultz ZD, Yu L, Aziz R, Osuna-Diaz MM, Sebastian KR, Giusti MM, Rodriguez-Saona L. Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics. Biomedicines 2024; 12:133. [PMID: 38255238 PMCID: PMC10813180 DOI: 10.3390/biomedicines12010133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 12/28/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm-1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.
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Affiliation(s)
- Haona Bao
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Kevin V. Hackshaw
- Department of Internal Medicine, Division of Rheumatology, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA
| | - Silvia de Lamo Castellvi
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
- Departament d’Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain
| | - Yalan Wu
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Celeste Matos Gonzalez
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Shreya Madhav Nuguri
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Siyu Yao
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
- Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, Nanjing 210009, China
| | - Chelsea M. Goetzman
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, USA; (C.M.G.); (Z.D.S.)
- Savannah River National Laboratory, Jackson, SC 29831, USA
| | - Zachary D. Schultz
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, USA; (C.M.G.); (Z.D.S.)
| | - Lianbo Yu
- Center of Biostatistics and Bioinformatics, The Ohio State University, Columbus, OH 43210, USA;
| | - Rija Aziz
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (K.R.S.)
| | - Michelle M. Osuna-Diaz
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (K.R.S.)
| | - Katherine R. Sebastian
- Department of Internal Medicine, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA; (R.A.); (M.M.O.-D.); (K.R.S.)
| | - Monica M. Giusti
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
| | - Luis Rodriguez-Saona
- Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA; (H.B.); (S.d.L.C.); (Y.W.); (C.M.G.); (S.M.N.); (S.Y.); (M.M.G.); (L.R.-S.)
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