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Xie X, Yu W, Wang L, Yang J, Tu X, Liu X, Liu S, Zhou H, Chi R, Huang Y. SERS-based AI diagnosis of lung and gastric cancer via exhaled breath. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 314:124181. [PMID: 38527410 DOI: 10.1016/j.saa.2024.124181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 03/13/2024] [Accepted: 03/20/2024] [Indexed: 03/27/2024]
Abstract
Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.
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Affiliation(s)
- Xin Xie
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Wenrou Yu
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Li Wang
- School of Optoelectronics Engineering, Chongqing University, Chongqing 401331, China
| | - Junjun Yang
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Xiaobin Tu
- Department of Oncology and Department of Hematology, Chongqing Wulong People's Hospital, Chongqing 408500, China
| | - Xiaochun Liu
- Department of Oncology and Department of Hematology, Chongqing Wulong People's Hospital, Chongqing 408500, China
| | - Shihong Liu
- Department of Geriatric Oncology and Department of Palliative Care, Chongqing University Cancer Hospital, Chongqing 400030, China.
| | - Han Zhou
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Runwei Chi
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Yingzhou Huang
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China.
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Matthies L, Gebrekidan MT, Braeuer AS, Friedrich RE, Stelzle F, Schmidt C, Smeets R, Assaf AT, Gosau M, Rolvien T, Knipfer C. Raman spectroscopy and U-Net deep neural network in antiresorptive drug-related osteonecrosis of the jaw. Oral Dis 2024; 30:2439-2452. [PMID: 37650266 DOI: 10.1111/odi.14721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 07/30/2023] [Accepted: 08/14/2023] [Indexed: 09/01/2023]
Abstract
OBJECTIVE Application of an optical method for the identification of antiresorptive drug-related osteonecrosis of the jaw (ARONJ). METHODS We introduce shifted-excitation Raman difference spectroscopy followed by U-Net deep neural network refinement to determine bone tissue viability. The obtained results are validated through established histological methods. RESULTS Discrimination of osteonecrosis from physiological tissues was evaluated at 119 distinct measurement loci in 40 surgical specimens from 28 patients. Mean Raman spectra were refined from 11,900 raw spectra, and characteristic peaks were assigned to their respective molecular origin. Then, following principal component and linear discriminant analyses, osteonecrotic lesions were distinguished from physiological tissue entities, such as viable bone, with a sensitivity, specificity, and overall accuracy of 100%. Moreover, bone mineral content, quality, maturity, and crystallinity were quantified, revealing an increased mineral-to-matrix ratio and decreased carbonate-to-phosphate ratio in ARONJ lesions compared to physiological bone. CONCLUSION The results demonstrate feasibility with high classification accuracy in this collective. The differentiation was determined by the spectral features of the organic and mineral composition of bone. This merely optical, noninvasive technique is a promising candidate to ameliorate both the diagnosis and treatment of ARONJ in the future.
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Affiliation(s)
- Levi Matthies
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Mildred Scheel Cancer Career Center HaTriCS4, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Medhanie T Gebrekidan
- Institute of Thermal-, Environmental- and Resources' Process Engineering (ITUN), Technische Universität Bergakademie Freiberg (TUBAF), Freiberg, Germany
| | - Andreas S Braeuer
- Institute of Thermal-, Environmental- and Resources' Process Engineering (ITUN), Technische Universität Bergakademie Freiberg (TUBAF), Freiberg, Germany
| | - Reinhard E Friedrich
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Florian Stelzle
- Department of Oral and Maxillofacial Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Constantin Schmidt
- Division of Orthopedics, Department of Trauma and Orthopedic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ralf Smeets
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Division of "Regenerative Orofacial Medicine", Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexandre T Assaf
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Martin Gosau
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tim Rolvien
- Division of Orthopedics, Department of Trauma and Orthopedic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Knipfer
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Zheng X, Li J, Lü G, Li X, Lü X, Wu G, Xu L. Machine learning-assisted serum SERS strategy for rapid and non-invasive screening of early cystic echinococcosis. JOURNAL OF BIOPHOTONICS 2024; 17:e202300376. [PMID: 38163898 DOI: 10.1002/jbio.202300376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/11/2023] [Accepted: 12/17/2023] [Indexed: 01/03/2024]
Abstract
Early and accurate diagnosis of cystic echinococcosis (CE) with existing technologies is still challenging. Herein, we proposed a novel strategy based on the combination of label-free serum surface-enhanced Raman scattering (SERS) spectroscopy and machine learning for rapid and non-invasive diagnosis of early-stage CE. Specifically, by establishing early- and middle-stage mouse models, the corresponding CE-infected and normal control serum samples were collected, and silver nanoparticles (AgNPs) were utilized as the substrate to obtain SERS spectra. The early- and middle-stage discriminant models were developed using a support vector machine, with diagnostic accuracies of 91.7% and 95.7%, respectively. Furthermore, by analyzing the serum SERS spectra, some biomarkers that may be related to early CE were found, including purine metabolites and protein-related amide bands, which was consistent with other biochemical studies. Thus, our findings indicate that label-free serum SERS analysis is a potential early-stage CE detection method that is promising for clinical translation.
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Affiliation(s)
- Xiangxiang Zheng
- Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, China
| | - Jintian Li
- School of Public Healthy, Xinjiang Medical University, Urumqi, China
| | - Guodong Lü
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiaojing Li
- Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, China
| | - Xiaoyi Lü
- School of Software, Xinjiang University, Urumqi, China
| | - Guohua Wu
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Liang Xu
- Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems, School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, China
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Kralova K, Kral M, Vrtelka O, Setnicka V. Comparative study of Raman spectroscopy techniques in blood plasma-based clinical diagnostics: A demonstration on Alzheimer's disease. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123392. [PMID: 37716043 DOI: 10.1016/j.saa.2023.123392] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/26/2023] [Accepted: 09/08/2023] [Indexed: 09/18/2023]
Abstract
Nowadays, there are still many diseases with limited or no reliable methods of early diagnosis. A popular approach in clinical diagnostic research is Raman spectroscopy, as a relatively simple, cost-effective, and high-throughput method for searching for disease-specific alterations in the composition of blood plasma. However, the high variability of the experimental designs, targeted diseases, or statistical processing in the individual studies makes it challenging to compare and compile the results to critically assess the applicability of Raman spectroscopy in real clinical practice. This study aimed to compare data from a single series of blood plasma samples of patients with Alzheimer's disease and non-demented elderly controls obtained by four different techniques/experimental setups - Raman spectroscopy with excitation at 532 and 785 nm, Raman optical activity, and surface-enhanced Raman scattering spectroscopy. The obtained results showed that the spectra from each Raman spectroscopy technique contain different information about biomolecules of blood plasma or their conformation and may, therefore, offer diverse points of view on underlying biochemical processes of the disease. The classification models based on the datasets generated by the three non-chiroptical variants of Raman spectroscopy exhibited comparable diagnostic performance, all reaching an accuracy close to or equal to 80%. Raman optical activity achieved only 60% classification accuracy, suggesting its limited applicability in the specific case of Alzheimer's disease diagnostics. The described differences in the outputs of the four utilized techniques/setups of Raman spectroscopy imply that their choice may crucially affect the acquired results and thus should be approached carefully concerning the specific purpose.
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Affiliation(s)
- Katerina Kralova
- Department of Analytical Chemistry, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Martin Kral
- Department of Physical Chemistry, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Ondrej Vrtelka
- Department of Analytical Chemistry, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Technická 5, 166 28 Prague 6, Czech Republic
| | - Vladimir Setnicka
- Department of Analytical Chemistry, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Technická 5, 166 28 Prague 6, Czech Republic.
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Yang M, Wang J, Quan S, Xu Q. High-precision bladder cancer diagnosis method: 2D Raman spectrum figures based on maintenance technology combined with automatic weighted feature fusion network. Anal Chim Acta 2023; 1282:341908. [PMID: 37923405 DOI: 10.1016/j.aca.2023.341908] [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: 06/07/2023] [Revised: 08/28/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Raman spectroscopy has been extensively utilized as a marker-free detection method in the complementary diagnosis of cancer. Multivariate statistical classification analysis is frequently employed for Raman spectral data classification. Nevertheless, traditional multivariate statistical classification analysis performs poorly when analyzing large samples and multicategory spectral data. In addition, with the advancement of computer vision, convolutional neural networks (CNNs) have demonstrated extraordinarily precise analysis of two-dimensional image processing. RESULT Combining 2D Raman spectrograms with automatic weighted feature fusion network (AWFFN) for bladder cancer detection is presented in this paper. Initially, the s-transform (ST) is implemented for the first time to convert 1D Raman data into 2D spectrograms, achieving 99.2% detection accuracy. Second, four upscaling techniques, including short time fourier transform (STFT), recurrence map (RP), markov transform field (MTF), and grammy angle field (GAF), were used to transform the 1D Raman spectral data into a variety of 2D Raman spectrograms. In addition, a particle swarm optimization (PSO) algorithm is combined with VGG19, ResNet50, and ResNet101 to construct a weighted feature fusion network, and this parallel network is employed for evaluating multiple spectrograms. Class activation mapping (CAM) is additionally employed to illustrate and evaluate the process of feature extraction via the three parallel network branches. The results demonstrate that the combination of a 2D Raman spectrogram along with a CNN for the diagnosis of bladder cancer obtains a 99.2% accuracy rate,which indicates that it is an extremely promising auxiliary technology for cancer diagnosis. SIGNIFICANCE The proposed two-dimensional Raman spectroscopy method has an improved precision than one-dimensional spectroscopic data, which presents a potential methodology for assisted cancer detection and providing crucial technical support for assisted diagnosis.
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Affiliation(s)
- Mengge Yang
- School of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Jiajia Wang
- School of Information Science and Engineering, Xinjiang University, Urumqi, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, China; Post-doctoral Workstation of Xinjiang Uygur Autonomous Region Institute of Product Quality Supervision and Inspection, Urumqi, China.
| | - Siyu Quan
- School of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Qiqi Xu
- School of Information Science and Engineering, Xinjiang University, Urumqi, China
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Xie X, Yu W, Chen Z, Wang L, Yang J, Liu S, Li L, Li Y, Huang Y. Early-stage oral cancer diagnosis by artificial intelligence-based SERS using Ag NWs@ZIF core-shell nanochains. NANOSCALE 2023; 15:13466-13472. [PMID: 37548371 DOI: 10.1039/d3nr02662k] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has great potential in the early diagnosis of diseases by detecting the changes of volatile biomarkers in exhaled breath, because of its high sensitivity, rich chemical molecular fingerprint information, and immunity to humidity. Here, an accurate diagnosis of oral cancer (OC) is demonstrated using artificial intelligence (AI)-based SERS of exhaled breath in plasmonic-metal organic framework (MOF) nanoparticles. These plasmonic-MOF nanoparticles were prepared using a zeolitic imidazolate framework coated on Ag nanowires (Ag NWs@ZIF), which offers Raman enhancement from the plasmonic nanowires and gas enrichment from the ZIF shells. Then, the core-shell nanochains of Ag NWs@ZIF prepared with 0.5 mL Ag NWs were selected to capture gaseous methanethiol, which is a tumor biomarker, from the exhalation of OC patients. The substrate was used to collect a total of 400 SERS spectra of exhaled breath of simulated healthy people and simulated OC patients. The artificial neural network (ANN) model in the AI algorithm was trained with these SERS spectra and could classify them with an accuracy of 99%. Notably, the model predicted OC with an area under the curve (AUC) of 0.996 for the simulated OC breath samples. This work suggests the great potential of the combination of breath analysis and AI as a method for the early-stage diagnosis of oral cancer.
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Affiliation(s)
- Xin Xie
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 400044, China.
| | - Wenrou Yu
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 400044, China.
| | - Zhaoxian Chen
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 400044, China.
| | - Li Wang
- School of Optoelectronics Engineering, Chongqing University, Chongqing 401331, China
| | - Junjun Yang
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 400044, China.
| | - Shihong Liu
- Department of Geriatric Oncology and Department of Palliative Care, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Linze Li
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 400044, China.
| | - Yanxi Li
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 400044, China.
| | - Yingzhou Huang
- State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 400044, China.
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Tuli HS, Joshi R, Kaur G, Garg VK, Sak K, Varol M, Kaur J, Alharbi SA, Alahmadi TA, Aggarwal D, Dhama K, Jaswal VS, Mittal S, Sethi G. Metal nanoparticles in cancer: from synthesis and metabolism to cellular interactions. JOURNAL OF NANOSTRUCTURE IN CHEMISTRY 2023; 13:321-348. [DOI: 10.1007/s40097-022-00504-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/23/2022] [Indexed: 07/28/2024]
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Lin J, Lin D, Qiu S, Huang Z, Liu F, Huang W, Xu Y, Zhang X, Feng S. Shifted-excitation Raman difference spectroscopy for improving in vivo detection of nasopharyngeal carcinoma. Talanta 2023; 257:124330. [PMID: 36773510 DOI: 10.1016/j.talanta.2023.124330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/10/2023]
Abstract
A strong fluorescence background is one of the common interference factors of Raman spectroscopic analysis in biological tissue. This study developed an endoscopic shifted-excitation Raman difference spectroscopy (SERDS) system for real-time in vivo detection of nasopharyngeal carcinoma (NPC) for the first time. Owing to the use of the SERDS method, the high-quality Raman signals of nasopharyngeal tissue could be well extracted and characterized from the complex raw spectra by removing the fluorescence interference signals. Significant spectral differences relating to proteins, phospholipids, glucose, and DNA were found between 42 NPC and 42 normal tissue sites. Using linear discriminant analysis, the diagnostic accuracy of SERDS for NPC detection was 100%, which was much higher than that of raw Raman spectroscopy (75.0%), showing the great potential of SERDS for improving the accurate in vivo detection of NPC.
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Affiliation(s)
- Jinyong Lin
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China; Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350007, China
| | - Duo Lin
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350007, China
| | - Sufang Qiu
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Zufang Huang
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350007, China.
| | - Feng Liu
- Simple & Smart Instrument (Beijing) Co.,Ltd, China
| | - Wei Huang
- Department of Forensic Science, Fujian Police College, Fuzhou, 350007, PR China
| | - Yuanji Xu
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Xianzeng Zhang
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350007, China.
| | - Shangyuan Feng
- Key Laboratory of OptoElectronic Science and Technology for Medicine, Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350007, China.
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Cheng Z, Li H, Chen C, Lv X, Zuo E, Han S, Li Z, Liu P, Li H, Chen C. Application of serum SERS technology based on thermally annealed silver nanoparticle composite substrate in breast cancer. Photodiagnosis Photodyn Ther 2023; 41:103284. [PMID: 36646366 DOI: 10.1016/j.pdpdt.2023.103284] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/24/2022] [Accepted: 01/09/2023] [Indexed: 01/15/2023]
Abstract
Liquid biopsy is currently a non-destructive and convenient method of cancer screening, due to human blood containing a variety of cancer-related biomolecules. Therefore, the development of an accurate and rapid breast cancer screening technique combined with breast cancer serum is crucial for the treatment and prognosis of breast cancer patients. In this study, the surface enhanced Raman spectroscopy (SERS) technique is used to enhance the Raman spectroscopy (RS) signal of serum based on a high sensitivity thermally annealed silver nanoparticle/porous silicon bragg mirror (AgNPs/PSB) composite substrate. Compared with RS, SERS reflects more and stronger spectral peak information, which is beneficial to discover new biomarkers of breast cancer. At the same time, to further explore the diagnostic ability of SERS technology for breast cancer. In this study, the raw spectral data are processed by baseline correction, polynomial smoothing, and normalization. Then, the relevant feature information of SERS and RS is extracted by principal component analysis (PCA), and five classification models are established to compare the diagnostic performance of SERS and RS models respectively. The experimental results show that the breast cancer diagnosis model based on the improved SERS substrate combined with the machine learning algorithm can be used to distinguish breast cancer patients from controls. The accuracy, sensitivity, specificity and AUC values of the SVM model are 100%, 100%, 100% and 100%, respectively, as well as the training time of 4ms. The above experimental results show that the SERS technology based on AgNPs/PSB composite substrate, combined with machine learning methods, has great potential in the rapid and accurate identification of breast cancer patients.
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Affiliation(s)
- Zhiyuan Cheng
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Hongyi Li
- Guangzhou Panyu Polytechnic, No. 1342 Shiliang Road, Guangzhou Panyu 511483, Guangdong, China
| | - Chen Chen
- College of Information Science, Engineering Xinjiang University, Urumqi 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China
| | - EnGuang Zuo
- College of Information Science, Engineering Xinjiang University, Urumqi 830046, China
| | - Shibin Han
- School of Physical Science and Technology, Xinjiang University, Urumqi 830046, China
| | - Zhongyuan Li
- College of Information Science, Engineering Xinjiang University, Urumqi 830046, China
| | - Pei Liu
- College of Information Science, Engineering Xinjiang University, Urumqi 830046, China
| | - Hongtao Li
- Xinjiang Medical University Affiliated Tumor Hospital, Urumqi 830054, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
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Liang H, Shi R, Wang H, Zhou Y. Advances in the application of Raman spectroscopy in haematological tumours. Front Bioeng Biotechnol 2023; 10:1103785. [PMID: 36704299 PMCID: PMC9871369 DOI: 10.3389/fbioe.2022.1103785] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 12/29/2022] [Indexed: 01/12/2023] Open
Abstract
Hematologic malignancies are a diverse collection of cancers that affect the blood, bone marrow, and organs. They have a very unpredictable prognosis and recur after treatment. Leukemia, lymphoma, and myeloma are the most prevalent symptoms. Despite advancements in chemotherapy and supportive care, the incidence rate and mortality of patients with hematological malignancies remain high. Additionally, there are issues with the clinical diagnosis because several hematological malignancies lack defined, systematic diagnostic criteria. This work provided an overview of the fundamentals, benefits, and limitations of Raman spectroscopy and its use in hematological cancers. The alterations of trace substances can be recognized using Raman spectroscopy. High sensitivity, non-destructive, quick, real-time, and other attributes define it. Clinicians must promptly identify disorders and keep track of analytes in biological fluids. For instance, surface-enhanced Raman spectroscopy is employed in diagnosing gene mutations in myelodysplastic syndromes due to its high sensitivity and multiple detection benefits. Serum indicators for multiple myeloma have been routinely used for detection. The simultaneous observation of DNA strand modifications and the production of new molecular bonds by tip-enhanced Raman spectroscopy is of tremendous significance for diagnosing lymphoma and multiple myeloma with unidentified diagnostic criteria.
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Affiliation(s)
- Haoyue Liang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Ruxue Shi
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Haoyu Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Yuan Zhou
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China,*Correspondence: Yuan Zhou,
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11
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Kowalska AA, Czaplicka M, Nowicka AB, Chmielewska I, Kędra K, Szymborski T, Kamińska A. Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy. Biomedicines 2022; 10:biomedicines10050993. [PMID: 35625729 PMCID: PMC9138770 DOI: 10.3390/biomedicines10050993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/20/2022] [Accepted: 04/23/2022] [Indexed: 11/22/2022] Open
Abstract
We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.
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Affiliation(s)
- Aneta Aniela Kowalska
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland; (M.C.); (A.B.N.); (K.K.); (T.S.)
- Correspondence: (A.A.K.); (A.K.)
| | - Marta Czaplicka
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland; (M.C.); (A.B.N.); (K.K.); (T.S.)
| | - Ariadna B. Nowicka
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland; (M.C.); (A.B.N.); (K.K.); (T.S.)
| | - Izabela Chmielewska
- Department of Pneumonology, Oncology and Allergology, Medical University of Lublin, Jaczewskiego 8, 20-090 Lublin, Poland;
| | - Karolina Kędra
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland; (M.C.); (A.B.N.); (K.K.); (T.S.)
| | - Tomasz Szymborski
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland; (M.C.); (A.B.N.); (K.K.); (T.S.)
| | - Agnieszka Kamińska
- Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland; (M.C.); (A.B.N.); (K.K.); (T.S.)
- Correspondence: (A.A.K.); (A.K.)
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Fornasaro S, Sergo V, Bonifacio A. The key role of ergothioneine in label‐free surface‐enhanced Raman scattering spectra of biofluids: a retrospective re‐assessment of the literature. FEBS Lett 2022; 596:1348-1355. [DOI: 10.1002/1873-3468.14312] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/21/2022] [Accepted: 02/02/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Stefano Fornasaro
- Raman Spectroscopy Lab Department of Engineering and Architecture University of Trieste 34127 Trieste Italy
| | - Valter Sergo
- Raman Spectroscopy Lab Department of Engineering and Architecture University of Trieste 34127 Trieste Italy
- Health Sciences Dept University of Macau SAR Macau China
| | - Alois Bonifacio
- Raman Spectroscopy Lab Department of Engineering and Architecture University of Trieste 34127 Trieste Italy
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