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Wang X, Hou J, Chen C, Jia Z, Zuo E, Chang C, Huang Y, Chen C, Lv X. Non-invasive detection of systemic lupus erythematosus using SERS serum detection technology and deep learning algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 320:124592. [PMID: 38861826 DOI: 10.1016/j.saa.2024.124592] [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: 03/13/2024] [Revised: 05/21/2024] [Accepted: 06/02/2024] [Indexed: 06/13/2024]
Abstract
Systemic lupus erythematosus (SLE) is an autoimmune disease with multiple symptoms, and its rapid screening is the research focus of surface-enhanced Raman scattering (SERS) technology. In this study, gold@silver-porous silicon (Au@Ag-PSi) composite substrates were synthesized by electrochemical etching and in-situ reduction methods, which showed excellent sensitivity and accuracy in the detection of rhodamine 6G (R6G) and serum from SLE patients. SERS technology was combined with deep learning algorithms to model serum features using selected CNN, AlexNet, and RF models. 92 % accuracy was achieved in classifying SLE patients by CNN models, and the reliability of these models in accurately identifying sera was verified by ROC curve analysis. This study highlights the great potential of Au@Ag-PSi substrate in SERS detection and introduces a novel deep learning approach for SERS for accurate screening of SLE. The proposed method and composite substrate provide significant value for rapid, accurate, and noninvasive SLE screening and provide insights into SERS-based diagnostic techniques.
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Affiliation(s)
- Xuehua Wang
- College of Physical Science and Technology, Xinjiang University, Urumqi 830046, China.
| | - Junwei Hou
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum-Beijing at Karamay, Karamay 834000, China.
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| | - Zhenhong Jia
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| | - Enguang Zuo
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| | - Chenjie Chang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
| | - Yuhao Huang
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China.
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2
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Li X, Zhou S, Wang F, Fu L. An improved sparrow search algorithm and CNN-BiLSTM neural network for predicting sea level height. Sci Rep 2024; 14:4560. [PMID: 38402324 PMCID: PMC10894254 DOI: 10.1038/s41598-024-55266-4] [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: 10/09/2023] [Accepted: 02/21/2024] [Indexed: 02/26/2024] Open
Abstract
Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea level prediction is challenging. To improve the accuracy of sea level prediction, this paper uses a new swarm intelligence algorithm named the sparrow search algorithm (SSA), which can imitate the foraging behavior and antipredation behavior of sparrows, to determine optimal solutions. To avoid the algorithm falling into a local optimal situation, this paper integrates the sine-cosine algorithm and the Cauchy variation strategy into the SSA to obtain an algorithm named the SCSSA. The SCSSA is used to optimize the parameter values of the CNN-BiLSTM (convolutional neural network combined with bidirectional long short-term memory neural network) model; finally, a combined neural network model (named SCSSA-CNN-BiLSTM) is proposed. In this paper, the time series data of seven tidal stations located in coastal China are used for experimental analysis. First, the SCSSA-CNN-BiLSTM model is compared with the CNN-BiLSTM model to predict the time series data of SHANWEI Station. With respect to the training and test sets of data, the SCSSA-CNN-BiLSTM model outperforms the other models on all the evaluation metrics. In addition, the remaining six tide station datasets and five neural network models, including the SCSSA-CNN-BiLSTM model, are used to further study the performance of the proposed prediction model. Four evaluation indices including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) are adopted. For six stations, the RMSE, MAE, MAPE and R2 of SCSSA-CNN-BiLSTM model are ranged from 20.9217 ~ 27.8427 mm, 9.4770 ~ 17.8603 mm, 0.1322% ~ 0.2482% and 0.9119 ~ 0.9759, respectively. The experimental analysis results show that the SCSSA-CNN-BiLSTM model makes effective predictions at all stations, and the prediction performance is better than that of the other models. Even though the combination of SCSSA algorithm may increase the complexity of the model, indeed the proposed model is a new prediction method with good accuracy and robustness for predicting sea level change.
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Affiliation(s)
- Xiao Li
- School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang, 330013, China
- Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang, 330013, China
| | - Shijian Zhou
- Nanchang Hangkong University, Nanchang, 330063, China.
| | - Fengwei Wang
- State Key Laboratory of Marine Geology, Tongji University, Shanghai, 200092, China
| | - Laiying Fu
- School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang, 330013, China
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3
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Yang J, Chen X, Luo C, Li Z, Chen C, Han S, Lv X, Wu L, Chen C. Application of serum SERS technology combined with deep learning algorithm in the rapid diagnosis of immune diseases and chronic kidney disease. Sci Rep 2023; 13:15719. [PMID: 37735599 PMCID: PMC10514316 DOI: 10.1038/s41598-023-42719-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 09/14/2023] [Indexed: 09/23/2023] Open
Abstract
Surface-enhanced Raman spectroscopy (SERS), as a rapid, non-invasive and reliable spectroscopic detection technique, has promising applications in disease screening and diagnosis. In this paper, an annealed silver nanoparticles/porous silicon Bragg reflector (AgNPs/PSB) composite SERS substrate with high sensitivity and strong stability was prepared by immersion plating and heat treatment using porous silicon Bragg reflector (PSB) as the substrate. The substrate combines the five deep learning algorithms of the improved AlexNet, ResNet, SqueezeNet, temporal convolutional network (TCN) and multiscale fusion convolutional neural network (MCNN). We constructed rapid screening models for patients with primary Sjögren's syndrome (pSS) and healthy controls (HC), diabetic nephropathy patients (DN) and healthy controls (HC), respectively. The results showed that the annealed AgNPs/PSB composite SERS substrates performed well in diagnosing. Among them, the MCNN model had the best classification effect in the two groups of experiments, with an accuracy rate of 94.7% and 92.0%, respectively. Previous studies have indicated that the AgNPs/PSB composite SERS substrate, combined with machine learning algorithms, has achieved promising classification results in disease diagnosis. This study shows that SERS technology based on annealed AgNPs/PSB composite substrate combined with deep learning algorithm has a greater developmental prospect and research value in the early identification and screening of immune diseases and chronic kidney disease, providing reference ideas for non-invasive and rapid clinical medical diagnosis of patients.
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Affiliation(s)
- Jie Yang
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Xiaomei Chen
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001, China
- Xinjiang Clinical Research Center for Rheumatoid arthritis, Urumqi, 830001, China
- Xinjiang Medical University, Urumqi, 830054, China
| | - Cainan Luo
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001, China
- Xinjiang Clinical Research Center for Rheumatoid arthritis, Urumqi, 830001, China
| | - Zhengfang Li
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001, China
- Xinjiang Clinical Research Center for Rheumatoid arthritis, Urumqi, 830001, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Shibin Han
- College of Physics Science and Technology, Xinjiang University, Urumqi, 830046, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi, 830046, China
| | - Lijun Wu
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830001, China.
- Xinjiang Clinical Research Center for Rheumatoid arthritis, Urumqi, 830001, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, 830046, China.
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4
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Tseng YM, Chen KL, Chao PH, Han YY, Huang NT. Deep Learning-Assisted Surface-Enhanced Raman Scattering for Rapid Bacterial Identification. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 37216401 DOI: 10.1021/acsami.3c03212] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Bloodstream infection (BSI) is characterized by the presence of viable microorganisms in the bloodstream and may induce systemic immune responses. Early and appropriate antibiotic usage is crucial to effectively treating BSI. However, conventional culture-based microbiological diagnostics are time-consuming and cannot provide timely bacterial identification for subsequent antimicrobial susceptibility test (AST) and clinical decision-making. To address this issue, modern microbiological diagnostics have been developed, such as surface-enhanced Raman scattering (SERS), which is a sensitive, label-free, and quick bacterial detection method measuring specific bacterial metabolites. In this study, we aim to integrate a new deep learning (DL) method, Vision Transformer (ViT), with bacterial SERS spectral analysis to build the SERS-DL model for rapid identification of Gram type, species, and resistant strains. To demonstrate the feasibility of our approach, we used 11,774 SERS spectra obtained directly from eight common bacterial species in clinical blood samples without artificial introduction as the training dataset for the SERS-DL model. Our results showed that ViT achieved excellent identification accuracy of 99.30% for Gram type and 97.56% for species. Moreover, we employed transfer learning by using the Gram-positive species identifier as a pre-trained model to perform the antibiotic-resistant strain task. The identification accuracy of methicillin-resistant and -susceptible Staphylococcus aureus (MRSA and MSSA) can reach 98.5% with only 200-dataset requirement. In summary, our SERS-DL model has great potential to provide a quick clinical reference to determine the bacterial Gram type, species, and even resistant strains, which can guide early antibiotic usage in BSI.
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Affiliation(s)
- Yi-Ming Tseng
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, 106319
| | - Ko-Lun Chen
- Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei, Taiwan, 106319
| | - Po-Hsuan Chao
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, 106319
| | - Yin-Yi Han
- Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan, 100229
| | - Nien-Tsu Huang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, 106319
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, 106319
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5
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Cheng N, Lou B, Wang H. Discovering the digital biomarker of hepatocellular carcinoma in serum with SERS-based biosensors and intelligence vision. Colloids Surf B Biointerfaces 2023; 226:113315. [PMID: 37086688 DOI: 10.1016/j.colsurfb.2023.113315] [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: 02/10/2023] [Revised: 03/31/2023] [Accepted: 04/11/2023] [Indexed: 04/24/2023]
Abstract
By its many virtues, non-biomarker-reliant molecular detection has recently shown bright prospects for cancer screening but its clinical application is hindered by the shortage of measurable criteria that are analogous to biomarkers. Here, we report a digital biomarker, as a new-concept serum biomarker, of hepatocellular carcinoma (HCC) found with SERS-based biosensors and a deep neural network "digital retina" for visualizing and explicitly defining spectral fingerprints. We validate the discovered digital biomarker (a collection of 10 characteristic peaks in the serum SERS spectra) with unsupervised clustering of spectra from an independent sample batch comprised normal individuals and HCC cases; the validation results show clustering accuracies of 95.71% and 100.00%, respectively. Furthermore, we find that the digital biomarker of HCC shares a few common peaks with three clinically applied serum biomarkers, which means it could convey essential biomolecular information similar to these biomarkers. Accordingly, we present an intelligent method for early HCC detection that leverages the digital biomarker with similar traits as biomarkers. Employing the digital biomarker, we could accurately stratify HCC, hepatitis B, and normal populations with linear classifiers, exhibiting accuracies over 92% and area under the receiver operating curve values above 0.93. It is anticipated that this non-biomarker-reliant molecular detection method will facilitate mass cancer screening.
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Affiliation(s)
- Ningtao Cheng
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Bin Lou
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310003, China
| | - Hongyang Wang
- International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Shanghai 200438, China; National Center for Liver Cancer, Shanghai 201805, China.
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6
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Alkhuder K. Raman Scattering-Based Optical Sensing Of Chronic Liver Diseases. Photodiagnosis Photodyn Ther 2023; 42:103505. [PMID: 36965755 DOI: 10.1016/j.pdpdt.2023.103505] [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: 11/17/2022] [Revised: 02/26/2023] [Accepted: 03/07/2023] [Indexed: 03/27/2023]
Abstract
Chronic liver diseases (CLDs) are a major public health problem. Despite the progress achieved in fighting against viral hepatitis, the emergence of non-alcoholic fatty liver disease might pose a serious challenge to the public's health in the coming decades. Medical management of CLDs represents a substantial burden on the public health infrastructures. The health care cost of these diseases is an additional burden that weighs heavily on the economies of developing countries. Effective management of CLDs requires the adoption of reliable and cost-effective screening and diagnosing methods to ensure early detection and accurate clinical assessment of these diseases. Vibrational spectroscopies have emerged as universal analytical methods with promising applications in various industrial and biomedical fields. These revolutionary analytical techniques rely on analyzing the interaction between a light beam and the test sample to generate a spectral fingerprint. This latter is defined by the analyte's chemical structure and the molecular vibrations of its functional groups. Raman spectroscopy and surface-enhanced Raman spectroscopy have been used in combination with various chemometric tests to diagnose a wide range of malignant, metabolic and infectious diseases. The aim of the current review is to cast light on the use of these optical sensing methods in the diagnosis of CLDs. The vast majority of research works that investigated the potential application of these spectroscopic techniques in screening and detecting CLDs were discussed here. The advantages and limitations of these modern analytical methods, as compared with the routine and gold standard diagnostic approaches, were also reviewed in details.
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7
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Yang H, Li X, Zhang S, Li Y, Zhu Z, Shen J, Dai N, Zhou F. A one-dimensional convolutional neural network based deep learning for high accuracy classification of transformation stages in esophageal squamous cell carcinoma tissue using micro-FTIR. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 289:122210. [PMID: 36508904 DOI: 10.1016/j.saa.2022.122210] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 11/08/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
Among the most frequently diagnosed cancers in developing countries, esophageal squamous cell carcinoma (ESCC) ranks among the top six causes of death. It would be beneficial if a rapid, accurate, and automatic ESCC diagnostic method could be developed to reduce the workload of pathologists and improve the effectiveness of cancer treatments. Using micro-FTIR spectroscopy, this study classified the transformation stages of ESCC tissues. Based on 6,352 raw micro-FTIR spectra, a one-dimensional convolutional neural network (1D-CNN) model was constructed to classify-five stages. Based on the established model, more than 93% accuracy was achieved at each stage, and the accuracy of identifying proliferation, low grade neoplasia, and ESCC cancer groups was achieved 99% for the test dataset. In this proof-of-concept study, the developed method can be applied to other diseases in order to promote the use of FTIR spectroscopy in cancer pathology.
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Affiliation(s)
- Haijun Yang
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China
| | - Xianchang Li
- Huzhou College, Huzhou 313000, Zhejiang Province, China; Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China.
| | - Shiding Zhang
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China
| | - Yuan Li
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China
| | - Zunwei Zhu
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China
| | - Jingwei Shen
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China
| | - Ningtao Dai
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China
| | - Fuyou Zhou
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China.
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8
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Fatkullin M, Rodriguez RD, Petrov I, Villa NE, Lipovka A, Gridina M, Murastov G, Chernova A, Plotnikov E, Averkiev A, Cheshev D, Semyonov O, Gubarev F, Brazovskiy K, Sheng W, Amin I, Liu J, Jia X, Sheremet E. Molecular Plasmonic Silver Forests for the Photocatalytic-Driven Sensing Platforms. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:923. [PMID: 36903801 PMCID: PMC10005408 DOI: 10.3390/nano13050923] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 01/23/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
Structural electronics, as well as flexible and wearable devices are applications that are possible by merging polymers with metal nanoparticles. However, using conventional technologies, it is challenging to fabricate plasmonic structures that remain flexible. We developed three-dimensional (3D) plasmonic nanostructures/polymer sensors via single-step laser processing and further functionalization with 4-nitrobenzenethiol (4-NBT) as a molecular probe. These sensors allow ultrasensitive detection with surface-enhanced Raman spectroscopy (SERS). We tracked the 4-NBT plasmonic enhancement and changes in its vibrational spectrum under the chemical environment perturbations. As a model system, we investigated the sensor's performance when exposed to prostate cancer cells' media over 7 days showing the possibility of identifying the cell death reflected in the environment through the effects on the 4-NBT probe. Thus, the fabricated sensor could have an impact on the monitoring of the cancer treatment process. Moreover, the laser-driven nanoparticles/polymer intermixing resulted in a free-form electrically conductive composite that withstands over 1000 bending cycles without losing electrical properties. Our results bridge the gap between plasmonic sensing with SERS and flexible electronics in a scalable, energy-efficient, inexpensive, and environmentally friendly way.
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Affiliation(s)
- Maxim Fatkullin
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Raul D. Rodriguez
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Ilia Petrov
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Nelson E. Villa
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Anna Lipovka
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Maria Gridina
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Gennadiy Murastov
- Montanuniversität Leoben, Franz Josef-Straße 18, 8700 Leoben, Austria
| | - Anna Chernova
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Evgenii Plotnikov
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Andrey Averkiev
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Dmitry Cheshev
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Oleg Semyonov
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Fedor Gubarev
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Konstantin Brazovskiy
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
| | - Wenbo Sheng
- State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou 730000, China
| | - Ihsan Amin
- Van’t Hoff Institute of Molecular Science, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Jianxi Liu
- School of Materials Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China
| | - Xin Jia
- School of Chemistry and Chemical Engineering, Shihezi University, Shihezi 832003, China
| | - Evgeniya Sheremet
- Research School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, 30 Lenin Ave, 634050 Tomsk, Russia
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9
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Beeram R, Vepa KR, Soma VR. Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques. BIOSENSORS 2023; 13:328. [PMID: 36979540 PMCID: PMC10046859 DOI: 10.3390/bios13030328] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/20/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.
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10
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Chen Y, An Q, Teng K, Liu C, Sun F, Li G. Application of SERS in In-Vitro Biomedical Detection. Chem Asian J 2023; 18:e202201194. [PMID: 36581747 DOI: 10.1002/asia.202201194] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/24/2022] [Accepted: 12/29/2022] [Indexed: 12/31/2022]
Abstract
Surface-enhanced Raman scattering (SERS), as a rapid and nondestructive biological detection method, holds great promise for clinical on spot and early diagnosis. In order to address the challenging demands of on spot detection of biomedical samples, a variety of strategies has been developed. These strategies include substrate structural and component engineering, data processing techniques, as well as combination with other analytical methods. This report summarizes the recent SERS developments for biomedical detection, and their promising applications in cancer detection, virus or bacterial infection detection, miscarriage spotting, neurological disease screening et al. The first part discusses the frequently used SERS substrate component and structures, the second part reports on the detection strategies for nucleic acids, proteins, bacteria, and virus, the third part summarizes their promising applications in clinical detection in a variety of illnesses, and the forth part reports on recent development of SERS in combination with other analytical techniques. The special merits, challenges, and perspectives are discussed in both introduction and conclusion sections.
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Affiliation(s)
- Yunfan Chen
- School of Materials Science and Technology, China University of Geosciences, Beijing, 100083, P. R. China.,Engineering Research Center of Ministry of Education for, Geological Carbon Storage and Low Carbon Utilization of Resources, Beijing Key Laboratory of Materials Utilization of, Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, School of Material Sciences and Technology, China University of Geosciences, Beijing, 100083, P. R. China
| | - Qi An
- School of Materials Science and Technology, China University of Geosciences, Beijing, 100083, P. R. China.,Engineering Research Center of Ministry of Education for, Geological Carbon Storage and Low Carbon Utilization of Resources, Beijing Key Laboratory of Materials Utilization of, Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, School of Material Sciences and Technology, China University of Geosciences, Beijing, 100083, P. R. China
| | - Kaixuan Teng
- School of Materials Science and Technology, China University of Geosciences, Beijing, 100083, P. R. China.,Engineering Research Center of Ministry of Education for, Geological Carbon Storage and Low Carbon Utilization of Resources, Beijing Key Laboratory of Materials Utilization of, Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, School of Material Sciences and Technology, China University of Geosciences, Beijing, 100083, P. R. China
| | - Chao Liu
- School of Materials Science and Technology, China University of Geosciences, Beijing, 100083, P. R. China.,Department of Chemistry, China, Tsinghua University, Beijing, 100084, P. R. China.,Engineering Research Center of Ministry of Education for, Geological Carbon Storage and Low Carbon Utilization of Resources, Beijing Key Laboratory of Materials Utilization of, Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, School of Material Sciences and Technology, China University of Geosciences, Beijing, 100083, P. R. China
| | - Fuwei Sun
- Fujian Provincial Key Laboratory of, Terahertz Functional Devices and Intelligent Sensing, School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, P. R. China
| | - Guangtao Li
- Department of Chemistry, China, Tsinghua University, Beijing, 100084, P. R. China
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11
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Li J, Chen J, Dai Y, Liu Z, Zhao J, Liu S, Xiao R. Magnetic SERS Strip Based on 4-mercaptophenylboronic Acid-Modified Fe 3O 4@Au for Active Capture and Simultaneous Detection of Respiratory Bacteria. BIOSENSORS 2023; 13:210. [PMID: 36831976 PMCID: PMC9953780 DOI: 10.3390/bios13020210] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/16/2023] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
The rapid diagnosis and detection of respiratory bacteria at the early stage can effectively control the epidemic spread and bacterial infection. Here, we designed a rapid, ultrasensitive, and quantitative lateral flow immunoassay (LFA) strip for simultaneous detection of respiratory bacteria S. aureus and S. pneumoniae. In this assay, the surface enhanced Raman scattering (SERS) tags were designed through combining magnetite Raman enhancement nanoparticle Fe3O4@Au/DTNB and recognition element 4-mercaptophenylboronic acid (4-MPBA). Further, 4-MPBA could capture multiple bacteria in a complex environmental solution. Based on the strategies, Fe3O4@Au/DTNB-mediated magnetic enrichment and 4-MPBA-mediated universal capture capabilities improved the detection sensitivity, the limits of detection for S. aureus and S. pneumoniae were as low as 8 and 13 CFU mL-1, respectively, which were more sensitive than those of colloidal gold method. The Fe3O4@Au/DTNB/Au/4-MPBA-LFA also exhibited good reproducibility, excellent specificity, and high recovery rates in sputum samples, indicating its potential application in the detection of respiratory bacteria samples.
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Affiliation(s)
- Jingfei Li
- Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Jin Chen
- Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
- Department of Clinical Laboratory, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Yuwei Dai
- Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Zhenzhen Liu
- Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Junnan Zhao
- Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Shuchen Liu
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Rui Xiao
- Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
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Lu P, Lin D, Chen N, Wang L, Zhang X, Chen H, Ma P. CNN-assisted SERS enables ultra-sensitive and simultaneous detection of Scr and BUN for rapid kidney function assessment. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:322-332. [PMID: 36594673 DOI: 10.1039/d2ay01573k] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Kidney disease is highly prevalent and may result in severe clinical outcomes. Serum creatinine (Scr) and blood urea nitrogen (BUN) are the most widely used biomarkers for kidney function assessment, yet when measured alone, the result can be affected by a variety of parameters such as age, gender, protein consumption, etc. Measuring Scr and BUN simultaneously can eliminate most of the external influences and greatly improve the assessment of kidney function. In this study, a real-time kidney function assessment system based on dual biomarker detection was proposed. Scr and BUN were determined using surface-enhanced Raman scattering (SERS) within the concentration range of 10-1 to 10-6 M and 0.28 to 100 mg dl-1, respectively. A one-dimensional convolutional neural network (1D-CNN) model was employed to quantitatively analyze the concentration of biomarkers from the SERS spectral measurements. Moreover, we simulated a variety of kidney health conditions with 16 groups of mixed Scr and BUN in serum. The proposed CNN-assisted SERS method was used to quantify both biomarkers and provide diagnostic results. The Au core-Ag shell nanoprobes provided ultra-sensitive SERS detection and the CNN model achieved excellent regression results with an R2 of 0.9871 in the testing dataset. The system demonstrated a rapid and robust evaluation for the assessment of kidney function, providing a promising idea for medical diagnosis with the help of spectroscopy and deep learning methods.
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Affiliation(s)
- Ping Lu
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, College of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Dajun Lin
- Department of Electrical and Computer Engineering, The University of Utah, Salt Lake City, UT 84112, USA
| | - Ning Chen
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, College of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Luyao Wang
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, College of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Xuedian Zhang
- Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China
| | - Hui Chen
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, College of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Pei Ma
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, College of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
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13
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Liu Z, Meng D, Su G, Hu P, Song B, Wang Y, Wei J, Yang H, Yuan T, Chen B, Ou TH, Hossain S, Miller M, Liu F, Wu W. Ultrafast Early Warning of Heart Attacks through Plasmon-Enhanced Raman Spectroscopy using Collapsible Nanofingers and Machine Learning. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2204719. [PMID: 36333119 DOI: 10.1002/smll.202204719] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/03/2022] [Indexed: 06/16/2023]
Abstract
As the leading cause of death, heart attacks result in millions of deaths annually, with no end in sight. Early intervention is the only strategy for rescuing lives threatened by heart disease. However, the detection time of the fastest heart-attack detection system is >15 min, which is too long considering the rapid passage of life. In this study, a machine learning (ML)-driven system with a simple process, low-cost, short detection time (only 10 s), and high precision is developed. By utilizing a functionalized nanofinger structure, even a trace amount of biomarker leaked before a heart attack can be captured. Additionally, enhanced Raman profiles are constructed for predictive analytics. Five ML models are developed to harness the useful characteristics of each Raman spectrum and provide early warnings of heart attacks with >98% accuracy. Through the strategic combination of nanofingers and ML algorithms, the proposed warning system accurately provides alerts on silent heart-attack attempts seconds ahead of actual attacks.
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Affiliation(s)
- Zerui Liu
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Deming Meng
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Guangxu Su
- Department of Applied Physics, Zhejiang University of Technology, Hangzhou, Zhejiang, 310023, China
| | - Pan Hu
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Boxiang Song
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wu Han, Hu Bei, 430074, China
| | - Yunxiang Wang
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Junhan Wei
- Department of Applied Physics, Zhejiang University of Technology, Hangzhou, Zhejiang, 310023, China
| | - Hao Yang
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Tianyi Yuan
- Beijing Etown Academy, Beijing, 100176, China
| | - Buyun Chen
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Tse-Hsien Ou
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Sushmit Hossain
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Matthew Miller
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Fanxin Liu
- Department of Applied Physics, Zhejiang University of Technology, Hangzhou, Zhejiang, 310023, China
| | - Wei Wu
- Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
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14
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Xiong M, Xu Y, Zhao Y, He S, Zhu Q, Wu Y, Hu X, Liu L. Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis. Front Oncol 2023; 13:990306. [PMID: 36874099 PMCID: PMC9978515 DOI: 10.3389/fonc.2023.990306] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 02/03/2023] [Indexed: 02/18/2023] Open
Abstract
Objective To provide the current research progress, hotspots, and emerging trends for AI in liver cancer, we have compiled a relative comprehensive and quantitative report on the research of liver disease using artificial intelligence by employing bibliometrics in this study. Methods In this study, the Web of Science Core Collection (WoSCC) database was used to perform systematic searches using keywords and a manual screening strategy, VOSviewer was used to analyze the degree of cooperation between countries/regions and institutions, as well as the co-occurrence of cooperation between authors and cited authors. Citespace was applied to generate a dual map to analyze the relationship of citing journals and citied journals and conduct a strong citation bursts ranking analysis of references. Online SRplot was used for in-depth keyword analysis and Microsoft Excel 2019 was used to collect the targeted variables from retrieved articles. Results 1724 papers were collected in this study, including 1547 original articles and 177 reviews. The study of AI in liver cancer mostly began from 2003 and has developed rapidly from 2017. China has the largest number of publications, and the United States has the highest H-index and total citation counts. The top three most productive institutions are the League of European Research Universities, Sun Yat Sen University, and Zhejiang University. Jasjit S. Suri and Frontiers in Oncology are the most published author and journal, respectively. Keyword analysis showed that in addition to the research on liver cancer, research on liver cirrhosis, fatty liver disease, and liver fibrosis were also common. Computed tomography was the most used diagnostic tool, followed by ultrasound and magnetic resonance imaging. The diagnosis and differential diagnosis of liver cancer are currently the most widely adopted research goals, and comprehensive analyses of multi-type data and postoperative analysis of patients with advanced liver cancer are rare. The use of convolutional neural networks is the main technical method used in studies of AI on liver cancer. Conclusion AI has undergone rapid development and has a wide application in the diagnosis and treatment of liver diseases, especially in China. Imaging is an indispensable tool in this filed. Mmulti-type data fusion analysis and development of multimodal treatment plans for liver cancer could become the major trend of future research in AI in liver cancer.
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Affiliation(s)
- Ming Xiong
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yaona Xu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yang Zhao
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Si He
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Qihan Zhu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yi Wu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xiaofei Hu
- Department of Nuclear Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Li Liu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China.,Department of Ultrasound, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
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15
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Lv R, Wang Z, Ma Y, Li W, Tian J. Machine Learning Enhanced Optical Spectroscopy for Disease Detection. J Phys Chem Lett 2022; 13:9238-9249. [PMID: 36173116 DOI: 10.1021/acs.jpclett.2c02193] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Optical spectroscopy plays an important role in disease detection. Improving the sensitivity and specificity of spectral detection has great importance in the development of accurate diagnosis. The development of artificial intelligence technology provides a great opportunity to improve the detection accuracy through machine learning methods. In this Perspective, we focus on the combination of machine learning methods with the optical spectroscopy methods widely used for disease detection, including absorbance, fluorescence, scattering, FTIR, terahertz, etc. By comparing the spectral analysis with different machine learning methods, we illustrate that the support vector machine and convolutional neural network are most effective, which have potential to further improve the classification accuracy to distinguish disease subtypes if these machine learning methods are used. This Perspective broadens the scope of optical spectroscopy enhanced by machine learning and will be useful for the development of disease detection.
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Affiliation(s)
- Ruichan Lv
- Interdisciplinary Research Center of Smart Sensor, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Zhan Wang
- Interdisciplinary Research Center of Smart Sensor, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Yaqun Ma
- Interdisciplinary Research Center of Smart Sensor, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Wenjing Li
- Interdisciplinary Research Center of Smart Sensor, Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Jie Tian
- Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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16
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Ni J, Huang M, Ji W, Wang L, Sun T. Recent advances in Surface-enhanced Raman Scattering for Liver Cancer Detection. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1016/j.cjac.2022.100180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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17
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Yang Y, Xu B, Murray J, Haverstick J, Chen X, Tripp RA, Zhao Y. Rapid and quantitative detection of respiratory viruses using surface-enhanced Raman spectroscopy and machine learning. Biosens Bioelectron 2022; 217:114721. [PMID: 36152394 DOI: 10.1016/j.bios.2022.114721] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/29/2022] [Accepted: 09/11/2022] [Indexed: 12/23/2022]
Abstract
Rapid and sensitive pathogen detection is important for prevention and control of disease. Here, we report a label-free diagnostic platform that combines surface-enhanced Raman scattering (SERS) and machine learning for the rapid and accurate detection of thirteen respiratory virus species including SARS-CoV-2, common human coronaviruses, influenza viruses, and others. Virus detection and measurement have been performed using highly sensitive SiO2 coated silver nanorod array substrates, allowing for detection and identification of their characteristic SERS peaks. Using appropriate spectral processing procedures and machine learning algorithms (MLAs) including support vector machine (SVM), k-nearest neighbor, and random forest, the virus species as well as strains and variants have been differentiated and classified and a differentiation accuracy of >99% has been obtained. Utilizing SVM-based regression, quantitative calibration curves have been constructed to accurately estimate the unknown virus concentrations in buffer and saliva. This study shows that using a combination of SERS, MLA, and regression, it is possible to classify and quantify the virus in saliva, which could aid medical diagnosis and therapeutic intervention.
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Affiliation(s)
- Yanjun Yang
- School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, GA, 30602, USA.
| | - Beibei Xu
- Department of Statistics, The University of Georgia, Athens, GA, 30602, USA
| | - Jackelyn Murray
- Department of Infectious Diseases, College of Veterinary Medicine, The University of Georgia, Athens, GA, 30602, USA
| | - James Haverstick
- Department of Physics and Astronomy, The University of Georgia, Athens, GA, 30602, USA
| | - Xianyan Chen
- Department of Statistics, The University of Georgia, Athens, GA, 30602, USA
| | - Ralph A Tripp
- Department of Infectious Diseases, College of Veterinary Medicine, The University of Georgia, Athens, GA, 30602, USA
| | - Yiping Zhao
- Department of Physics and Astronomy, The University of Georgia, Athens, GA, 30602, USA.
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18
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Lin J, Weng Y, Lin X, Qiu S, Huang Z, Pan C, Li Y, Kong KV, Zhang X, Feng S. Highly Efficient Blood Protein Analysis Using Membrane Purification Technique and Super-Hydrophobic SERS Platform for Precise Screening and Staging of Nasopharyngeal Carcinoma. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:2724. [PMID: 35957154 PMCID: PMC9370769 DOI: 10.3390/nano12152724] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/18/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
Early screening and precise staging are crucial for reducing mortality in patients with nasopharyngeal carcinoma (NPC). This study aimed to assess the performance of blood protein surface-enhanced Raman scattering (SERS) spectroscopy, combined with deep learning, for the precise detection of NPC. A highly efficient protein SERS analysis, based on a membrane purification technique and super-hydrophobic platform, was developed and applied to blood samples from 1164 subjects, including 225 healthy volunteers, 120 stage I, 249 stage II, 291 stage III, and 279 stage IV NPC patients. The proteins were rapidly purified from only 10 µL of blood plasma using the membrane purification technique. Then, the super-hydrophobic platform was prepared to pre-concentrate tiny amounts of proteins by forming a uniform deposition to provide repeatable SERS spectra. A total of 1164 high-quality protein SERS spectra were rapidly collected using a self-developed macro-Raman system. A convolutional neural network-based deep-learning algorithm was used to classify the spectra. An accuracy of 100% was achieved for distinguishing between the healthy and NPC groups, and accuracies of 96%, 96%, 100%, and 100% were found for the differential classification among the four NPC stages. This study demonstrated the great promise of SERS- and deep-learning-based blood protein testing for rapid, non-invasive, and precise screening and staging of NPC.
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Affiliation(s)
- Jinyong 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
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Youliang Weng
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Xueliang Lin
- Fujian Provincial Key Laboratory for Advanced Micro-Nano Photonics Technology and Devices, Research Center for Photonics Technology, Quanzhou Normal University, Quanzhou 362046, 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
| | - Changbin Pan
- 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
| | - Ying Li
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350014, China
| | - Kien Voon Kong
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - 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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19
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Yang Y, Xu B, Haverstick J, Ibtehaz N, Muszyński A, Chen X, Chowdhury MEH, Zughaier SM, Zhao Y. Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning. NANOSCALE 2022; 14:8806-8817. [PMID: 35686584 PMCID: PMC9575096 DOI: 10.1039/d2nr01277d] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Bacterial endotoxin, a major component of the Gram-negative bacterial outer membrane leaflet, is a lipopolysaccharide shed from bacteria during their growth and infection and can be utilized as a biomarker for bacterial detection. Here, the surface enhanced Raman scattering (SERS) spectra of eleven bacterial endotoxins with an average detection amount of 8.75 pg per measurement have been obtained based on silver nanorod array substrates, and the characteristic SERS peaks have been identified. With appropriate spectral pre-processing procedures, different classical machine learning algorithms, including support vector machine, k-nearest neighbor, random forest, etc., and a modified deep learning algorithm, RamanNet, have been applied to differentiate and classify these endotoxins. It has been found that most conventional machine learning algorithms can attain a differentiation accuracy of >99%, while RamanNet can achieve 100% accuracy. Such an approach has the potential for precise classification of endotoxins and could be used for rapid medical diagnoses and therapeutic decisions for pathogenic infections.
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Affiliation(s)
- Yanjun Yang
- School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA.
| | - Beibei Xu
- Department of Statistics, The University of Georgia, Athens, GA 30602, USA
| | - James Haverstick
- Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA.
| | - Nabil Ibtehaz
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Artur Muszyński
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Xianyan Chen
- Department of Statistics, The University of Georgia, Athens, GA 30602, USA
| | - Muhammad E H Chowdhury
- Department of Electrical Engineering, College of Engineering, Qatar University, PO. Box 2713, Doha, Qatar
| | - Susu M Zughaier
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, PO. Box 2713, Doha, Qatar.
| | - Yiping Zhao
- Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA.
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20
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Zheng X, Wu G, Wang J, Yin L, Lv X. Rapid detection of hysteromyoma and cervical cancer based on serum surface-enhanced Raman spectroscopy and a support vector machine. BIOMEDICAL OPTICS EXPRESS 2022; 13:1912-1923. [PMID: 35519280 PMCID: PMC9045898 DOI: 10.1364/boe.448121] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/30/2022] [Accepted: 02/05/2022] [Indexed: 06/14/2023]
Abstract
In this study, we investigated the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with a support vector machine (SVM) algorithm to discriminate hysteromyoma and cervical cancer from healthy volunteers rapidly. SERS spectra of serum samples were recorded from 30 hysteromyoma patients, 36 cervical cancer patients as well as 30 healthy subjects. SVM was used to establish the classification models, and three types of kernel functions, namely linear, polynomial, and Gaussian radial basis function (RBF), were utilized for comparison. When the polynomial kernel function was employed, the overall diagnostic accuracy for classifying the three groups could achieve 86.5%. In addition, when the optimal kernel function was selected, the diagnostic accuracy for identifying healthy versus hysteromyoma, healthy versus cervical cancer, and hysteromyoma versus cervical cancer reached 98.3%, 93.9%, and 90.9%, respectively. The current results indicate that serum SERS technology, together with the SVM algorithm, is expected to become a clinical tool for rapid screening of hysteromyoma and cervical cancer.
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Affiliation(s)
- Xiangxiang Zheng
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Guohua Wu
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Jing Wang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Gynecology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - Longfei Yin
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xiaoyi Lv
- School of Software, Xinjiang University, Urumqi 830091, China
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21
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Wang J, Zhang H, Tang Y, Wen M, Yao B, Yuan S, Zhang W, Lei H. Metal-Nanostructure-Decorated Spider Silk for Highly Sensitive Refractive Index Sensing. ACS Biomater Sci Eng 2022; 8:1060-1066. [PMID: 35212530 DOI: 10.1021/acsbiomaterials.1c01565] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Highly sensitive detection of refractive index (RI) is essential for the analysis of the bio-microenvironment and basic cellular reactions. To achieve this, optic-fiber RI sensors based on localized surface plasmon resonance (LSPR) have been widely used for their flexibility and high sensitivity. However, the current optic-fiber RI sensors are mainly fabricated using glass, which makes them face the challenges in biocompatibility and biosafety. In this work, a RI sensor with high sensitivity is fabricated using metal-nanostructure-decorated spider silk. The spider silk, which is directly dragged from Araneus ventricosus, is natural protein-based biopolymer with low attenuation, good biocompatibility and biodegradability, large RI, great flexibility, and easy functionalization. Hence, the spider silk can be an ideal alternative to glass for sensing in biological environments with a wide RI range. Different kinds of metal nanostructures, such as gold nanorods (GNRs), gold nanobipyramids (GNBP), and Ag@GNRs, are decorated on the surface of the spider silk utilizing the surface viscidity of the silk. By directing a beam of white light into the spider silk, the LSPR of the metal nanostructures was excited and a highly sensitive RI sensing (the highest sensitivity of 1746 nm per refractive index was achieved on the GNBP-decorated spider silk) was obtained. This work may pave a new way to precise and sensitive biosensing and bioanalysis.
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Affiliation(s)
- Jiale Wang
- School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| | - Hao Zhang
- School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| | - Yangjie Tang
- School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| | - Mingcong Wen
- School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| | - Benjun Yao
- School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| | - Shun Yuan
- School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| | - Weina Zhang
- School of Information Engineering, Guangdong Provincial Key Laboratory of Photonics Information Technology, Guangdong University of Technology, Guangzhou 510006, China
| | - Hongxiang Lei
- School of Materials Science and Engineering, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
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22
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de Carvalho Gomes P, Crossman A, Massey E, Stanley Rickard JJ, Oppenheimer PG. Real-time validation of Surface-Enhanced Raman Scattering substrates via convolutional neural network algorithm. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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23
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Li M, He H, Huang G, Lin B, Tian H, Xia K, Yuan C, Zhan X, Zhang Y, Fu W. A Novel and Rapid Serum Detection Technology for Non-Invasive Screening of Gastric Cancer Based on Raman Spectroscopy Combined With Different Machine Learning Methods. Front Oncol 2021; 11:665176. [PMID: 34646758 PMCID: PMC8504718 DOI: 10.3389/fonc.2021.665176] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 09/06/2021] [Indexed: 12/04/2022] Open
Abstract
Gastric cancer (GC) is the fifth most common cancer in the world and a serious threat to human health. Due to its high morbidity and mortality, a simple, rapid and accurate early screening method for GC is urgently needed. In this study, the potential of Raman spectroscopy combined with different machine learning methods was explored to distinguish serum samples from GC patients and healthy controls. Serum Raman spectra were collected from 109 patients with GC (including 35 in stage I, 14 in stage II, 35 in stage III, and 25 in stage IV) and 104 healthy volunteers matched for age, presenting for a routine physical examination. We analyzed the difference in serum metabolism between GC patients and healthy people through a comparative study of the average Raman spectra of the two groups. Four machine learning methods, one-dimensional convolutional neural network, random forest, support vector machine, and K-nearest neighbor were used to explore identifying two sets of Raman spectral data. The classification model was established by using 70% of the data as a training set and 30% as a test set. Using unseen data to test the model, the RF model yielded an accuracy of 92.8%, and the sensitivity and specificity were 94.7% and 90.8%. The performance of the RF model was further confirmed by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.9199. This exploratory work shows that serum Raman spectroscopy combined with RF has great potential in the machine-assisted classification of GC, and is expected to provide a non-destructive and convenient technology for the screening of GC patients.
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Affiliation(s)
- Mengya Li
- Department of Laboratory Medicine, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Haiyan He
- Department of Laboratory Medicine, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Guorong Huang
- Department of Laboratory Medicine, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Bo Lin
- Department of Laboratory Medicine, Chongqing University Cancer Hospital, Chongqing, China
| | - Huiyan Tian
- Department of Laboratory Medicine, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Ke Xia
- Department of Laboratory Medicine, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Changjing Yuan
- Department of Laboratory Medicine, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xinyu Zhan
- Department of Laboratory Medicine, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yang Zhang
- Department of Laboratory Medicine, Chongqing University Cancer Hospital, Chongqing, China
| | - Weiling Fu
- Department of Laboratory Medicine, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing, China
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24
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Moisoiu V, Iancu SD, Stefancu A, Moisoiu T, Pardini B, Dragomir MP, Crisan N, Avram L, Crisan D, Andras I, Fodor D, Leopold LF, Socaciu C, Bálint Z, Tomuleasa C, Elec F, Leopold N. SERS liquid biopsy: An emerging tool for medical diagnosis. Colloids Surf B Biointerfaces 2021; 208:112064. [PMID: 34517219 DOI: 10.1016/j.colsurfb.2021.112064] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/09/2021] [Accepted: 08/20/2021] [Indexed: 02/02/2023]
Abstract
Surface-enhanced Raman scattering (SERS) is emerging as a novel strategy for biofluid analysis. In this review, we delineate four experimental SERS protocols that are frequently used for the profiling of biofluids: 1) liquid SERS for the detection of purine metabolites; 2) iodide-modified liquid SERS for the detection of proteins; 3) dried SERS for the detection of both purine metabolites and proteins; 4) resonant Raman for the detection of carotenoids. To explain the selectivity of each experimental SERS protocol, we introduce a heuristic model for the chemisorption of analytes mediated by adsorbed ions (adions) onto the SERS substrate. Next, we show that the promising results of SERS liquid biopsy stem from the fact that the concentration levels of purine metabolites, proteins and carotenoids are informative of the cellular turnover rate, inflammation, and oxidative stress, respectively. These processes are perturbed in virtually every disease, from cancer to autoimmune maladies. Finally, we review recent SERS liquid biopsy studies and discuss future steps that are required for translating SERS in the clinical setting.
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Affiliation(s)
- Vlad Moisoiu
- Faculty of Physics, Babeș-Bolyai University, 400084, Cluj-Napoca, Romania
| | - Stefania D Iancu
- Faculty of Physics, Babeș-Bolyai University, 400084, Cluj-Napoca, Romania
| | - Andrei Stefancu
- Faculty of Physics, Babeș-Bolyai University, 400084, Cluj-Napoca, Romania
| | - Tudor Moisoiu
- Clinical Institute of Urology and Renal Transplant, 400006, Cluj-Napoca, Romania; Biomed Data Analytics SRL, 400696, Cluj-Napoca, Romania; Department of Urology, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania
| | - Barbara Pardini
- Candiolo Cancer Institute, FPO-IRCCS, 10060, Candiolo, Italy; Italian Institute of Genomic Medicine (IIGM), 10060, Candiolo, Italy
| | - Mihnea P Dragomir
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany
| | - Nicolae Crisan
- Department of Urology, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania; Clinical Municipal Hospital, 400139, Cluj-Napoca, Romania
| | - Lucretia Avram
- Clinical Municipal Hospital, 400139, Cluj-Napoca, Romania; Department of Geriatrics, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania
| | - Dana Crisan
- Clinical Municipal Hospital, 400139, Cluj-Napoca, Romania; 5th Internal Medicine Department, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania
| | - Iulia Andras
- Department of Urology, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania; Clinical Municipal Hospital, 400139, Cluj-Napoca, Romania
| | - Daniela Fodor
- 2nd Internal Medicine Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400006, Cluj-Napoca, Romania
| | - Loredana F Leopold
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine, 400372, Cluj-Napoca, Romania
| | - Carmen Socaciu
- Faculty of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine, 400372, Cluj-Napoca, Romania; BIODIATECH Research Centre for Applied Biotechnology, SC Proplanta, 400478, Cluj-Napoca, Romania
| | - Zoltán Bálint
- Faculty of Physics, Babeș-Bolyai University, 400084, Cluj-Napoca, Romania
| | - Ciprian Tomuleasa
- Department of Hematology, Iuliu Hatieganu University of Medicine and Pharmacy, 400124, Cluj-Napoca, Romania; Department of Hematology, Ion Chiricuta Clinical Cancer Center, 400124, Cluj-Napoca, Romania; Medfuture Research Center for Advanced Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400349, Cluj-Napoca, Romania
| | - Florin Elec
- Clinical Institute of Urology and Renal Transplant, 400006, Cluj-Napoca, Romania; Department of Urology, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012, Cluj-Napoca, Romania.
| | - Nicolae Leopold
- Faculty of Physics, Babeș-Bolyai University, 400084, Cluj-Napoca, Romania; Biomed Data Analytics SRL, 400696, Cluj-Napoca, Romania.
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