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Zhang Q, Lin Y, Lin D, Lin X, Liu M, Tao H, Wu J, Wang T, Wang C, Feng S. Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms. Talanta 2024; 275:126136. [PMID: 38692045 DOI: 10.1016/j.talanta.2024.126136] [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: 02/05/2024] [Revised: 04/06/2024] [Accepted: 04/19/2024] [Indexed: 05/03/2024]
Abstract
Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.
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Affiliation(s)
- Qiyi Zhang
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Yuxiang Lin
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian, 350001, China
| | - Duo Lin
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Xueliang Lin
- Fujian Provincial Key Laboratory for Advanced Micro-nano Photonics Technology and Devices, Quanzhou Normal University, Quanzhou, 362000, China
| | - Miaomiao Liu
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Hong Tao
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Jinxun Wu
- Department of Pathology, Fuzhou Lianjiang Country Hospital, Fuzhou, Fujian, 350500, China
| | - Tingyin Wang
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China.
| | - Chuan Wang
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian, 350001, China.
| | - Shangyuan Feng
- Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, 350117, China.
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Huang Y, Chen C, Chang C, Cheng Z, Liu Y, Wang X, Chen C, Lv X. SLE diagnosis research based on SERS combined with a multi-modal fusion method. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 315:124296. [PMID: 38640628 DOI: 10.1016/j.saa.2024.124296] [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: 12/09/2023] [Revised: 03/15/2024] [Accepted: 04/14/2024] [Indexed: 04/21/2024]
Abstract
As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future.
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Affiliation(s)
- Yuhao Huang
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Xinjiang Cloud Computing Engineering Technology Research Center, Karamay 834000, China
| | - Chenjie Chang
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Zhiyuan Cheng
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Yang Liu
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China
| | - Xuehua Wang
- College of Physical Science and Technology, Xinjiang University, Urumqi 830046, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Xinjiang Cloud Computing Engineering Technology Research Center, Karamay 834000, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China; Xinjiang Cloud Computing Application Laboratory, Xinjiang Cloud Computing Engineering Technology Research Center, Karamay 834000, China.
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Yan L, Su H, Liu J, Wen X, Luo H, Yin Y, Guo X. Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study. BMC Cancer 2024; 24:791. [PMID: 38956551 PMCID: PMC11220989 DOI: 10.1186/s12885-024-12578-y] [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: 05/25/2023] [Accepted: 06/28/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening. METHODS Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls. RESULTS The diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly. CONCLUSION Therefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.
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Affiliation(s)
- Linfang Yan
- Guang'an People's Hospital, Guang'an, Sichuan Province, China
| | - Huiting Su
- Guang'an People's Hospital, Guang'an, Sichuan Province, China.
| | - Jiafei Liu
- Guang'an People's Hospital, Guang'an, Sichuan Province, China
| | - Xiaozheng Wen
- Guang'an People's Hospital, Guang'an, Sichuan Province, China
| | - Huaichao Luo
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China
| | - Yu Yin
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoqiang Guo
- Guang'an People's Hospital, Guang'an, Sichuan Province, China
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Zhao J, Lui H, Kalia S, Lee TK, Zeng H. Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation. Front Oncol 2024; 14:1320220. [PMID: 38962264 PMCID: PMC11219827 DOI: 10.3389/fonc.2024.1320220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 05/23/2024] [Indexed: 07/05/2024] Open
Abstract
Background Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. Results The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Conclusions Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.
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Affiliation(s)
- Jianhua Zhao
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Harvey Lui
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Sunil Kalia
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
| | - Tim K. Lee
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Haishan Zeng
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
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5
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Ember K, Dallaire F, Plante A, Sheehy G, Guiot MC, Agarwal R, Yadav R, Douet A, Selb J, Tremblay JP, Dupuis A, Marple E, Urmey K, Rizea C, Harb A, McCarthy L, Schupper A, Umphlett M, Tsankova N, Leblond F, Hadjipanayis C, Petrecca K. In situ brain tumor detection using a Raman spectroscopy system-results of a multicenter study. Sci Rep 2024; 14:13309. [PMID: 38858389 PMCID: PMC11164901 DOI: 10.1038/s41598-024-62543-9] [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: 04/19/2024] [Accepted: 05/17/2024] [Indexed: 06/12/2024] Open
Abstract
Safe and effective brain tumor surgery aims to remove tumor tissue, not non-tumoral brain. This is a challenge since tumor cells are often not visually distinguishable from peritumoral brain during surgery. To address this, we conducted a multicenter study testing whether the Sentry System could distinguish the three most common types of brain tumors from brain tissue in a label-free manner. The Sentry System is a new real time, in situ brain tumor detection device that merges Raman spectroscopy with machine learning tissue classifiers. Nine hundred and seventy-six in situ spectroscopy measurements and colocalized tissue specimens were acquired from 67 patients undergoing surgery for glioblastoma, brain metastases, or meningioma to assess tumor classification. The device achieved diagnostic accuracies of 91% for glioblastoma, 97% for brain metastases, and 96% for meningiomas. These data show that the Sentry System discriminated tumor containing tissue from non-tumoral brain in real time and prior to resection.
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Affiliation(s)
- Katherine Ember
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Frédérick Dallaire
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Arthur Plante
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Guillaume Sheehy
- Polytechnique Montréal, Montreal, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Marie-Christine Guiot
- Division of Neuropathology, Department of Pathology, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Frédéric Leblond
- Polytechnique Montréal, Montreal, Canada.
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada.
- Institut du Cancer de Montréal, Montreal, Canada.
| | - Constantinos Hadjipanayis
- Mount Sinai Hospital, New York, NY, USA.
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
| | - Kevin Petrecca
- Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada.
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6
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Chu J, Yu X, Jiang G, Tao Y, Wu W, Han S. Bacterial imaging in tumour diagnosis. Microb Biotechnol 2024; 17:e14474. [PMID: 38808743 PMCID: PMC11135020 DOI: 10.1111/1751-7915.14474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 04/15/2024] [Accepted: 04/17/2024] [Indexed: 05/30/2024] Open
Abstract
Some bacteria, such as Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium), have an inherent ability to locate solid tumours, making them a versatile platform that can be combined with other tools to improve the tumour diagnosis and treatment. In anti-cancer therapy, bacteria function by carrying drugs directly or expressing exogenous therapeutic genes. The application of bacterial imaging in tumour diagnosis, a novel and promising research area, can indeed provide dynamic and real-time monitoring in both pre-treatment assessment and post-treatment detection. Different imaging techniques, including optical technology, acoustic imaging, magnetic resonance imaging (MRI) and nuclear medicine imaging, allow us to observe and track tumour-associated bacteria. Optical imaging, including bioluminescence and fluorescence, provides high-sensitivity and high-resolution imaging. Acoustic imaging is a real-time and non-invasive imaging technique with good penetration depth and spatial resolution. MRI provides high spatial resolution and radiation-free imaging. Nuclear medicine imaging, including positron emission tomography (PET) and single photon emission computed tomography (SPECT) can provide information on the distribution and dynamics of bacterial population. Moreover, strategies of synthetic biology modification and nanomaterial engineering modification can improve the viability and localization ability of bacteria while maintaining their autonomy and vitality, thus aiding the visualization of gut bacteria. However, there are some challenges, such as the relatively low bacterial abundance and heterogeneously distribution within the tumour, the high dimensionality of spatial datasets and the limitations of imaging labeling tools. In summary, with the continuous development of imaging technology and nanotechnology, it is expected to further make in-depth study on tumour-associated bacteria and develop new bacterial imaging methods for tumour diagnosis.
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Affiliation(s)
- Jian Chu
- Huzhou Central HospitalAffiliated Central Hospital Huzhou UniversityHuzhouChina
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive CancerHuzhouChina
- Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital)HuzhouChina
| | - Xiang Yu
- Huzhou Central HospitalAffiliated Central Hospital Huzhou UniversityHuzhouChina
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive CancerHuzhouChina
- Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital)HuzhouChina
| | - Gaofei Jiang
- Key Lab of Organic‐Based Fertilizers of China, Jiangsu Provincial Key Lab for Solid Organic Waste UtilizationNanjing Agricultural UniversityNanjingChina
| | - Ye Tao
- Shanghai BIOZERON Biotechnology Co., Ltd.ShanghaiChina
| | - Wei Wu
- Huzhou Central HospitalAffiliated Central Hospital Huzhou UniversityHuzhouChina
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive CancerHuzhouChina
- Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital)HuzhouChina
| | - Shuwen Han
- Huzhou Central HospitalAffiliated Central Hospital Huzhou UniversityHuzhouChina
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive CancerHuzhouChina
- Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital)HuzhouChina
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7
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Xiong C, Zhong Q, Yan D, Zhang B, Yao Y, Qian W, Zheng C, Mei X, Zhu S. Multi-branch attention Raman network and surface-enhanced Raman spectroscopy for the classification of neurological disorders. BIOMEDICAL OPTICS EXPRESS 2024; 15:3523-3540. [PMID: 38867772 PMCID: PMC11166416 DOI: 10.1364/boe.514196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/21/2024] [Accepted: 04/23/2024] [Indexed: 06/14/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS), a rapid, low-cost, non-invasive, ultrasensitive, and label-free technique, has been widely used in-situ and ex-situ biomedical diagnostics questions. However, analyzing and interpreting the untargeted spectral data remains challenging due to the difficulty of designing an optimal data pre-processing and modelling procedure. In this paper, we propose a Multi-branch Attention Raman Network (MBA-RamanNet) with a multi-branch attention module, including the convolutional block attention module (CBAM) branch, deep convolution module (DCM) branch, and branch weights, to extract more global and local information of characteristic Raman peaks which are more distinctive for classification tasks. CBAM, including channel and spatial aspects, is adopted to enhance the distinctive global information on Raman peaks. DCM is used to supplement local information of Raman peaks. Autonomously trained branch weights are applied to fuse the features of each branch, thereby optimizing the global and local information of the characteristic Raman peaks for identifying diseases. Extensive experiments are performed for two different neurological disorders classification tasks via untargeted serum SERS data. The results demonstrate that MBA-RamanNet outperforms commonly used CNN methods with an accuracy of 88.24% for the classification of healthy controls, mild cognitive impairment, Alzheimer's disease, and Non-Alzheimer's dementia; an accuracy of 90% for the classification of healthy controls, elderly depression, and elderly anxiety.
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Affiliation(s)
- Changchun Xiong
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
| | - Qingshan Zhong
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
| | - Denghui Yan
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
| | - Baihua Zhang
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
| | - Yudong Yao
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
| | - Wei Qian
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
| | - Chengying Zheng
- Department of Psychiatry, Ningbo Kangning Hospital and Affiliated Mental Health Centre, Ningbo Key Laboratory for Physical Diagnosis and Treatment of Mental and Psychological Disorders, Ningbo University, Ningbo 315211, China
| | - Xi Mei
- Department of Psychiatry, Ningbo Kangning Hospital and Affiliated Mental Health Centre, Ningbo Key Laboratory for Physical Diagnosis and Treatment of Mental and Psychological Disorders, Ningbo University, Ningbo 315211, China
| | - Shanshan Zhu
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology , Fujian Normal University, Fuzhou 350117, China
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8
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Song J, So PTC, Yoo H, Kang JW. Swept-source Raman spectroscopy of chemical and biological materials. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:S22703. [PMID: 38584965 PMCID: PMC10996846 DOI: 10.1117/1.jbo.29.s2.s22703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/22/2024] [Accepted: 02/23/2024] [Indexed: 04/09/2024]
Abstract
Significance Raman spectroscopy has been used as a powerful tool for chemical analysis, enabling the noninvasive acquisition of molecular fingerprints from various samples. Raman spectroscopy has proven to be valuable in numerous fields, including pharmaceutical, materials science, and biomedicine. Active research and development efforts are currently underway to bring this analytical instrument into the field, enabling in situ Raman measurements for a wider range of applications. Dispersive Raman spectroscopy using a fixed, narrowband source is a common method for acquiring Raman spectra. However, dispersive Raman spectroscopy requires a bulky spectrometer, which limits its field applicability. Therefore, there has been a tremendous need to develop a portable and sensitive Raman system. Aim We developed a compact swept-source Raman (SS-Raman) spectroscopy system and proposed a signal processing method to mitigate hardware limitations. We demonstrated the capabilities of the SS-Raman spectroscopy by acquiring Raman spectra from both chemical and biological samples. These spectra were then compared with Raman spectra obtained using a conventional dispersive Raman spectroscopy system. Approach The SS-Raman spectroscopy system used a wavelength-swept source laser (822 to 842 nm), a bandpass filter with a bandwidth of 1.5 nm, and a low-noise silicon photoreceiver. Raman spectra were acquired from various chemical samples, including phenylalanine, hydroxyapatite, glucose, and acetaminophen. A comparative analysis with the conventional dispersive Raman spectroscopy was conducted by calculating the correlation coefficients between the spectra from the SS-Raman spectroscopy and those from the conventional system. Furthermore, Raman mapping was obtained from cross-sections of swine tissue, demonstrating the applicability of the SS-Raman spectroscopy in biological samples. Results We developed a compact SS-Raman system and validated its performance by acquiring Raman spectra from both chemical and biological materials. Our straightforward signal processing method enhanced the quality of the Raman spectra without incurring high costs. Raman spectra in the range of 900 to 1200 cm - 1 were observed for phenylalanine, hydroxyapatite, glucose, and acetaminophen. The results were validated with correlation coefficients of 0.88, 0.84, 0.87, and 0.73, respectively, compared with those obtained from dispersive Raman spectroscopy. Furthermore, we performed scans across the cross-section of swine tissue to generate a biological tissue mapping plot, providing information about the composition of swine tissue. Conclusions We demonstrate the capabilities of the proposed compact SS-Raman spectroscopy system by obtaining Raman spectra of chemical and biological materials, utilizing straightforward signal processing. We anticipate that the SS-Raman spectroscopy will be utilized in various fields, including biomedical and chemical applications.
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Affiliation(s)
- Jeonggeun Song
- Korea Advanced Institute of Science and Technology, Department of Mechanical Engineering, Daejeon, Republic of Korea
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Peter T. C. So
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Hongki Yoo
- Korea Advanced Institute of Science and Technology, Department of Mechanical Engineering, Daejeon, Republic of Korea
| | - Jeon Woong Kang
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
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9
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Georgiev D, Pedersen SV, Xie R, Fernández-Galiana Á, Stevens MM, Barahona M. RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis. Anal Chem 2024; 96:8492-8500. [PMID: 38747470 PMCID: PMC11140669 DOI: 10.1021/acs.analchem.4c00383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/29/2024]
Abstract
Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardization, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of tools for spectroscopic analysis that supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning in Python. RamanSPy is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.
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Affiliation(s)
- Dimitar Georgiev
- Department
of Computing & UKRI Centre
for Doctoral Training in AI for Healthcare, Imperial College London, London SW7 2AZ, United
Kingdom
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Simon Vilms Pedersen
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Ruoxiao Xie
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Álvaro Fernández-Galiana
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Molly M. Stevens
- Department
of Materials, Department of Bioengineering & Institute of Biomedical
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Mauricio Barahona
- Department
of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
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10
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Kluz-Barłowska M, Kluz T, Paja W, Pancerz K, Łączyńska-Madera M, Miziak P, Cebulski J, Depciuch J. FT-Raman and FTIR spectroscopy as a tools showing marker of platinum-resistant phenomena in women suffering from ovarian cancer. Sci Rep 2024; 14:11025. [PMID: 38744861 PMCID: PMC11094164 DOI: 10.1038/s41598-024-61775-z] [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: 03/02/2024] [Accepted: 05/09/2024] [Indexed: 05/16/2024] Open
Abstract
Platinum-resistant phenomena in ovarian cancer is very dangerous for women suffering from this disease, because reduces the chances of complete recovery. Unfortunately, until now there are no methods to verify whether a woman with ovarian cancer is platinum-resistant. Importantly, histopathology images also were not shown differences in the ovarian cancer between platinum-resistant and platinum-sensitive tissues. Therefore, in this study, Fourier Transform InfraRed (FTIR) and FT-Raman spectroscopy techniques were used to find chemical differences between platinum-resistant and platinum-sensitive ovarian cancer tissues. Furthermore, Principal Component Analysis (PCA) and machine learning methods were performed to show if it possible to differentiate these two kind of tissues as well as to propose spectroscopy marker of platinum-resistant. Indeed, obtained results showed, that in platinum-resistant ovarian cancer tissues higher amount of phospholipids, proteins and lipids were visible, however when the ratio between intensities of peaks at 1637 cm-1 (FTIR) and at 2944 cm-1 (Raman) and every peaks in spectra was calculated, difference between groups of samples were not noticed. Moreover, structural changes visible as a shift of peaks were noticed for C-O-C, C-H bending and amide II bonds. PCA clearly showed, that PC1 can be used to differentiate platinum-resistant and platinum-sensitive ovarian cancer tissues, while two-trace two-dimensional correlation spectra (2T2D-COS) showed, that only in amide II, amide I and asymmetric CH lipids vibrations correlation between two analyzed types of tissues were noticed. Finally, machine learning algorithms showed, that values of accuracy, sensitivity and specificity were near to 100% for FTIR and around 95% for FT-Raman spectroscopy. Using decision tree peaks at 1777 cm-1, 2974 cm-1 (FTIR) and 1714 cm-1, 2817 cm-1 (FT-Raman) were proposed as spectroscopy marker of platinum-resistant.
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Affiliation(s)
- Marta Kluz-Barłowska
- Department of Pathology, Fryderyk Chopin University Hospital, F. Szopena 2, 35-055, Rzeszow, Poland.
| | - Tomasz Kluz
- Department of Gynecology, Gynecology Oncology and Obstetrics, Fryderyk Chopin University Hospital, F. Szopena 2, 35-055, Rzeszow, Poland
- Institute of Medical Sciences, Medical College of Rzeszow University, Kopisto 2a, 35-959, Rzeszow, Poland
| | - Wiesław Paja
- Institute of Computer Science, College of Natural Sciences, University of Rzeszow, Rzeszow, Poland
| | - Krzysztof Pancerz
- Institute of Philosophy, John Paul II Catholic University of Lublin, Lublin, Poland
| | - Monika Łączyńska-Madera
- Department of Gynecology, Gynecology Oncology and Obstetrics, Fryderyk Chopin University Hospital, F. Szopena 2, 35-055, Rzeszow, Poland
| | - Paulina Miziak
- Department of Biochemistry and Molecular Biology, Medical University of Lublin, 20-093, Lublin, Poland
| | - Jozef Cebulski
- Institute of Physics, College of Natural Sciences, University of Rzeszow, 35959, Rzeszow, Poland
| | - Joanna Depciuch
- Department of Biochemistry and Molecular Biology, Medical University of Lublin, 20-093, Lublin, Poland.
- Institute of Nuclear Physics Polish Academy of Sciences, 31342, Krakow, Poland.
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11
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Chu Q, Li F, Li X, Sun C, Sun Y, Jirigalantu, Song N, Yu S, Zhang R, Bayanheshig. Development of a high-resolution, broadband spatial heterodyne Raman spectrometer based on field-widened grating-echelle structure. OPTICS EXPRESS 2024; 32:17819-17836. [PMID: 38858953 DOI: 10.1364/oe.519704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 03/31/2024] [Indexed: 06/12/2024]
Abstract
We propose a spatial heterodyne Raman spectrometer (SHRS) based on a field-widened grating-echelle (FWGE). A normal grating is combined with an echelle grating in a conventional spatial heterodyne spectrometer to eliminate ghost images without using masks, and prevents interference among the spatial frequencies of different diffraction orders. Mathematical expressions and derivation processes are given for the spectral parameters in the FWGE-SHRS and a verification breadboard system is fabricated. The FWGE-SHRS measures Raman spectra of single chemicals and mixed targets with different integration times, laser powers, concentrations, and transparent containers. The results of the experiments demonstrate that the FWGE-SHRS is suitable for high-resolution, broadband Raman measurements for a wide range of applications.
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12
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Xu J, Morten KJ. Raman micro-spectroscopy as a tool to study immunometabolism. Biochem Soc Trans 2024; 52:733-745. [PMID: 38477393 PMCID: PMC11088913 DOI: 10.1042/bst20230794] [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: 12/05/2023] [Revised: 02/27/2024] [Accepted: 03/04/2024] [Indexed: 03/14/2024]
Abstract
In the past two decades, immunometabolism has emerged as a crucial field, unraveling the intricate molecular connections between cellular metabolism and immune function across various cell types, tissues, and diseases. This review explores the insights gained from studies using the emerging technology, Raman micro-spectroscopy, to investigate immunometabolism. Raman micro-spectroscopy provides an exciting opportunity to directly study metabolism at the single cell level where it can be combined with other Raman-based technologies and platforms such as single cell RNA sequencing. The review showcases applications of Raman micro-spectroscopy to study the immune system including cell identification, activation, and autoimmune disease diagnosis, offering a rapid, label-free, and minimally invasive analytical approach. The review spotlights three promising Raman technologies, Raman-activated cell sorting, Raman stable isotope probing, and Raman imaging. The synergy of Raman technologies with machine learning is poised to enhance the understanding of complex Raman phenotypes, enabling biomarker discovery and comprehensive investigations in immunometabolism. The review encourages further exploration of these evolving technologies in the rapidly advancing field of immunometabolism.
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Affiliation(s)
- Jiabao Xu
- Division of Biomedical Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8LT, U.K
| | - Karl J Morten
- Nuffield Department of Women's and Reproductive Health, University of Oxford, The Women Centre, John Radcliffe Hospital, Headley Way, Headington, Oxford OX3 9DU, U.K
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13
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Ilchenko O, Pilhun Y, Kutsyk A, Slobodianiuk D, Goksel Y, Dumont E, Vaut L, Mazzoni C, Morelli L, Boisen S, Stergiou K, Aulin Y, Rindzevicius T, Andersen TE, Lassen M, Mundhada H, Jendresen CB, Philipsen PA, Hædersdal M, Boisen A. Optics miniaturization strategy for demanding Raman spectroscopy applications. Nat Commun 2024; 15:3049. [PMID: 38589380 PMCID: PMC11001912 DOI: 10.1038/s41467-024-47044-7] [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: 07/25/2023] [Accepted: 03/12/2024] [Indexed: 04/10/2024] Open
Abstract
Raman spectroscopy provides non-destructive, label-free quantitative studies of chemical compositions at the microscale as used on NASA's Perseverance rover on Mars. Such capabilities come at the cost of high requirements for instrumentation. Here we present a centimeter-scale miniaturization of a Raman spectrometer using cheap non-stabilized laser diodes, densely packed optics, and non-cooled small sensors. The performance is comparable with expensive bulky research-grade Raman systems. It has excellent sensitivity, low power consumption, perfect wavenumber, intensity calibration, and 7 cm-1 resolution within the 400-4000 cm-1 range using a built-in reference. High performance and versatility are demonstrated in use cases including quantification of methanol in beverages, in-vivo Raman measurements of human skin, fermentation monitoring, chemical Raman mapping at sub-micrometer resolution, quantitative SERS mapping of the anti-cancer drug methotrexate and in-vitro bacteria identification. We foresee that the miniaturization will allow realization of super-compact Raman spectrometers for integration in smartphones and medical devices, democratizing Raman technology.
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Affiliation(s)
- Oleksii Ilchenko
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark.
- Lightnovo ApS, Birkerød, Denmark.
| | - Yurii Pilhun
- Lightnovo ApS, Birkerød, Denmark
- Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
| | - Andrii Kutsyk
- Lightnovo ApS, Birkerød, Denmark
- Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
- Technical University of Denmark, Department of Energy Conversion and Storage, Kgs. Lyngby, Denmark
| | - Denys Slobodianiuk
- Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
- Institute of Magnetism, Kyiv, Ukraine
| | - Yaman Goksel
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
| | - Elodie Dumont
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
| | - Lukas Vaut
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
| | - Chiara Mazzoni
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
| | - Lidia Morelli
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
| | | | | | | | - Tomas Rindzevicius
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
| | - Thomas Emil Andersen
- Department of Clinical Microbiology, Odense University Hospital and Research Unit of Clinical Microbiology, University of Southern Denmark, Odense, Denmark
| | | | | | | | | | - Merete Hædersdal
- Department of Dermatology, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Copenhagen University, Copenhagen, Denmark
| | - Anja Boisen
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
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14
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Vardaki MZ, Gregoriou VG, Chochos CL. Biomedical applications, perspectives and tag design concepts in the cell - silent Raman window. RSC Chem Biol 2024; 5:273-292. [PMID: 38576725 PMCID: PMC10989507 DOI: 10.1039/d3cb00217a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 02/12/2024] [Indexed: 04/06/2024] Open
Abstract
Spectroscopic studies increasingly employ Raman tags exhibiting a signal in the cell - silent region of the Raman spectrum (1800-2800 cm-1), where bands arising from biological molecules are inherently absent. Raman tags bearing functional groups which contain a triple bond, such as alkyne and nitrile or a carbon-deuterium bond, have a distinct vibrational frequency in this region. Due to the lack of spectral background and cell-associated bands in the specific area, the implementation of those tags can help overcome the inherently poor signal-to-noise ratio and presence of overlapping Raman bands in measurements of biological samples. The cell - silent Raman tags allow for bioorthogonal imaging of biomolecules with improved chemical contrast and they have found application in analyte detection and monitoring, biomarker profiling and live cell imaging. This review focuses on the potential of the cell - silent Raman region, reporting on the tags employed for biomedical applications using variants of Raman spectroscopy.
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Affiliation(s)
- Martha Z Vardaki
- Institute of Chemical Biology, National Hellenic Research Foundation, 48 Vassileos Constantinou Avenue Athens 11635 Greece
| | - Vasilis G Gregoriou
- Institute of Chemical Biology, National Hellenic Research Foundation, 48 Vassileos Constantinou Avenue Athens 11635 Greece
- Advent Technologies SA, Stadiou Street, Platani Rio Patras 26504 Greece
| | - Christos L Chochos
- Institute of Chemical Biology, National Hellenic Research Foundation, 48 Vassileos Constantinou Avenue Athens 11635 Greece
- Advent Technologies SA, Stadiou Street, Platani Rio Patras 26504 Greece
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15
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Parham E, Rousseau A, Quémener M, Parent M, Côté DC. Wavelength-swept spontaneous Raman spectroscopy system improves fiber-based collection efficiency for whole brain tissue classification. NEUROPHOTONICS 2024; 11:025007. [PMID: 38898963 PMCID: PMC11185955 DOI: 10.1117/1.nph.11.2.025007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/21/2024]
Abstract
Significance Raman spectroscopy is a valuable technique for tissue identification, but its conventional implementation is hindered by low efficiency due to scattering. Addressing this limitation, we are further developing the wavelength-swept Raman spectroscopy approach. Aim We aim to enhance Raman signal detection by employing a laser capable of sweeping over a wide wavelength range to sequentially excite tissue with different wavelengths, paired with a photodetector featuring a fixed narrow-bandpass filter for collecting the Raman signal at a specific wavelength. Approach We experimentally validate our technique using a fiber-based swept-source Raman spectroscopy setup. In addition, simulations are conducted to assess the efficacy of our approach in comparison with conventional spectrometer-based Raman spectroscopy. Results Our simulations reveal that the wavelength-swept configuration leads to a significantly stronger signal compared with conventional spectrometer-based Raman spectroscopy. Experimentally, our setup demonstrates an improvement of at least 200× in photon detection compared with the spectrometer-based setup. Furthermore, data acquired from different regions of a fixed monkey brain using our technique achieves 99% accuracy in classification via k -nearest neighbor analysis. Conclusions Our study showcases the potential of wavelength-swept Raman spectroscopy for tissue identification, particularly in highly scattering media, such as the brain. The developed technique offers enhanced signal detection capabilities, paving the way for future in vivo applications in tissue characterization.
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Affiliation(s)
- Elahe Parham
- CERVO Brain Research Center, Québec City, Québec, Canada
- Université Laval, Centre d’optique, photonique et laser, Québec City, Québec, Canada
| | - Antoine Rousseau
- CERVO Brain Research Center, Québec City, Québec, Canada
- Université Laval, Centre d’optique, photonique et laser, Québec City, Québec, Canada
| | - Mireille Quémener
- CERVO Brain Research Center, Québec City, Québec, Canada
- Université Laval, Centre d’optique, photonique et laser, Québec City, Québec, Canada
| | - Martin Parent
- CERVO Brain Research Center, Québec City, Québec, Canada
| | - Daniel C. Côté
- CERVO Brain Research Center, Québec City, Québec, Canada
- Université Laval, Centre d’optique, photonique et laser, Québec City, Québec, Canada
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16
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Yan Z, Chang C, Kang Z, Chen C, Lv X, Chen C. Application of one-dimensional hierarchical network assisted screening for cervical cancer based on Raman spectroscopy combined with attention mechanism. Photodiagnosis Photodyn Ther 2024; 46:104086. [PMID: 38608802 DOI: 10.1016/j.pdpdt.2024.104086] [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: 02/27/2024] [Revised: 04/07/2024] [Accepted: 04/10/2024] [Indexed: 04/14/2024]
Abstract
Cervical cancer is one of the most common malignant tumors among women, and its pathological change is a relatively slow process. If it can be detected in time and treated properly, it can effectively reduce the incidence rate and mortality rate of cervical cancer, so the early screening of cervical cancer is particularly critical and significant. In this paper, we used Raman spectroscopy technology to collect the tissue sample data of patients with cervicitis, Low-grade Squamous Intraepithelial Lesion, High-grade Squamous Intraepithelial Lesion, Well differentiated squamous cell carcinoma, Moderately differentiated squamous cell carcinoma, Poorly differentiated squamous cell carcinoma and cervical adenocarcinoma. A one-dimensional hierarchical convolutional neural network based on attention mechanism was constructed to classify and identify seven types of tissue samples. The attention mechanism Efficient Channel Attention Networks module and Squeeze-and-Excitation Networks module were combined with the established one-dimensional convolutional hierarchical network model, and the results showed that the combined model had better diagnostic performance. The average accuracy, F1, and AUC of the Principal Component Analysis-Squeeze and Excitation-hierarchical network model after 5-fold cross validations could reach 96.49%±2.12%, 0.97±0.03, and 0.98±0.02, respectively, which were 1.58%, 0.0140, and 0.008 higher than those of hierarchical network. The recall rate of the Principal Component Analysis-Efficient Channel Attention-hierarchical network model was as high as 96.78%±2.85%, which is 1.47% higher than hierarchical network. Compared with the classification results of traditional CNN and ResNet for seven types of cervical cancer staging, the accuracy of the Principal Component Analysis-Squeeze and Excitation-hierarchical network model is 3.33% and 11.05% higher, respectively. The experimental results indicate that the model established in this study is easy to operate and has high accuracy. It has good reference value for rapid screening of cervical cancer, laying a foundation for further research on Raman spectroscopy as a clinical diagnostic method for cervical cancer.
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Affiliation(s)
- Ziwei Yan
- College of Software, Xinjiang University, Urumqi, China
| | - Chenjie Chang
- School of Computer Science and Technology, Xinjiang University, Urumqi, China
| | - Zhenping Kang
- School of Computer Science and Technology, Xinjiang University, Urumqi, China
| | - Chen Chen
- College of Software, Xinjiang University, Urumqi, China
| | - Xiaoyi Lv
- School of Computer Science and Technology, Xinjiang University, Urumqi, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, China.
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17
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Klamminger GG, Mombaerts L, Kemp F, Jelke F, Klein K, Slimani R, Mirizzi G, Husch A, Hertel F, Mittelbronn M, Kleine Borgmann FB. Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy. Brain Sci 2024; 14:301. [PMID: 38671953 PMCID: PMC11048578 DOI: 10.3390/brainsci14040301] [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: 02/26/2024] [Revised: 03/15/2024] [Accepted: 03/22/2024] [Indexed: 04/28/2024] Open
Abstract
Raman spectroscopy (RS) has demonstrated its utility in neurooncological diagnostics, spanning from intraoperative tumor detection to the analysis of tissue samples peri- and postoperatively. In this study, we employed Raman spectroscopy (RS) to monitor alterations in the molecular vibrational characteristics of a broad range of formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms (including primary brain tumors and meningiomas, as well as brain metastases) and considered specific challenges when employing RS on FFPE tissue during the routine neuropathological workflow. We spectroscopically measured 82 intracranial neoplasms on CaF2 slides (in total, 679 individual measurements) and set up a machine learning framework to classify spectral characteristics by splitting our data into training cohorts and external validation cohorts. The effectiveness of our machine learning algorithms was assessed by using common performance metrics such as AUROC and AUPR values. With our trained random forest algorithms, we distinguished among various types of gliomas and identified the primary origin in cases of brain metastases. Moreover, we spectroscopically diagnosed tumor types by using biopsy fragments of pure necrotic tissue, a task unattainable through conventional light microscopy. In order to address misclassifications and enhance the assessment of our models, we sought out significant Raman bands suitable for tumor identification. Through the validation phase, we affirmed a considerable complexity within the spectroscopic data, potentially arising not only from the biological tissue subjected to a rigorous chemical procedure but also from residual components of the fixation and paraffin-embedding process. The present study demonstrates not only the potential applications but also the constraints of RS as a diagnostic tool in neuropathology, considering the challenges associated with conducting vibrational spectroscopic analysis on formalin-fixed, paraffin-embedded (FFPE) tissue.
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Affiliation(s)
- Gilbert Georg Klamminger
- Department of General and Special Pathology, Saarland University (USAAR), 66424 Homburg, Germany
- Department of General and Special Pathology, Saarland University Medical Center (UKS), 66424 Homburg, Germany
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
| | - Laurent Mombaerts
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
| | - Françoise Kemp
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
| | - Finn Jelke
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Karoline Klein
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
| | - Rédouane Slimani
- Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
| | - Giulia Mirizzi
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
| | - Andreas Husch
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
| | - Michel Mittelbronn
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
- Department of Life Sciences and Medicine (DLSM), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
- Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
| | - Felix B. Kleine Borgmann
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
- Hôpitaux Robert Schuman, 1130 Luxembourg, Luxembourg
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18
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Liu Y, Ye F, Yang C, Jiang H. Use of in vivo Raman spectroscopy and cryoablation for diagnosis and treatment of bladder cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 308:123707. [PMID: 38043292 DOI: 10.1016/j.saa.2023.123707] [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: 08/19/2023] [Revised: 11/13/2023] [Accepted: 11/27/2023] [Indexed: 12/05/2023]
Abstract
Transurethral resection of bladder tumor (TURBT) is the first-line treatment option for non-muscle invasive bladder cancer (NMIBC), but residual tumor often remains after TURBT, thereby leading to cancer recurrence. Here, we introduce combined use of in vivo Raman spectroscopy and in vivo cryoablation as a new approach to detect and remove residual bladder tumor during TURBT. Bladder cancer (BCa) patients treated with TURBT at our urological department between Dec 2019 and Jan 2021 were collected. First, Raman signals were collected from 74 BCa patients to build reference spectra of normal bladder tissue and of bladder cancers of different pathological types. Then, another 53 BCa patients were randomly categorized into two groups, 26 patients accepted traditional TURBT, 27 patients accepted TURBT followed by Raman scanning and cryoablation if Raman detected existence of residual tumor. The recurrence rates of the two groups until Oct 2022 were compared. Raman was capable of discriminating normal bladder tissue and BCa with a sensitivity and specificity of 90.5% and 80.8 %; and discriminating invasive (T1, T2) and noninvasive (Ta) BCa with a sensitivity and specificity of 83.3 % and 87.3 %. During follow-up, 2 in 27 patients had cancer recurrence in Raman-Cryoablation group, while 8 in 26 patients had cancer recurrence in traditional TURBT group. Combined use of Raman and cryoablation significantly reduced cancer recurrence (p = 0.0394). Raman and cryoablation can serve as an adjuvant therapy to TURBT to improve therapeutic effects and reduce recurrence rate.
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Affiliation(s)
- Yufei Liu
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, China.
| | - Fangdie Ye
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Chen Yang
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Haowen Jiang
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, China.
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19
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Allakhverdiev ES, Kossalbayev BD, Sadvakasova AK, Bauenova MO, Belkozhayev AM, Rodnenkov OV, Martynyuk TV, Maksimov GV, Allakhverdiev SI. Spectral insights: Navigating the frontiers of biomedical and microbiological exploration with Raman spectroscopy. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY. B, BIOLOGY 2024; 252:112870. [PMID: 38368635 DOI: 10.1016/j.jphotobiol.2024.112870] [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: 11/24/2023] [Revised: 01/04/2024] [Accepted: 02/14/2024] [Indexed: 02/20/2024]
Abstract
Raman spectroscopy (RS), a powerful analytical technique, has gained increasing recognition and utility in the fields of biomedical and biological research. Raman spectroscopic analyses find extensive application in the field of medicine and are employed for intricate research endeavors and diagnostic purposes. Consequently, it enjoys broad utilization within the realm of biological research, facilitating the identification of cellular classifications, metabolite profiling within the cellular milieu, and the assessment of pigment constituents within microalgae. This article also explores the multifaceted role of RS in these domains, highlighting its distinct advantages, acknowledging its limitations, and proposing strategies for enhancement.
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Affiliation(s)
- Elvin S Allakhverdiev
- National Medical Research Center of Cardiology named after academician E.I. Chazov, Academician Chazov 15А St., Moscow 121552, Russia; Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, Leninskie Gory 1/12, Moscow 119991, Russia.
| | - Bekzhan D Kossalbayev
- Ecology Research Institute, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkistan, Kazakhstan; Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, No. 32, West 7th Road, Tianjin Airport Economic Area, 300308 Tianjin, China; Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050038, Kazakhstan; Department of Chemical and Biochemical Engineering, Institute of Geology and Oil-Gas Business Institute Named after K. Turyssov, Satbayev University, Almaty 050043, Kazakhstan
| | - Asemgul K Sadvakasova
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050038, Kazakhstan
| | - Meruyert O Bauenova
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050038, Kazakhstan
| | - Ayaz M Belkozhayev
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050038, Kazakhstan; Department of Chemical and Biochemical Engineering, Institute of Geology and Oil-Gas Business Institute Named after K. Turyssov, Satbayev University, Almaty 050043, Kazakhstan; M.A. Aitkhozhin Institute of Molecular Biology and Biochemistry, Almaty 050012, Kazakhstan
| | - Oleg V Rodnenkov
- National Medical Research Center of Cardiology named after academician E.I. Chazov, Academician Chazov 15А St., Moscow 121552, Russia
| | - Tamila V Martynyuk
- National Medical Research Center of Cardiology named after academician E.I. Chazov, Academician Chazov 15А St., Moscow 121552, Russia
| | - Georgy V Maksimov
- Department of Biophysics, Faculty of Biology, Lomonosov Moscow State University, Moscow, Leninskie Gory 1/12, Moscow 119991, Russia
| | - Suleyman I Allakhverdiev
- K.A. Timiryazev Institute of Plant Physiology, Russian Academy of Sciences, Botanicheskaya Street 35, Moscow 127276, Russia; Institute of Basic Biological Problems, FRC PSCBR Russian Academy of Sciences, Pushchino 142290, Russia; Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey.
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20
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Klein K, Klamminger GG, Mombaerts L, Jelke F, Arroteia IF, Slimani R, Mirizzi G, Husch A, Frauenknecht KBM, Mittelbronn M, Hertel F, Kleine Borgmann FB. Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms. Molecules 2024; 29:979. [PMID: 38474491 DOI: 10.3390/molecules29050979] [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: 11/13/2023] [Revised: 12/24/2023] [Accepted: 02/07/2024] [Indexed: 03/14/2024] Open
Abstract
Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas-vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%-but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control.
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Affiliation(s)
- Karoline Klein
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
| | - Gilbert Georg Klamminger
- Department of General and Special Pathology, Saarland University (USAAR), 66424 Homburg, Germany
- Department of General and Special Pathology, Saarland University Medical Center (UKS), 66424 Homburg, Germany
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
| | - Laurent Mombaerts
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Finn Jelke
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Isabel Fernandes Arroteia
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
| | - Rédouane Slimani
- Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
| | - Giulia Mirizzi
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
| | - Andreas Husch
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
| | - Katrin B M Frauenknecht
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
| | - Michel Mittelbronn
- National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
- Department of Life Sciences and Medicine (DLSM), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
- Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
| | - Felix B Kleine Borgmann
- Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
- Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
- Hôpitaux Robert Schuman, 1130 Luxembourg, Luxembourg
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21
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Gao C, Fan Q, Zhao P, Sun C, Dang R, Feng Y, Hu B, Wang Q. Spectral encoder to extract the efficient features of Raman spectra for reliable and precise quantitative analysis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 312:124036. [PMID: 38367343 DOI: 10.1016/j.saa.2024.124036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/04/2024] [Accepted: 02/10/2024] [Indexed: 02/19/2024]
Abstract
Raman spectroscopy has become a powerful analytical tool highly demanded in many applications such as microorganism sample analysis, food quality control, environmental science, and pharmaceutical analysis, owing to its non-invasiveness, simplicity, rapidity and ease of use. Among them, quantitative research using Raman spectroscopy is a crucial application field of spectral analysis. However, the entire process of quantitative modeling largely relies on the extraction of effective spectral features, particularly for measurements on complex samples or in environments with poor spectral signal quality. In this paper, we propose a method of utilizing a spectral encoder to extract effective spectral features, which can significantly enhance the reliability and precision of quantitative analysis. We built a latent encoded feature regression model; in the process of utilizing the autoencoder for reconstructing the spectrometer output, the latent feature obtained from the intermediate bottleneck layer is extracted. Then, these latent features are fed into a deep regression model for component concentration prediction. Through detailed ablation and comparative experiments, our proposed model demonstrates superior performance to common methods on single-component and multi-component mixture datasets, remarkably improving regression precision while without needing user-selected parameters and eliminating the interference of irrelevant and redundant information. Furthermore, in-depth analysis reveals that latent encoded feature possesses strong nonlinear feature representation capabilities, low computational costs, wide adaptability, and robustness against noise interference. This highlights its effectiveness in spectral regression tasks and indicates its potential in other application fields. Sufficient experimental results show that our proposed method provides a novel and effective feature extraction approach for spectral analysis, which is simple, suitable for various methods, and can meet the measurement needs of different real-world scenarios.
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Affiliation(s)
- Chi Gao
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qi Fan
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China
| | - Peng Zhao
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chao Sun
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China
| | - Ruochen Dang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yutao Feng
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China
| | - Bingliang Hu
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China
| | - Quan Wang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China.
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22
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Liu J, Wang P, Zhang H, Wu N. Distinguishing brain tumors by Label-free confocal micro-Raman spectroscopy. Photodiagnosis Photodyn Ther 2024; 45:104010. [PMID: 38336147 DOI: 10.1016/j.pdpdt.2024.104010] [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: 11/26/2023] [Revised: 01/31/2024] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Brain tumors have serious adverse effects on public health and social economy. Accurate detection of brain tumor types is critical for effective and proactive treatment, and thus improve the survival of patients. METHODS Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors. RESULTS Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm-1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %. CONCLUSIONS Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy.
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Affiliation(s)
- Jie Liu
- Chongqing Medical University, Chongqing, 400016, China; Department of Neurosurgery, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China; Chongqing School, University of Academy of Sciences, Chongqing, 400714, China
| | - Pan Wang
- Department of Neurosurgery, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China
| | - Hua Zhang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China
| | - Nan Wu
- Chongqing Medical University, Chongqing, 400016, China; Department of Neurosurgery, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China; Chongqing School, University of Academy of Sciences, Chongqing, 400714, China.
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23
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Chang M, He C, Du Y, Qiu Y, Wang L, Chen H. RaT: Raman Transformer for highly accurate melanoma detection with critical features visualization. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123475. [PMID: 37806238 DOI: 10.1016/j.saa.2023.123475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/27/2023] [Accepted: 09/29/2023] [Indexed: 10/10/2023]
Abstract
Melanoma is an important cause of death from skin cancer. Early and accurate diagnosis can effectively reduce mortality. But the current diagnosis relies on the experience of pathologists, increasing the rate of misdiagnosis. In this paper, Raman Transformer (RaT) model is proposed by combining Raman spectroscopy and a Transformer encoder to distinguish the Raman spectra of melanoma and normal tissue. To make the spectral data more suitable for the Transformer encoder, we split the Raman spectrum into segments and map them into block vectors, which are then input into the Transformer encoder and classified using the multi-head self-attention mechanism and the Multilayer Perceptron (MLP). The RaT model achieves 99.69% accuracy, 99.61% sensitivity, and 99.82% specificity, which is higher than the classical principal component analysis with the neural network (PCA + NNET) method. In addition, we visualize and explain the fingerprint peaks found by the RaT model and their corresponding biological information. Our proposed RaT model provides a novel and reliable method for processing Raman spectral data, which is expected to help distinguish melanoma from normal cells, diagnose other diseases, and save human lives.
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Affiliation(s)
- Min Chang
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Chen He
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yi Du
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Yemin Qiu
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Luyao Wang
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Hui Chen
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education, University of Shanghai for Science and Technology, Shanghai 200093, China
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24
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Issatayeva A, Farnesi E, Cialla-May D, Schmitt M, Rizzi FMA, Milanese D, Selleri S, Cucinotta A. SERS-based methods for the detection of genomic biomarkers of cancer. Talanta 2024; 267:125198. [PMID: 37722343 DOI: 10.1016/j.talanta.2023.125198] [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: 04/24/2023] [Revised: 09/05/2023] [Accepted: 09/10/2023] [Indexed: 09/20/2023]
Abstract
Genomic biomarkers of cancer are based on changes in nucleic acids, which include abnormal expression levels of some miRNAs, point mutations in DNA sequences, and altered levels of DNA methylation. The presence of tumor-related nucleic acids in body fluids (blood, saliva, or urine) makes it possible to achieve a non-invasive early-stage cancer diagnosis. Currently existing techniques for the discovery of nucleic acids require complex, time-consuming, costly assays and have limited multiplexing abilities. Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopy technique that is able to provide molecular specificity combined with trace sensitivity. SERS has gained research attention as a tool for the detection of nucleic acids because of its promising potential: label-free SERS can decrease the complexity of assays currently used with fluorescence-based detection due to the absence of the label, while labeled SERS may outperform the gold standard in terms of the multiplexing ability. The first papers about SERS-based methods for the measurement of genomic biomarkers were written in 2008, and since then, more than 150 papers have been published. The aim of this paper is to review and evaluate the proposed SERS-based methods in terms of their level of development and their potential for liquid biopsy application, as well as to contribute to their further evolution by attracting research attention to the field. This goal will be reached by grouping, on the basis of their experimental protocol, all the published manuscripts on the topic and evaluating each group in terms of its limit of detection and applicability to real body fluids. Thus, the methods are classified according to their working principles into five main groups, including capture-based, displacement-based, sandwich-based, enzyme-assisted, and specialized protocols.
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Affiliation(s)
- Aizhan Issatayeva
- Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/a, 43124, Parma, Italy.
| | - Edoardo Farnesi
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743, Jena, Germany; Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745, Jena, Germany
| | - Dana Cialla-May
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743, Jena, Germany; Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745, Jena, Germany
| | - Michael Schmitt
- Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743, Jena, Germany
| | | | - Daniel Milanese
- Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/a, 43124, Parma, Italy
| | - Stefano Selleri
- Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/a, 43124, Parma, Italy
| | - Annamaria Cucinotta
- Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/a, 43124, Parma, Italy
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25
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Elsheikh S, Coles NP, Achadu OJ, Filippou PS, Khundakar AA. Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects. BIOSENSORS 2024; 14:33. [PMID: 38248410 PMCID: PMC10813143 DOI: 10.3390/bios14010033] [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: 11/21/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer's and Parkinson's diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research.
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Affiliation(s)
- Suzan Elsheikh
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
| | - Nathan P. Coles
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
| | - Ojodomo J. Achadu
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
- School of Health and Life Science, Teesside University, Campus Heart, Southfield Rd, Middlesbrough TS1 3BX, UK
| | - Panagiota S. Filippou
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
- School of Health and Life Science, Teesside University, Campus Heart, Southfield Rd, Middlesbrough TS1 3BX, UK
| | - Ahmad A. Khundakar
- National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington DL1 1HG, UK (N.P.C.); (O.J.A.); (P.S.F.)
- School of Health and Life Science, Teesside University, Campus Heart, Southfield Rd, Middlesbrough TS1 3BX, UK
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
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26
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Tipatet K, Du Boulay I, Muir H, Davison-Gates L, Ellederová Z, Downes A. Raman spectroscopy of brain and skin tissue in a minipig model of Huntington's disease. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:253-261. [PMID: 38108410 DOI: 10.1039/d3ay00970j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
We applied Raman spectroscopy to brain and skin tissues from a minipig model of Huntington's disease. Differences were observed between measured spectra of tissues with and without Huntington's disease, for both brain tissue and skin tissue. There are linked to changes in the chemical composition between tissue types. Using machine learning we correctly classified 96% of test spectra as diseased or wild type, indicating that the test would have a similar accuracy when used as a diagnostic tool for the disease. This suggests the technique has great potential in the rapid and accurate diagnosis of Huntington's and other neurodegenerative diseases in a clinical setting.
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Affiliation(s)
- Kevin Tipatet
- a, Institute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh EH9 3DW, UK.
| | - Isla Du Boulay
- a, Institute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh EH9 3DW, UK.
| | - Hamish Muir
- a, Institute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh EH9 3DW, UK.
| | - Liam Davison-Gates
- a, Institute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh EH9 3DW, UK.
| | - Zdenka Ellederová
- a, Institute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh EH9 3DW, UK.
- Institute of Animal Physiology and Genetics, Czech Academy of Sciences, Rumburská 89, 277 21 Liběchov, UK
| | - Andrew Downes
- a, Institute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh EH9 3DW, UK.
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27
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Bassler MC, Knoblich M, Gerhard-Hartmann E, Mukherjee A, Youssef A, Hagen R, Haug L, Goncalves M, Scherzad A, Stöth M, Ostertag E, Steinke M, Brecht M, Hackenberg S, Meyer TJ. Differentiation of Salivary Gland and Salivary Gland Tumor Tissue via Raman Imaging Combined with Multivariate Data Analysis. Diagnostics (Basel) 2023; 14:92. [PMID: 38201401 PMCID: PMC10795677 DOI: 10.3390/diagnostics14010092] [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: 11/06/2023] [Revised: 12/10/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
Salivary gland tumors (SGTs) are a relevant, highly diverse subgroup of head and neck tumors whose entity determination can be difficult. Confocal Raman imaging in combination with multivariate data analysis may possibly support their correct classification. For the analysis of the translational potential of Raman imaging in SGT determination, a multi-stage evaluation process is necessary. By measuring a sample set of Warthin tumor, pleomorphic adenoma and non-tumor salivary gland tissue, Raman data were obtained and a thorough Raman band analysis was performed. This evaluation revealed highly overlapping Raman patterns with only minor spectral differences. Consequently, a principal component analysis (PCA) was calculated and further combined with a discriminant analysis (DA) to enable the best possible distinction. The PCA-DA model was characterized by accuracy, sensitivity, selectivity and precision values above 90% and validated by predicting model-unknown Raman spectra, of which 93% were classified correctly. Thus, we state our PCA-DA to be suitable for parotid tumor and non-salivary salivary gland tissue discrimination and prediction. For evaluation of the translational potential, further validation steps are necessary.
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Affiliation(s)
- Miriam C. Bassler
- Process Analysis and Technology (PA&T), School of Life Science, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany; (M.C.B.); (M.K.); (A.M.); (E.O.)
- Institute of Physical and Theoretical Chemistry, Faculty of Science, University of Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany
| | - Mona Knoblich
- Process Analysis and Technology (PA&T), School of Life Science, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany; (M.C.B.); (M.K.); (A.M.); (E.O.)
- Institute of Physical and Theoretical Chemistry, Faculty of Science, University of Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany
| | - Elena Gerhard-Hartmann
- Institute of Pathology, University of Würzburg, Josef-Schneider-Str. 2, 97080 Würzburg, Germany; (E.G.-H.); (A.Y.); (L.H.)
| | - Ashutosh Mukherjee
- Process Analysis and Technology (PA&T), School of Life Science, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany; (M.C.B.); (M.K.); (A.M.); (E.O.)
- Institute of Physical and Theoretical Chemistry, Faculty of Science, University of Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany
| | - Almoatazbellah Youssef
- Institute of Pathology, University of Würzburg, Josef-Schneider-Str. 2, 97080 Würzburg, Germany; (E.G.-H.); (A.Y.); (L.H.)
| | - Rudolf Hagen
- Department of Oto-Rhino-Laryngology, Plastic, Aesthetic & Reconstructive Head and Neck Surgery, University Hospital Würzburg, Josef-Schneider-Str. 11, 97080 Würzburg, Germany; (R.H.); (M.G.); (A.S.); (M.S.); (S.H.)
| | - Lukas Haug
- Institute of Pathology, University of Würzburg, Josef-Schneider-Str. 2, 97080 Würzburg, Germany; (E.G.-H.); (A.Y.); (L.H.)
| | - Miguel Goncalves
- Department of Oto-Rhino-Laryngology, Plastic, Aesthetic & Reconstructive Head and Neck Surgery, University Hospital Würzburg, Josef-Schneider-Str. 11, 97080 Würzburg, Germany; (R.H.); (M.G.); (A.S.); (M.S.); (S.H.)
| | - Agmal Scherzad
- Department of Oto-Rhino-Laryngology, Plastic, Aesthetic & Reconstructive Head and Neck Surgery, University Hospital Würzburg, Josef-Schneider-Str. 11, 97080 Würzburg, Germany; (R.H.); (M.G.); (A.S.); (M.S.); (S.H.)
| | - Manuel Stöth
- Department of Oto-Rhino-Laryngology, Plastic, Aesthetic & Reconstructive Head and Neck Surgery, University Hospital Würzburg, Josef-Schneider-Str. 11, 97080 Würzburg, Germany; (R.H.); (M.G.); (A.S.); (M.S.); (S.H.)
| | - Edwin Ostertag
- Process Analysis and Technology (PA&T), School of Life Science, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany; (M.C.B.); (M.K.); (A.M.); (E.O.)
| | - Maria Steinke
- Chair of Tissue Engineering and Regenerative Medicine, University Hospital Würzburg, Röntgenring 11, 97070 Würzburg, Germany;
- Fraunhofer Institute for Silicate Research ISC, Röntgenring 11, 97070 Würzburg, Germany
| | - Marc Brecht
- Process Analysis and Technology (PA&T), School of Life Science, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany; (M.C.B.); (M.K.); (A.M.); (E.O.)
- Institute of Physical and Theoretical Chemistry, Faculty of Science, University of Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany
| | - Stephan Hackenberg
- Department of Oto-Rhino-Laryngology, Plastic, Aesthetic & Reconstructive Head and Neck Surgery, University Hospital Würzburg, Josef-Schneider-Str. 11, 97080 Würzburg, Germany; (R.H.); (M.G.); (A.S.); (M.S.); (S.H.)
| | - Till Jasper Meyer
- Department of Oto-Rhino-Laryngology, Plastic, Aesthetic & Reconstructive Head and Neck Surgery, University Hospital Würzburg, Josef-Schneider-Str. 11, 97080 Würzburg, Germany; (R.H.); (M.G.); (A.S.); (M.S.); (S.H.)
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Su Z, Tan P, Zhang J, Wang P, Zhu S, Jiang N. Understanding the Mechanics of the Temporomandibular Joint Osteochondral Interface from Micro- and Nanoscopic Perspectives. NANO LETTERS 2023; 23:11702-11709. [PMID: 38060440 DOI: 10.1021/acs.nanolett.3c03564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
The condylar cartilage of the temporomandibular joint (TMJ) is connected to the subchondral bone by an osteochondral interface that transmits loads without causing fatigue damage. However, the microstructure, composition, and mechanical properties of this interface remain elusive. In this study, we found that structurally, a spatial gradient assembly of hydroxyapatite (HAP) particles exists in the osteochondral interface, with increasing volume of apatite crystals with depth and a tendency to form denser and stacked structures. Combined with nanoindentation, this complex assembly of nanoscale structures and components enhanced energy dissipation at the osteochondral interface, achieving a smooth stress transition between soft and hard tissues. This study comprehensively demonstrates the elemental composition and complex nanogradient spatial assembly of the osteochondral interface at the ultramicroscopic scale, providing a basis for exploring the construction of complex mechanical models of the interfacial region.
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Affiliation(s)
- Zhan Su
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Orthognathic and TMJ Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan, China
| | - Peijie Tan
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Orthognathic and TMJ Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan, China
| | - Jie Zhang
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Orthognathic and TMJ Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan, China
| | - Peng Wang
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Orthognathic and TMJ Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan, China
| | - Songsong Zhu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Orthognathic and TMJ Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan, China
| | - Nan Jiang
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Orthognathic and TMJ Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan, China
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Vlocskó M, Piffkó J, Janovszky Á. Intraoperative Assessment of Resection Margin in Oral Cancer: The Potential Role of Spectroscopy. Cancers (Basel) 2023; 16:121. [PMID: 38201548 PMCID: PMC10777979 DOI: 10.3390/cancers16010121] [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: 10/25/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024] Open
Abstract
In parallel with the increasing number of oncological cases, the need for faster and more efficient diagnostic tools has also appeared. Different diagnostic approaches are available, such as radiological imaging or histological staining methods, but these do not provide adequate information regarding the resection margin, intraoperatively, or are time consuming. The purpose of this review is to summarize the current knowledge on spectrometric diagnostic modalities suitable for intraoperative use, with an emphasis on their relevance in the management of oral cancer. The literature agrees on the sensitivity, specificity, and accuracy of spectrometric diagnostic modalities, but further long-term prospective, multicentric clinical studies are needed, which may standardize the intraoperative assessment of the resection margin and the use of real-time spectroscopic approaches.
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Affiliation(s)
| | | | - Ágnes Janovszky
- Department of Oral and Maxillofacial Surgery, Albert Szent-Györgyi Medical School, University of Szeged, Kálvária 57, H-6725 Szeged, Hungary; (M.V.); (J.P.)
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Liu Y, Chen C, Xie X, Lv X, Chen C. For cervical cancer diagnosis: Tissue Raman spectroscopy and multi-level feature fusion with SENet attention mechanism. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 303:123147. [PMID: 37517264 DOI: 10.1016/j.saa.2023.123147] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 08/01/2023]
Abstract
Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer.
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Affiliation(s)
- Yang Liu
- College of Software, Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Karamay 834099, China
| | - Xiaodong Xie
- Xinjiang Uygur Autonomous Region People's Hospital, Urumqi 830046, China.
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China; Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
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31
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Thenuwara G, Curtin J, Tian F. Advances in Diagnostic Tools and Therapeutic Approaches for Gliomas: A Comprehensive Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:9842. [PMID: 38139688 PMCID: PMC10747598 DOI: 10.3390/s23249842] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/07/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023]
Abstract
Gliomas, a prevalent category of primary malignant brain tumors, pose formidable clinical challenges due to their invasive nature and limited treatment options. The current therapeutic landscape for gliomas is constrained by a "one-size-fits-all" paradigm, significantly restricting treatment efficacy. Despite the implementation of multimodal therapeutic strategies, survival rates remain disheartening. The conventional treatment approach, involving surgical resection, radiation, and chemotherapy, grapples with substantial limitations, particularly in addressing the invasive nature of gliomas. Conventional diagnostic tools, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), play pivotal roles in outlining tumor characteristics. However, they face limitations, such as poor biological specificity and challenges in distinguishing active tumor regions. The ongoing development of diagnostic tools and therapeutic approaches represents a multifaceted and promising frontier in the battle against this challenging brain tumor. The aim of this comprehensive review is to address recent advances in diagnostic tools and therapeutic approaches for gliomas. These innovations aim to minimize invasiveness while enabling the precise, multimodal targeting of localized gliomas. Researchers are actively developing new diagnostic tools, such as colorimetric techniques, electrochemical biosensors, optical coherence tomography, reflectometric interference spectroscopy, surface-enhanced Raman spectroscopy, and optical biosensors. These tools aim to regulate tumor progression and develop precise treatment methods for gliomas. Recent technological advancements, coupled with bioelectronic sensors, open avenues for new therapeutic modalities, minimizing invasiveness and enabling multimodal targeting with unprecedented precision. The next generation of multimodal therapeutic strategies holds potential for precision medicine, aiding the early detection and effective management of solid brain tumors. These innovations offer promise in adopting precision medicine methodologies, enabling early disease detection, and improving solid brain tumor management. This review comprehensively recognizes the critical role of pioneering therapeutic interventions, holding significant potential to revolutionize brain tumor therapeutics.
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Affiliation(s)
- Gayathree Thenuwara
- School of Food Science and Environmental Health, Technological University Dublin, Grangegorman Lower, D07 H6K8 Dublin, Ireland;
- Institute of Biochemistry, Molecular Biology, and Biotechnology, University of Colombo, Colombo 00300, Sri Lanka
| | - James Curtin
- Faculty of Engineering and Built Environment, Technological University Dublin, Bolton Street, D01 K822 Dublin, Ireland;
| | - Furong Tian
- School of Food Science and Environmental Health, Technological University Dublin, Grangegorman Lower, D07 H6K8 Dublin, Ireland;
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Macgregor-Fairlie M, De Gomes P, Weston D, Rickard JJS, Goldberg Oppenheimer P. Hybrid use of Raman spectroscopy and artificial neural networks to discriminate Mycobacterium bovis BCG and other Mycobacteriales. PLoS One 2023; 18:e0293093. [PMID: 38079400 PMCID: PMC10712843 DOI: 10.1371/journal.pone.0293093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 10/05/2023] [Indexed: 12/18/2023] Open
Abstract
Even in the face of the COVID-19 pandemic, Tuberculosis (TB) continues to be a major public health problem and the 2nd biggest infectious cause of death worldwide. There is, therefore, an urgent need to develop effective TB diagnostic methods, which are cheap, portable, sensitive and specific. Raman spectroscopy is a potential spectroscopic technique for this purpose, however, so far, research efforts have focused primarily on the characterisation of Mycobacterium tuberculosis and other Mycobacteria, neglecting bacteria within the microbiome and thus, failing to consider the bigger picture. It is paramount to characterise relevant Mycobacteriales and develop suitable analytical tools to discriminate them from each other. Herein, through the combined use of Raman spectroscopy and the self-optimising Kohonen index network and further multivariate tools, we have successfully undertaken the spectral analysis of Mycobacterium bovis BCG, Corynebacterium glutamicum and Rhodoccocus erythropolis. This has led to development of a useful tool set, which can readily discern spectral differences between these three closely related bacteria as well as generate a unique spectral barcode for each species. Further optimisation and refinement of the developed method will enable its application to other bacteria inhabiting the microbiome and ultimately lead to advanced diagnostic technologies, which can save many lives.
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Affiliation(s)
- Michael Macgregor-Fairlie
- School of Chemical Engineering, Advanced Nanomaterials Structures and Applications Laboratories, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Paulo De Gomes
- School of Chemical Engineering, Advanced Nanomaterials Structures and Applications Laboratories, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Daniel Weston
- School of Chemical Engineering, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
| | | | - Pola Goldberg Oppenheimer
- School of Chemical Engineering, Advanced Nanomaterials Structures and Applications Laboratories, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
- Healthcare Technologies Institute, Institute of Translational Medicine, University of Birmingham, Birmingham, United Kingdom
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Gao C, Zhao P, Fan Q, Jing H, Dang R, Sun W, Feng Y, Hu B, Wang Q. Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123086. [PMID: 37451210 DOI: 10.1016/j.saa.2023.123086] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/24/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023]
Abstract
Raman spectroscopy is a kind of vibrational method that can rapidly and non-invasively gives chemical structural information with the Raman spectrometer. Despite its technical advantages, in practical application scenarios, Raman spectroscopy often suffers from interference, such as noises and baseline drifts, resulting in the inability to acquire high-quality Raman spectroscopy signals, which brings challenges to subsequent spectral analysis. The commonly applied spectral preprocessing methods, such as Savitzky-Golay smooth and wavelet transform, can only perform corresponding single-item processing and require manual intervention to carry out a series of tedious trial parameters. Especially, each scheme can only be used for a specific data set. In recent years, the development of deep neural networks has provided new solutions for intelligent preprocessing of spectral data. In this paper, we first creatively started from the basic mechanism of spectral signal generation and constructed a mathematical model of the Raman spectral signal. By counting the noise parameters of the real system, we generated a simulation dataset close to the output of the real system, which alleviated the dependence on data during deep learning training. Due to the powerful nonlinear fitting ability of the neural network, fully connected network model is constructed to complete the baseline estimation task simply and quickly. Then building the Unet model can effectively achieve spectral denoising, and combining it with baseline estimation can realize intelligent joint processing. Through the simulation dataset experiment, it is proved that compared with the classic method, the method proposed in this paper has obvious advantages, which can effectively improve the signal quality and further ensure the accuracy of the peak intensity. At the same time, when the proposed method is applied to the actual system, it also achieves excellent performance compared with the common method, which indirectly indicates the effectiveness of the Raman signal simulation model. The research presented in this paper offers a variety of efficient pipelines for the intelligent processing of Raman spectroscopy, which can adapt to the requirements of different tasks while providing a new idea for enhancing the quality of Raman spectroscopy signals.
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Affiliation(s)
- Chi Gao
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peng Zhao
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Fan
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China
| | - Haonan Jing
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ruochen Dang
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weifeng Sun
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yutao Feng
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China
| | - Bingliang Hu
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China
| | - Quan Wang
- Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China.
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Wu Y, Wang Y, He C, Wang Y, Ma J, Lin Y, Zhou L, Xu S, Ye Y, Yin W, Ye J, Lu J. Precise diagnosis of breast phyllodes tumors using Raman spectroscopy: Biochemical fingerprint, tumor metabolism and possible mechanism. Anal Chim Acta 2023; 1283:341897. [PMID: 37977771 DOI: 10.1016/j.aca.2023.341897] [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: 05/22/2023] [Revised: 08/31/2023] [Accepted: 10/09/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Breast fibroadenomas and phyllodes tumors are both fibroepithelial tumors with comparable histological characteristics. However, rapid and precise differential diagnosis is a tough point in clinical pathology. Given the tendency of phyllodes tumors to recur, the difficulty in differential diagnosis with fibroadenomas leads to the difficulty in optimal management for these patients. METHOD In this study, we used Raman spectroscopy to differentiate phyllodes tumors from breast fibroadenomas based on the biochemical and metabolic composition and develop a classification model. The model was validated by 5-fold cross-validation in the training set and tested in an independent test set. The potential metabolic differences between the two types of tumors observed in Raman spectroscopy were confirmed by targeted metabolomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS). RESULTS A total of 204 patients with formalin-fixed paraffin-embedded (FFPE) tissue samples, including 100 fibroadenomas and 104 phyllodes tumors were recruited from April 2014 to August 2021. All patients were randomly divided into the training cohort (n = 153) and the test cohort (n = 51). The Raman classification model could differentiate phyllodes tumor versus fibroadenoma with cross-validation accuracy, sensitivity, precision, and area under curve (AUC) of 85.58 % ± 1.77 %, 83.82 % ± 1.01 %, 87.65 % ± 4.22 %, and 93.18 % ± 1.98 %, respectively. When tested in the independent test set, it performed well with the test accuracy, sensitivity, specificity, and AUC of 83.50 %, 86.54 %, 80.39 %, and 90.71 %. Furthermore, the AUC was significantly higher for the Raman model than that for ultrasound (P = 0.0017) and frozen section diagnosis (P < 0.0001). When it came to much more difficult diagnosis between fibroadenoma and benign or small-size phyllodes tumor for pathological examination, the Raman model was capable of differentiating with AUC up to 97.45 % and 95.61 %, respectively. On the other hand, targeted metabolomic analysis, based on fresh-frozen tissue samples, confirmed the differential metabolites (including thymine, dihydrothymine, trans-4-hydroxy-l-proline, etc.) identified from Raman spectra between phyllodes tumor and fibroadenoma. SIGNIFICANCE AND NOVELTY In this study, we obtained the molecular information map of breast phyllodes tumors provided by Raman spectroscopy for the first time. We identified a novel Raman fingerprint signature with the potential to precisely characterize and distinguish phyllodes tumors from fibroadenoma as a quick and accurate diagnostic tool. Raman spectroscopy is expected to further guide the precise diagnosis and optimal treatment of breast fibroepithelial tumors in the future.
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Affiliation(s)
- Yifan Wu
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Yaohui Wang
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.
| | - Chang He
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China
| | - Yan Wang
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Jiayi Ma
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Yanping Lin
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Liheng Zhou
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Shuguang Xu
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Yumei Ye
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Wenjin Yin
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.
| | - Jian Ye
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.
| | - Jingsong Lu
- Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.
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Liu H, Jiang H, Liu X, Wang X. Physicochemical understanding of biomineralization by molecular vibrational spectroscopy: From mechanism to nature. EXPLORATION (BEIJING, CHINA) 2023; 3:20230033. [PMID: 38264681 PMCID: PMC10742219 DOI: 10.1002/exp.20230033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 06/25/2023] [Indexed: 01/25/2024]
Abstract
The process and mechanism of biomineralization and relevant physicochemical properties of mineral crystals are remarkably sophisticated multidisciplinary fields that include biology, chemistry, physics, and materials science. The components of the organic matter, structural construction of minerals, and related mechanical interaction, etc., could help to reveal the unique nature of the special mineralization process. Herein, the paper provides an overview of the biomineralization process from the perspective of molecular vibrational spectroscopy, including the physicochemical properties of biomineralized tissues, from physiological to applied mineralization. These physicochemical characteristics closely to the hierarchical mineralization process include biological crystal defects, chemical bonding, atomic doping, structural changes, and content changes in organic matter, along with the interface between biocrystals and organic matter as well as the specific mechanical effects for hardness and toughness. Based on those observations, the special physiological properties of mineralization for enamel and bone, as well as the possible mechanism of pathological mineralization and calcification such as atherosclerosis, tumor micro mineralization, and urolithiasis are also reviewed and discussed. Indeed, the clearly defined physicochemical properties of mineral crystals could pave the way for studies on the mechanisms and applications.
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Affiliation(s)
- Hao Liu
- State Key Laboratory of Digital Medical EngineeringSchool of Biological Science and Medical EngineeringSoutheast UniversityNanjingJiangsuChina
| | - Hui Jiang
- State Key Laboratory of Digital Medical EngineeringSchool of Biological Science and Medical EngineeringSoutheast UniversityNanjingJiangsuChina
| | - Xiaohui Liu
- State Key Laboratory of Digital Medical EngineeringSchool of Biological Science and Medical EngineeringSoutheast UniversityNanjingJiangsuChina
| | - Xuemei Wang
- State Key Laboratory of Digital Medical EngineeringSchool of Biological Science and Medical EngineeringSoutheast UniversityNanjingJiangsuChina
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Kouri MA, Karnachoriti M, Spyratou E, Orfanoudakis S, Kalatzis D, Kontos AG, Seimenis I, Efstathopoulos EP, Tsaroucha A, Lambropoulou M. Shedding Light on Colorectal Cancer: An In Vivo Raman Spectroscopy Approach Combined with Deep Learning Analysis. Int J Mol Sci 2023; 24:16582. [PMID: 38068905 PMCID: PMC10706261 DOI: 10.3390/ijms242316582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/07/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research with high specificity, sensitivity, and spatial and temporal resolution. Recent advanced Raman systems, such as portable Raman systems and fiber-optic probes, provide the potential for accurate in vivo discrimination between healthy and cancerous tissues. In our study, a portable Raman probe spectrometer was tested in immunosuppressed mice for the in vivo localization of colorectal cancer malignancies from normal tissue margins. The acquired Raman spectra were preprocessed, and principal component analysis (PCA) was performed to facilitate discrimination between malignant and normal tissues and to highlight their biochemical differences using loading plots. A transfer learning model based on a one-dimensional convolutional neural network (1D-CNN) was employed for the Raman spectra data to assess the classification accuracy of Raman spectra in live animals. The 1D-CNN model yielded an 89.9% accuracy and 91.4% precision in tissue classification. Our results contribute to the field of Raman spectroscopy in cancer diagnosis, highlighting its promising role within clinical applications.
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Affiliation(s)
- Maria Anthi Kouri
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (M.A.K.); (E.S.); (D.K.); (E.P.E.)
- Medical Physics Program, Department of Physics and Applied Physics, Kennedy College of Sciences, University of Massachusetts Lowell, 265 Riverside St., Lowell, MA 01854, USA
| | - Maria Karnachoriti
- Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Iroon Politechniou 9, 15780 Athens, Greece; (M.K.); (S.O.)
| | - Ellas Spyratou
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (M.A.K.); (E.S.); (D.K.); (E.P.E.)
| | - Spyros Orfanoudakis
- Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Iroon Politechniou 9, 15780 Athens, Greece; (M.K.); (S.O.)
| | - Dimitris Kalatzis
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (M.A.K.); (E.S.); (D.K.); (E.P.E.)
| | - Athanassios G. Kontos
- Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Iroon Politechniou 9, 15780 Athens, Greece; (M.K.); (S.O.)
| | - Ioannis Seimenis
- Medical School, National and Kapodistrian University of Athens, 75 Mikras Assias Str., 11527 Athens, Greece;
| | - Efstathios P. Efstathopoulos
- 2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (M.A.K.); (E.S.); (D.K.); (E.P.E.)
| | - Alexandra Tsaroucha
- Laboratory of Bioethics, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
| | - Maria Lambropoulou
- Laboratory of Histology-Embryology, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
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Fu L, Lin CT, Karimi-Maleh H, Chen F, Zhao S. Plasmonic Nanoparticle-Enhanced Optical Techniques for Cancer Biomarker Sensing. BIOSENSORS 2023; 13:977. [PMID: 37998152 PMCID: PMC10669140 DOI: 10.3390/bios13110977] [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: 10/04/2023] [Revised: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 11/25/2023]
Abstract
This review summarizes recent advances in leveraging localized surface plasmon resonance (LSPR) nanotechnology for sensitive cancer biomarker detection. LSPR arising from noble metal nanoparticles under light excitation enables the enhancement of various optical techniques, including surface-enhanced Raman spectroscopy (SERS), dark-field microscopy (DFM), photothermal imaging, and photoacoustic imaging. Nanoparticle engineering strategies are discussed to optimize LSPR for maximum signal amplification. SERS utilizes electromagnetic enhancement from plasmonic nanostructures to boost inherently weak Raman signals, enabling single-molecule sensitivity for detecting proteins, nucleic acids, and exosomes. DFM visualizes LSPR nanoparticles based on scattered light color, allowing for the ultrasensitive detection of cancer cells, microRNAs, and proteins. Photothermal imaging employs LSPR nanoparticles as contrast agents that convert light to heat, producing thermal images that highlight cancerous tissues. Photoacoustic imaging detects ultrasonic waves generated by LSPR nanoparticle photothermal expansion for deep-tissue imaging. The multiplexing capabilities of LSPR techniques and integration with microfluidics and point-of-care devices are reviewed. Remaining challenges, such as toxicity, standardization, and clinical sample analysis, are examined. Overall, LSPR nanotechnology shows tremendous potential for advancing cancer screening, diagnosis, and treatment monitoring through the integration of nanoparticle engineering, optical techniques, and microscale device platforms.
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Affiliation(s)
- Li Fu
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, China; (F.C.); (S.Z.)
| | - Cheng-Te Lin
- Qianwan Institute, Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences, Ningbo 315201, China;
- Key Laboratory of Marine Materials and Related Technologies, Zhejiang Key Laboratory of Marine Materials and Protective Technologies, Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences, Ningbo 315201, China
- University of Chinese Academy of Sciences, 19 A Yuquan Rd., Shijingshan District, Beijing 100049, China
| | - Hassan Karimi-Maleh
- The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Wenzhou 325015, China;
- School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Engineering, Lebanese American University, Byblos 13-5053, Lebanon
| | - Fei Chen
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, China; (F.C.); (S.Z.)
| | - Shichao Zhao
- Key Laboratory of Novel Materials for Sensor of Zhejiang Province, College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, China; (F.C.); (S.Z.)
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Chaloupková Z, Žárská L, Belza J, Poláková K. Label-free detection and mapping of graphene oxide in single HeLa cells based on MCR-Raman spectroscopy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:5582-5588. [PMID: 37917034 DOI: 10.1039/d3ay01122d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
GO is a 2D nanomaterial that has attracted attention in many industries in recent years, such as the chemical industry, electronics or medicine. Due to its unique properties such as strength, hydrophilicity and large specific surface area with the possibility of functionalization, GO is a particularly attractive material in biomedicine as a candidate for use in targeted drug delivery. In such a case, we need information on whether graphene oxide penetrates into cells and whether we are able to detect and monitor GO in these cells during and also after the treatment to evaluate possible degradation process of GO and its interaction within the cell compartements. This work introduces the Raman spectroscopy as label-free detection method showing the advantages of combining Raman spectroscopy with MCR (Multivariate Curve Resolution) analysis for advanced detection of GO in cervical cancer (HeLa) cells. Our synthesized GO is characterized firstly by AFM, SEM and Raman spectroscopy and then MCR-Raman spectroscopy is used to detect internalized GO in individual HeLa cells. Moreover, by using our methodology, distribution of GO as well as its chemical stability inside the cell for up to six months is investigated without using any additional labeling or tracing the GO. Thus, MCR-Raman spectroscopy may become a new analytical tool in preclinical and clinical applications of graphene-based nanotheranostics.
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Affiliation(s)
- Zuzana Chaloupková
- CATRIN - Regional Center of Advanced Technologies and Materials, Palacky University Olomouc, Olomouc, Czechia.
| | - Ludmila Žárská
- CATRIN - Regional Center of Advanced Technologies and Materials, Palacky University Olomouc, Olomouc, Czechia.
| | - Jan Belza
- CATRIN - Regional Center of Advanced Technologies and Materials, Palacky University Olomouc, Olomouc, Czechia.
- Department of Physcial Chemistry, Faculty of Science, Palacky University Olomouc, Olomouc, Czechia
| | - Kateřina Poláková
- CATRIN - Regional Center of Advanced Technologies and Materials, Palacky University Olomouc, Olomouc, Czechia.
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Kopec M, Beton-Mysur K, Abramczyk H. Raman imaging and chemometric methods in human normal bronchial and cancer lung cells: Raman biomarkers of lipid reprogramming. Chem Phys Lipids 2023; 257:105339. [PMID: 37748746 DOI: 10.1016/j.chemphyslip.2023.105339] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/15/2023] [Accepted: 09/21/2023] [Indexed: 09/27/2023]
Abstract
This paper presents an approach to study biochemical changes in human normal bronchial cells (BEpiC) and human cancer lung cells (A549) by Raman spectroscopy and Raman imaging combined with chemometric methods. Based on Raman spectra and Raman imaging combined with chemometric methods we have proved that peaks at 845 cm-1, 2845 cm-1, 2936 cm-1, 1444 cm-1, 750 cm-1, 1126 cm-1, 1584 cm-1, can be treated as Raman biomarkers probing phosphorylation, lipid reprogramming, oxidative phosphorylation and changes in cholesterol and cytochrome in normal and cancer cells. Raman analysis of the bands at 845 cm-1, 2845 cm-1, 1444 cm-1, and 1126 cm-1 in human cancer lung cells and human normal bronchial cells demonstrate enhanced phosphorylation and triglycerides de novo synthesis, reduced levels of cholesterol and cytochrome c in cancer cells. The sensitivity is equal to 100% (nucleus), 87.5% (mitochondria), 100% (endoplasmic reticulum), 87.5% (lipid droplets), 87.5% (cytoplasm), 87.5% (cell membrane) for A549 cell line and 83.3% (nucleus), 100% (mitochondria), 83.3% (endoplasmic reticulum), 100% (lipid droplets), 100% (cytoplasm), 83.3% (cell membrane) for BEpiC. The values of specificity for cross-validation equal 93.4% (nucleus), 85.5% (mitochondria), 89.5% (endoplasmic reticulum), 90.8% (lipid droplets), 61.8% (cytoplasm), 94.7% (cell membrane) for A549 cell line and 88.5% (nucleus), 85.9% (mitochondria), 85.9% (endoplasmic reticulum), 97.4% (lipid droplets), 75.6% (cytoplasm), 92.3% (cell membrane) for BEpiC. We have confirmed that Raman spectroscopy methods combined with PLS-DA are useful tools to monitor changes in human cancer lung cells and human normal bronchial cells.
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Affiliation(s)
- Monika Kopec
- Lodz University of Technology, Institute of Applied Radiation Chemistry, Laboratory of Laser Molecular Spectroscopy, Wroblewskiego 15, Lodz 93-590, Poland.
| | - Karolina Beton-Mysur
- Lodz University of Technology, Institute of Applied Radiation Chemistry, Laboratory of Laser Molecular Spectroscopy, Wroblewskiego 15, Lodz 93-590, Poland
| | - Halina Abramczyk
- Lodz University of Technology, Institute of Applied Radiation Chemistry, Laboratory of Laser Molecular Spectroscopy, Wroblewskiego 15, Lodz 93-590, Poland
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40
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Kim JH, Zhang C, Sperati CJ, Barman I, Bagnasco SM. Hyperspectral Raman Imaging for Automated Recognition of Human Renal Amyloid. J Histochem Cytochem 2023; 71:643-652. [PMID: 37833851 PMCID: PMC10617441 DOI: 10.1369/00221554231206858] [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: 05/17/2023] [Accepted: 09/15/2023] [Indexed: 10/15/2023] Open
Abstract
In the clinical setting, routine identification of the main types of tissue amyloid deposits, light-chain amyloid (AL) and serum amyloid A (AA), is based on histochemical staining; rarer types of amyloid require mass spectrometry analysis. Raman spectroscopic imaging is an analytical tool, which can be used to chemically map, and thus characterize, the molecular composition of fluid and solid tissue. In this proof-of-concept study, we tested the feasibility of applying Raman spectroscopy combined with artificial intelligence to detect and characterize amyloid deposits in unstained frozen tissue sections from kidney biopsies with pathologic diagnosis of AL and AA amyloidosis and control biopsies with no amyloidosis (NA). Raman hyperspectral images, mapped in a 2D grid-like fashion over the tissue sections, were obtained. Three machine learning-assisted analysis models of the hyperspectral images could accurately distinguish AL (types λ and κ), AA, and NA 93-100% of the time. Although very preliminary, these findings illustrate the potential of Raman spectroscopy as a technique to identify, and possibly, subtype renal amyloidosis.
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Affiliation(s)
- Jeong Hee Kim
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chi Zhang
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - C. John Sperati
- Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Serena M. Bagnasco
- Clinical Renal Biopsy Service, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Khristoforova Y, Bratchenko L, Bratchenko I. Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review. Int J Mol Sci 2023; 24:15605. [PMID: 37958586 PMCID: PMC10647591 DOI: 10.3390/ijms242115605] [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: 10/04/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
Raman spectroscopy is a widely developing approach for noninvasive analysis that can provide information on chemical composition and molecular structure. High chemical specificity calls for developing different medical diagnostic applications based on Raman spectroscopy. This review focuses on the Raman-based techniques used in medical diagnostics and provides an overview of such techniques, possible areas of their application, and current limitations. We have reviewed recent studies proposing conventional Raman spectroscopy and surface-enhanced Raman spectroscopy for rapid measuring of specific biomarkers of such diseases as cardiovascular disease, cancer, neurogenerative disease, and coronavirus disease (COVID-19). As a result, we have discovered several most promising Raman-based applications to identify affected persons by detecting some significant spectral features. We have analyzed these approaches in terms of their potentially diagnostic power and highlighted the remaining challenges and limitations preventing their translation into clinical settings.
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Affiliation(s)
| | | | - Ivan Bratchenko
- Department of Laser and Biotechnical Systems, Samara National Research University, 34 Moskovskoye Shosse, Samara 443086, Russia; (Y.K.)
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de Rezende BS, Franca T, de Paula MAB, Cleveland HPK, Cena C, do Nascimento Ramos CA. Turning chaotic sample group clusterization into organized ones by feature selection: Application on photodiagnosis of Brucella abortus serological test. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY. B, BIOLOGY 2023; 247:112781. [PMID: 37657188 DOI: 10.1016/j.jphotobiol.2023.112781] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/14/2023] [Accepted: 08/24/2023] [Indexed: 09/03/2023]
Abstract
Bovine brucellosis diagnosis is a major problem to be solved; the disease has a tremendous economic impact with significant losses in meat and dairy products, besides the fact that it can be transmitted to humans. The sanitary measures instituted in Brazil are based on disease control through diagnosis, animal sacrifice, and vaccination. Although the currently available diagnostic tests show suitable quality parameters, they are time-consuming, and the incidence of false-positive and/or false-negative results is still observed, hindering effective disease control. The development of a low-cost, fast, and accurate brucellosis diagnosis test remains a need for proper sanitary measures at a large-scale analysis. In this context, spectroscopy techniques associated with machine learning tools have shown great potential for use in diagnostic tests. In this study, bovine blood serum was investigated by UV-vis spectroscopy and machine learning algorithms to build a prediction model for Brucella abortus diagnosis. Here we first pre-treated the UV raw data by using Standard Normal Deviate method to remove baseline deviation, then apply principal component analysis - a clustering method - to observe the group formation tendency; the first results showed no clustering tendency with a messy sample score distribution, then we properly select the main principal components to improve clusterization. Finally, by using machine learning algorithms (SVM and KNN), the predicting models achieved a 92.5% overall accuracy. The present methodology provides a test result in an average time of 5 min, while the standard diagnosis, with the screening and confirmatory tests, can take up to 48 h. The present result demonstrates the method's viability for diagnosing bovine brucellosis, which can significantly contribute to disease control programs in Brazil and other countries.
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Affiliation(s)
- Bruno Silva de Rezende
- UFMS - Universidade Federal de Mato Grosso do Sul, Faculdade de Medicina Veterinária e Zootecnia (FAMEZ), Campo Grande, MS, Brazil
| | - Thiago Franca
- UFMS - Universidade Federal de Mato Grosso do Sul, Optics and Photonic Lab (SISFOTON-UFMS), Campo Grande, MS, Brazil.
| | - Maykko Antônyo Bravo de Paula
- UFMS - Universidade Federal de Mato Grosso do Sul, Faculdade de Medicina Veterinária e Zootecnia (FAMEZ), Campo Grande, MS, Brazil.
| | | | - Cícero Cena
- UFMS - Universidade Federal de Mato Grosso do Sul, Optics and Photonic Lab (SISFOTON-UFMS), Campo Grande, MS, Brazil.
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Chu Q, Sun Y, Sun C, Shuo Y, Jirigalantu, Li X, Li F, Bayanheshig. Broadband, high-resolution spatial heterodyne Raman spectroscopy measurement based on a multi-Littrow-angle multi-grating. OPTICS EXPRESS 2023; 31:31284-31299. [PMID: 37710651 DOI: 10.1364/oe.500421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 08/25/2023] [Indexed: 09/16/2023]
Abstract
This paper proposes a spatial heterodyne Raman spectrometer (SHRS) based on a multi-Littrow-angle multi-grating (MLAMG). Compared with a conventional multi-grating, the MLAMG not only provides higher spectral resolution and a broader spectral range, but is also easier to produce. A verification breadboard system is built using the MLAMG combined with four sub-gratings with a groove density of 300 gr/mm and Littrow angles of 4.6355°, 4.8536°, 5.0820°, and 5.3253°. This MLAMG-SHRS is used to obtain the Raman spectra of inorganic solids and organic solutions for different integration times, laser powers, suspension contents, and containers. The Raman spectra of mixed targets and minerals are also presented. The experiments demonstrate that the MLAMG-SHRS is suitable for broadband measurements at high spectral resolution in a wide range of potential applications.
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44
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Wei Y, Chen H, Yu B, Jia C, Cong X, Cong L. Multi-scale sequential feature selection for disease classification using Raman spectroscopy data. Comput Biol Med 2023; 162:107053. [PMID: 37267829 DOI: 10.1016/j.compbiomed.2023.107053] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 04/20/2023] [Accepted: 05/20/2023] [Indexed: 06/04/2023]
Abstract
Raman spectroscopy (RS) optical technology promises non-destructive and fast application in medical disease diagnosis in a single step. However, achieving clinically relevant performance levels remains challenging due to the inability to search for significant Raman signals at different scales. Here we propose a multi-scale sequential feature selection method that can capture global sequential features and local peak features for disease classification using RS data. Specifically, we utilize the Long short-term memory network (LSTM) module to extract global sequential features in the Raman spectra, as it can capture long-term dependencies present in the Raman spectral sequences. Meanwhile, the attention mechanism is employed to select local peak features that were ignored before and are the key to distinguishing different diseases. Experimental results on three public and in-house datasets demonstrate the superiority of our model compared with state-of-the-art methods for RS classification. In particular, our model achieves an accuracy of 97.9 ± 0.2% on the COVID-19 dataset, 76.3 ± 0.4% on the H-IV dataset, and 96.8 ± 1.9% on the H-V dataset.
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Affiliation(s)
- Yue Wei
- School of Artificial Intelligence, Jilin University, Changchun, 130015, China; Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, China
| | - Hechang Chen
- School of Artificial Intelligence, Jilin University, Changchun, 130015, China; Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, China.
| | - Bo Yu
- School of Artificial Intelligence, Jilin University, Changchun, 130015, China; Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, China; Department of Radiology, Leiden University Medical Center, Leiden, 2333ZA, Netherlands.
| | - Chengyou Jia
- Tongji University School of Medicine, Tongji University, Shanghai, 200092, China; Shanghai Research Center for Thyroid Diseases, Shanghai Tenth People's Hospital, Shanghai, 200072, China
| | - Xianling Cong
- Tissue Bank, China-Japan Union Hospital of Jilin University, Changchun, 130033, China.
| | - Lele Cong
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, 130033, China
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45
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Duckworth E, Mortimer M, Al‐Sarireh B, Kanamarlapudi V, Roy D. Frontline clinical diagnosis-FTIR on pancreatic cancer. Cancer Med 2023; 12:17340-17345. [PMID: 37466344 PMCID: PMC10501286 DOI: 10.1002/cam4.6346] [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: 12/05/2022] [Revised: 06/23/2023] [Accepted: 07/05/2023] [Indexed: 07/20/2023] Open
Abstract
OBJECTIVE Accurate, easily accessible and economically viable cancer diagnostic tools are pivotal in improving the abysmal 5% survival rate of pancreatic cancer. METHODS A novel, affordable, non-invasive diagnostic method has been developed by combining measurement precision of infrared spectroscopy with classification using machine learning tools. RESULTS Diagnosis accuracy as high as 90% has been achieved. The study investigated urine and blood from pancreas cancer patients and healthy volunteers, and significantly improved accuracy by focusing on sweet-spots within blood plasma fractions containing molecules within a narrow range of molecular weights.
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46
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Tardito S, MacKay C. Rethinking our approach to cancer metabolism to deliver patient benefit. Br J Cancer 2023; 129:406-415. [PMID: 37340094 PMCID: PMC10403540 DOI: 10.1038/s41416-023-02324-9] [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: 02/28/2023] [Revised: 05/25/2023] [Accepted: 06/12/2023] [Indexed: 06/22/2023] Open
Abstract
Altered cellular metabolism is a major mechanism by which tumours support nutrient consumption associated with increased cellular proliferation. Selective dependency on specific metabolic pathways provides a therapeutic vulnerability that can be targeted in cancer therapy. Anti-metabolites have been used clinically since the 1940s and several agents targeting nucleotide metabolism are now well established as standard of care treatment in a range of indications. However, despite great progress in our understanding of the metabolic requirements of cancer and non-cancer cells within the tumour microenvironment, there has been limited clinical success for novel agents targeting pathways outside of nucleotide metabolism. We believe that there is significant therapeutic potential in targeting metabolic processes within cancer that is yet to be fully realised. However, current approaches to identify novel targets, test novel therapies and select patient populations most likely to benefit are sub-optimal. We highlight recent advances in technologies and understanding that will support the identification and validation of novel targets, re-evaluation of existing targets and design of optimal clinical positioning strategies to deliver patient benefit.
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Affiliation(s)
- Saverio Tardito
- The Cancer Research UK Beatson Institute, Glasgow, UK
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Craig MacKay
- Cancer Research Horizons, The Cancer Research UK Beatson Institute, Glasgow, UK.
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47
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Sharaha U, Hania D, Lapidot I, Salman A, Huleihel M. Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning. Cells 2023; 12:1909. [PMID: 37508572 PMCID: PMC10378363 DOI: 10.3390/cells12141909] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/06/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society. Currently used methods for the diagnosis of cancer are time-consuming, producing discomfort and anxiety for patients and significant medical waste. The main goal of this study is to evaluate the potential of Raman spectroscopy-based machine learning for the identification and characterization of precancerous and cancerous cells. As a representative model, normal mouse primary fibroblast cells (NFC) as healthy cells; a mouse fibroblast cell line (NIH/3T3), as precancerous cells; and fully malignant mouse fibroblasts (MBM-T) as cancerous cells were used. Raman spectra were measured from three different sites of each of the 457 investigated cells and analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA). Our results showed that it was possible to distinguish between the normal and abnormal (precancerous and cancerous) cells with a success rate of 93.1%; this value was 93.7% when distinguishing between normal and precancerous cells and 80.2% between precancerous and cancerous cells. Moreover, there was no influence of the measurement site on the differentiation between the different examined biological systems.
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Affiliation(s)
- Uraib Sharaha
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
- Department of Biology, Science and Technology College, Hebron University, Hebron P760, Palestine
| | - Daniel Hania
- Department of Green Engineering, SCE-Shamoon College of Engineering, Beer-Sheva 84100, Israel
| | - Itshak Lapidot
- Department of Electrical and Electronics Engineering, ACLP-Afeka Center for Language Processing, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv 69107, Israel
- Laboratoire Informatique d'Avignon (LIA), Avignon Université, 339 Chemin des Meinajaries, 84000 Avignon, France
| | - Ahmad Salman
- Department of Physics, SCE-Shamoon College of Engineering, Beer-Sheva 84100, Israel
| | - Mahmoud Huleihel
- Department of Microbiology, Immunology, and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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48
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Li H, Zhou Y, Wu Y, Jiang Y, Bao H, Peng A, Shao Y. Real-time and accurate calibration detection of gout stones based on terahertz and Raman spectroscopy. Front Bioeng Biotechnol 2023; 11:1218927. [PMID: 37520298 PMCID: PMC10374424 DOI: 10.3389/fbioe.2023.1218927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 07/03/2023] [Indexed: 08/01/2023] Open
Abstract
Gout is a metabolic disease that can result in the formation of gout stones. It is essential to promptly identify and confirm the type of gout stone to alleviate pain and inflammation in patients and prevent complications associated with gout stones. Traditional detection methods, such as X-ray, ultrasound, CT scanning, and blood uric acid measurement, have limitations in early diagnosis. Therefore, this article aims to explore the use of micro Raman spectroscopy, Fourier transform infrared spectroscopy, and Terahertz time-domain spectroscopy systems to detect gout stone samples. Through comparative analysis, Terahertz technology and Raman spectroscopy have been found to provide chemical composition and molecular structure information of different wavebands of samples. By combining these two technologies, faster and more comprehensive analysis and characterization of samples can be achieved. In the future, handheld portable integrated testing instruments will be developed to improve the efficiency and accuracy of testing. Furthermore, this article proposes establishing a spectral database of gout stones and urinary stones by combining Raman spectroscopy and Terahertz spectroscopy. This database would provide accurate and comprehensive technical support for the rapid diagnosis of gout in clinical practice.
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Affiliation(s)
- Han Li
- The First Rehabilitation Hospital of Shanghai, School of Medicine, Tongji University, Shanghai, China
- Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
| | - Yuxin Zhou
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, China
| | - Yi Wu
- Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yanfang Jiang
- Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Hui Bao
- Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Ai Peng
- Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yongni Shao
- Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
- Terahertz Technology Innovation Research Institute, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, China
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49
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Fatima GN, Fatma H, Saraf SK. Vaccines in Breast Cancer: Challenges and Breakthroughs. Diagnostics (Basel) 2023; 13:2175. [PMID: 37443570 DOI: 10.3390/diagnostics13132175] [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: 04/17/2023] [Revised: 06/09/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
Breast cancer is a problem for women's health globally. Early detection techniques come in a variety of forms ranging from local to systemic and from non-invasive to invasive. The treatment of cancer has always been challenging despite the availability of a wide range of therapeutics. This is either due to the variable behaviour and heterogeneity of the proliferating cells and/or the individual's response towards the treatment applied. However, advancements in cancer biology and scientific technology have changed the course of the cancer treatment approach. This current review briefly encompasses the diagnostics, the latest and most recent breakthrough strategies and challenges, and the limitations in fighting breast cancer, emphasising the development of breast cancer vaccines. It also includes the filed/granted patents referring to the same aspects.
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Affiliation(s)
- Gul Naz Fatima
- Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Babu Banarasi Das Northern India Institute of Technology, Lucknow 226028, Uttar Pradesh, India
| | - Hera Fatma
- Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Babu Banarasi Das Northern India Institute of Technology, Lucknow 226028, Uttar Pradesh, India
| | - Shailendra K Saraf
- Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Babu Banarasi Das Northern India Institute of Technology, Lucknow 226028, Uttar Pradesh, India
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Mohd Nor Ihsan NS, Abdul Sani SF, Looi LM, Cheah PL, Chiew SF, Pathmanathan D, Bradley DA. A review: Exploring the metabolic and structural characterisation of beta pleated amyloid fibril in human tissue using Raman spectrometry and SAXS. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023:S0079-6107(23)00059-7. [PMID: 37307955 DOI: 10.1016/j.pbiomolbio.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 05/12/2023] [Accepted: 06/09/2023] [Indexed: 06/14/2023]
Abstract
Amyloidosis is a deleterious condition caused by abnormal amyloid fibril build-up in living tissues. To date, 42 proteins that are linked to amyloid fibrils have been discovered. Amyloid fibril structure variation can affect the severity, progression rate, or clinical symptoms of amyloidosis. Since amyloid fibril build-up is the primary pathological basis for various neurodegenerative illnesses, characterization of these deadly proteins, particularly utilising optical techniques have been a focus. Spectroscopy techniques provide significant non-invasive platforms for the investigation of the structure and conformation of amyloid fibrils, offering a wide spectrum of analyses ranging from nanometric to micrometric size scales. Even though this area of study has been intensively explored, there still remain aspects of amyloid fibrillization that are not fully known, a matter hindering progress in treating and curing amyloidosis. This review aims to provide recent updates and comprehensive information on optical techniques for metabolic and proteomic characterization of β-pleated amyloid fibrils found in human tissue with thorough literature analysis of publications. Raman spectroscopy and SAXS are well established experimental methods for study of structural properties of biomaterials. With suitable models, they offer extended information for valid proteomic analysis under physiologically relevant conditions. This review points to evidence that despite limitations, these techniques are able to provide for the necessary output and proteomics indication in order to extrapolate the aetiology of amyloid fibrils for reliable diagnostic purposes. Our metabolic database may also contribute to elucidating the nature and function of the amyloid proteome in development and clearance of amyloid diseases.
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Affiliation(s)
- N S Mohd Nor Ihsan
- Department of Physics, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - S F Abdul Sani
- Department of Physics, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.
| | - L M Looi
- Department of Pathology, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - P L Cheah
- Department of Pathology, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - S F Chiew
- Department of Pathology, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Dharini Pathmanathan
- Institute of Mathematical Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - D A Bradley
- Centre for Applied Physics and Radiation Technologies, Sunway University, 46150 PJ, Malaysia; Department of Physics, School of Mathematics & Physics, University of Surrey, Guildford, GU2 7XH, UK
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