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Sun J, Cheng W, Guo S, Cai R, Liu G, Wu A, Yin J. A ratiometric SERS strategy for the prediction of cancer cell proportion and guidance of glioma surgical resection. Biosens Bioelectron 2024; 261:116475. [PMID: 38852324 DOI: 10.1016/j.bios.2024.116475] [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: 03/29/2024] [Revised: 06/01/2024] [Accepted: 06/05/2024] [Indexed: 06/11/2024]
Abstract
Rapid and accurate identification of tumor boundaries is critical for the cure of glioma, but it is difficult due to the invasive nature of glioma cells. This paper aimed to explore a rapid diagnostic strategy based on a label-free surface-enhanced Raman scattering (SERS) technique for the quantitative detection of glioma cell proportion intraoperatively. With silver nanoparticles as substrate, an in-depth SERS analysis was performed on simulated clinical samples containing normal brain tissue and different concentrations of patient-derived glioma cells. The results revealed two universal characteristic peaks of 655 and 717 cm-1, which strongly correlated with glioma cell proportion regardless of individual differences. Based on the intensity ratio of the two peaks, a ratiometric SERS strategy for the quantification of glioma cells was established by employing an artificial neuron network model and a polynomial regression model. Such a strategy accurately estimated the proportion of glioma cells in simulated clinical samples (R2 = 0.98) and frozen samples (R2 = 0.85). More importantly, it accurately facilitated the delineation of tumor margins in freshly obtained samples. Taken together, this SERS-based method ensured a rapid and more detailed identification of tumor margins during surgical resection, which could be beneficial for intraoperative decision-making and pathological evaluation.
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Affiliation(s)
- Jiaojiao Sun
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, PR China
| | - Wen Cheng
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, PR China
| | - Songyi Guo
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, PR China
| | - Ruikai Cai
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, PR China
| | - Guangxing Liu
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, PR China.
| | - Anhua Wu
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, PR China.
| | - Jian Yin
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, PR China.
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2
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Yang J, Xu P, Wu S, Chen Z, Fang S, Xiao H, Hu F, Jiang L, Wang L, Mo B, Ding F, Lin LL, Ye J. Raman spectroscopy for esophageal tumor diagnosis and delineation using machine learning and the portable Raman spectrometer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 317:124461. [PMID: 38759393 DOI: 10.1016/j.saa.2024.124461] [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: 01/05/2024] [Revised: 05/02/2024] [Accepted: 05/11/2024] [Indexed: 05/19/2024]
Abstract
Esophageal cancer is one of the leading causes of cancer-related deaths worldwide. The identification of residual tumor tissues in the surgical margin of esophageal cancer is essential for the treatment and prognosis of cancer patients. But the current diagnostic methods, either pathological frozen section or paraffin section examination, are laborious, time-consuming, and inconvenient. Raman spectroscopy is a label-free and non-invasive analytical technique that provides molecular information with high specificity. Here, we report the use of a portable Raman system and machine learning algorithms to achieve accurate diagnosis of esophageal tumor tissue in surgically resected specimens. We tested five machine learning-based classification methods, including k-Nearest Neighbors, Adaptive Boosting, Random Forest, Principal Component Analysis-Linear Discriminant Analysis, and Support Vector Machine (SVM). Among them, SVM shows the highest accuracy (88.61 %) in classifying the esophageal tumor and normal tissues. The portable Raman system demonstrates robust measurements with an acceptable focal plane shift of up to 3 mm, which enables large-area Raman mapping on resected tissues. Based on this, we finally achieve successful Raman visualization of tumor boundaries on surgical margin specimens, and the Raman measurement time is less than 5 min. This work provides a robust, convenient, accurate, and cost-effective tool for the diagnosis of esophageal cancer tumors, advancing toward Raman-based clinical intraoperative applications.
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Affiliation(s)
- Junqing Yang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Pei Xu
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China
| | - Siyi Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Zhou Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Shiyan Fang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Haibo Xiao
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China
| | - Fengqing Hu
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China
| | - Lianyong Jiang
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China
| | - Lei Wang
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China
| | - Bin Mo
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China
| | - Fangbao Ding
- Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No 1665 Kongjiang Road, Yangpu District, Shanghai 200092, China.
| | - Linley Li Lin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Jian Ye
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
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3
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Zhu R, He H, Chen Y, Yi M, Ran S, Wang C, Wang Y. Deep learning for rapid virtual H&E staining of label-free glioma tissue from hyperspectral images. Comput Biol Med 2024; 180:108958. [PMID: 39094325 DOI: 10.1016/j.compbiomed.2024.108958] [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: 03/22/2024] [Revised: 07/02/2024] [Accepted: 07/26/2024] [Indexed: 08/04/2024]
Abstract
Hematoxylin and eosin (H&E) staining is a crucial technique for diagnosing glioma, allowing direct observation of tissue structures. However, the H&E staining workflow necessitates intricate processing, specialized laboratory infrastructures, and specialist pathologists, rendering it expensive, labor-intensive, and time-consuming. In view of these considerations, we combine the deep learning method and hyperspectral imaging technique, aiming at accurately and rapidly converting the hyperspectral images into virtual H&E staining images. The method overcomes the limitations of H&E staining by capturing tissue information at different wavelengths, providing comprehensive and detailed tissue composition information as the realistic H&E staining. In comparison with various generator structures, the Unet exhibits substantial overall advantages, as evidenced by a mean structure similarity index measure (SSIM) of 0.7731 and a peak signal-to-noise ratio (PSNR) of 23.3120, as well as the shortest training and inference time. A comprehensive software system for virtual H&E staining, which integrates CCD control, microscope control, and virtual H&E staining technology, is developed to facilitate fast intraoperative imaging, promote disease diagnosis, and accelerate the development of medical automation. The platform reconstructs large-scale virtual H&E staining images of gliomas at a high speed of 3.81 mm2/s. This innovative approach will pave the way for a novel, expedited route in histological staining.
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Affiliation(s)
- Ruohua Zhu
- National Engineering Research Center of Ophthalmology and Optometry, School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University, Xueyuan Road 270, Wenzhou, 325027, China
| | - Haiyang He
- National Engineering Research Center of Ophthalmology and Optometry, School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University, Xueyuan Road 270, Wenzhou, 325027, China
| | - Yuzhe Chen
- National Engineering Research Center of Ophthalmology and Optometry, School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University, Xueyuan Road 270, Wenzhou, 325027, China
| | - Ming Yi
- National Engineering Research Center of Ophthalmology and Optometry, School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University, Xueyuan Road 270, Wenzhou, 325027, China
| | - Shengdong Ran
- National Engineering Research Center of Ophthalmology and Optometry, School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University, Xueyuan Road 270, Wenzhou, 325027, China
| | - Chengde Wang
- Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
| | - Yi Wang
- National Engineering Research Center of Ophthalmology and Optometry, School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University, Xueyuan Road 270, Wenzhou, 325027, China; Wenzhou Institute, University of Chinese Academy of Sciences, Jinlian Road 1, Wenzhou, 325001, China.
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4
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Zhang Y, Chang K, Ogunlade B, Herndon L, Tadesse LF, Kirane AR, Dionne JA. From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis. ACS NANO 2024; 18:18101-18117. [PMID: 38950145 DOI: 10.1021/acsnano.4c04282] [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: 07/03/2024]
Abstract
Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.
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Affiliation(s)
- Yirui Zhang
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
| | - Kai Chang
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, United States
| | - Babatunde Ogunlade
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
| | - Liam Herndon
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Loza F Tadesse
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts 02139, United States
- Jameel Clinic for AI & Healthcare, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Amanda R Kirane
- Department of Surgery, Stanford University, Stanford, California 94305, United States
| | - Jennifer A Dionne
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, California 94305, United States
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5
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Rimskaya E, Gorevoy A, Shelygina S, Perevedentseva E, Timurzieva A, Saraeva I, Melnik N, Kudryashov S, Kuchmizhak A. Multi-Wavelength Raman Differentiation of Malignant Skin Neoplasms. Int J Mol Sci 2024; 25:7422. [PMID: 39000528 PMCID: PMC11242141 DOI: 10.3390/ijms25137422] [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: 05/23/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/16/2024] Open
Abstract
Raman microspectroscopy has become an effective method for analyzing the molecular appearance of biomarkers in skin tissue. For the first time, we acquired in vitro Raman spectra of healthy and malignant skin tissues, including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), at 532 and 785 nm laser excitation wavelengths in the wavenumber ranges of 900-1800 cm-1 and 2800-3100 cm-1 and analyzed them to find spectral features for differentiation between the three classes of the samples. The intensity ratios of the bands at 1268, 1336, and 1445 cm-1 appeared to be the most reliable criteria for the three-class differentiation at 532 nm excitation, whereas the bands from the higher wavenumber region (2850, 2880, and 2930 cm-1) were a robust measure of the increased protein/lipid ratio in the tumors at both excitation wavelengths. Selecting ratios of the three bands from the merged (532 + 785) dataset made it possible to increase the accuracy to 87% for the three classes and reach the specificities for BCC + SCC equal to 87% and 81% for the sensitivities of 95% and 99%, respectively. Development of multi-wavelength excitation Raman spectroscopic techniques provides a versatile non-invasive tool for research of the processes in malignant skin tumors, as well as other forms of cancer.
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Affiliation(s)
- Elena Rimskaya
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Alexey Gorevoy
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Svetlana Shelygina
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Elena Perevedentseva
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Alina Timurzieva
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
- Semashko National Research Institute of Public Health, 105064 Moscow, Russia
| | - Irina Saraeva
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Nikolay Melnik
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Sergey Kudryashov
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Aleksandr Kuchmizhak
- Institute of Automation and Control Processes, Far Eastern Branch, Russian Academy of Science, 690041 Vladivostok, Russia
- Far Eastern Federal University, 690922 Vladivostok, Russia
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6
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Mo W, Ke Q, Yang Q, Zhou M, Xie G, Qi D, Peng L, Wang X, Wang F, Ni S, Wang A, Huang J, Wen J, Yang Y, Du K, Wang X, Du X, Zhao Z. A Dual-Modal, Label-Free Raman Imaging Method for Rapid Virtual Staining of Large-Area Breast Cancer Tissue Sections. Anal Chem 2024. [PMID: 38967251 DOI: 10.1021/acs.analchem.4c00870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/06/2024]
Abstract
As one of the most common cancers, accurate, rapid, and simple histopathological diagnosis is very important for breast cancer. Raman imaging is a powerful technique for label-free analysis of tissue composition and histopathology, but it suffers from slow speed when applied to large-area tissue sections. In this study, we propose a dual-modal Raman imaging method that combines Raman mapping data with microscopy bright-field images to achieve virtual staining of breast cancer tissue sections. We validate our method on various breast tissue sections with different morphologies and biomarker expressions and compare it with the golden standard of histopathological methods. The results demonstrate that our method can effectively distinguish various types and components of tissues, and provide staining images comparable to stained tissue sections. Moreover, our method can improve imaging speed by up to 65 times compared to general spontaneous Raman imaging methods. It is simple, fast, and suitable for clinical applications.
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Affiliation(s)
- Wenbo Mo
- National Key Laboratory of Plasma Physics, Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Qi Ke
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Qiang Yang
- China Academy of Engineering Physics, 621900 Mianyang, China
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Minjie Zhou
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Gang Xie
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Daojian Qi
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Lijun Peng
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Xinming Wang
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Fei Wang
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Shuang Ni
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Anqun Wang
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Jinglin Huang
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Jiaxing Wen
- National Key Laboratory of Plasma Physics, Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Yue Yang
- National Key Laboratory of Plasma Physics, Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Kai Du
- Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
| | - Xuewu Wang
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Xiaobo Du
- Mianyang Central Hospital, 621000 Mianyang, China
| | - Zongqing Zhao
- National Key Laboratory of Plasma Physics, Laser Fusion Research Center, China Academy of Engineering Physics, 621900 Mianyang, China
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7
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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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8
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Yu Q, Shen X, Yi L, Liang M, Li G, Guan Z, Wu X, Castel H, Hu B, Yin P, Zhang W. Fragment-Fusion Transformer: Deep Learning-Based Discretization Method for Continuous Single-Cell Raman Spectral Analysis. ACS Sens 2024. [PMID: 38934798 DOI: 10.1021/acssensors.4c00149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
Raman spectroscopy has become an important single-cell analysis tool for monitoring biochemical changes at the cellular level. However, Raman spectral data, typically presented as continuous data with high-dimensional characteristics, is distinct from discrete sequences, which limits the application of deep learning-based algorithms in data analysis due to the lack of discretization. Herein, a model called fragment-fusion transformer is proposed, which integrates the discrete fragmentation of continuous spectra based on their intrinsic characteristics with the extraction of intrafragment features and the fusion of interfragment features. The model integrates the intrinsic feature-based fragmentation of spectra with transformer, constructing the fragment transformer block for feature extraction within fragments. Interfragment information is combined through the pyramid design structure to improve the model's receptive field and fully exploit the spectral properties. During the pyramidal fusion process, the information gain of the final extracted features in the spectrum has been enhanced by a factor of 9.24 compared to the feature extraction stage within the fragment, and the information entropy has been enhanced by a factor of 13. The fragment-fusion transformer achieved a spectral recognition accuracy of 94.5%, which is 4% higher compared to the method without fragmentation and fusion processes on the test set of cell Raman spectroscopy identification experiments. In comparison to common spectral classification models such as KNN, SVM, logistic regression, and CNN, fragment-fusion transformer has achieved 4.4% higher accuracy than the best-performing CNN model. Fragment-fusion transformer method has the potential to serve as a general framework for discretization in the field of continuous spectral data analysis and as a research tool for analyzing the intrinsic information within spectra.
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Affiliation(s)
- Qiang Yu
- Hangzhou Institute of Technology, Xidian University, Hangzhou, Zhejiang 311200, China
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China
| | - Xiaokun Shen
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China
| | - LangLang Yi
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China
| | - Minghui Liang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China
| | - Guoqian Li
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China
| | - Zhihui Guan
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China
| | - Xiaoyao Wu
- School of Mathematics and Physics Science and Engineering, Hebei University of Engineering, Handan, Hebei 056038, China
| | - Helene Castel
- Institute of Research and Biomedical Innovation, University of Rouen Normandie, Mont-Saint-Aignan, 76821, France
| | - Bo Hu
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China
- School of Mathematics and Physics Science and Engineering, Hebei University of Engineering, Handan, Hebei 056038, China
- Xi'an Intelligent Precision Diagnosis and Treatment International Science and Technology Cooperation Base, Xi'an, Shaanxi 710126, China
| | - Pengju Yin
- School of Mathematics and Physics Science and Engineering, Hebei University of Engineering, Handan, Hebei 056038, China
| | - Wenbo Zhang
- School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710126, China
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9
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Ranasinghe JC, Wang Z, Huang S. Unveiling brain disorders using liquid biopsy and Raman spectroscopy. NANOSCALE 2024; 16:11879-11913. [PMID: 38845582 PMCID: PMC11290551 DOI: 10.1039/d4nr01413h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.
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Affiliation(s)
- Jeewan C Ranasinghe
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
| | - Ziyang Wang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
| | - Shengxi Huang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
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10
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Zhang M, You Y, Zhang H, Zhang J, Yang F, Wang X, Lin C, Wang B, Chen L, Wang Z, Dai Z. Rapid Glutathione Analysis with SERS Microneedles for Deep Glioblastoma Tissue Differentiation. Anal Chem 2024; 96:10200-10209. [PMID: 38867357 DOI: 10.1021/acs.analchem.4c00483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
Rapid tissue differentiation at the molecular level is a prerequisite for precise surgical resection, which is of special value for the treatment of malignant tumors, such as glioblastoma (GBM). Herein, a SERS-active microneedle is prepared by modifying glutathione (GSH)-responsive molecules, 5,5'-dithiobis(2-nitrobenzoic acid) (DTNB), on the surface of Au@Ag substrates for the distinction of different GBM tissues. Since the Raman signals on the surface of the DTNB@Au@Ag microneedle can be collected by both portable and benchtop Raman spectrometers, the distribution of GSH in different tissues at centimeter scale can be displayed through Raman spectroscopy and Raman imaging, and the entire analysis process can be accomplished within 12 min. Accordingly, in vivo brain tissues of orthotopic GBM xenograft mice and ex vivo tissues of GBM patients are accurately differentiated with the microneedle, and the results are well consistent with tissue staining and postoperative pathological reports. In addition, the outline of tumor, peritumoral, and normal tissues can be indicated by the DTNB@Au@Ag microneedle for at least 56 days. Considering that the tumor tissues are quickly discriminated at the molecular level without the restriction of depth, the DTNB@Au@Ag microneedle is promising to be a powerful intraoperative diagnostic tool for surgery navigation.
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Affiliation(s)
- Min Zhang
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
| | - Yongping You
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P. R. China
| | - Hang Zhang
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
| | - Junxia Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P. R. China
| | - Furong Yang
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
| | - Xiefeng Wang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P. R. China
| | - Chao Lin
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P. R. China
| | - Binbin Wang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P. R. China
| | - Li Chen
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
| | - Zhaoyin Wang
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
| | - Zhihui Dai
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
- School of Chemistry and Molecular Engineering, Nanjing Tech University, Nanjing 211816, P. R. China
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11
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Kamp M, Surmacki J, Segarra Mondejar M, Young T, Chrabaszcz K, Joud F, Zecchini V, Speed A, Frezza C, Bohndiek SE. Raman micro-spectroscopy reveals the spatial distribution of fumarate in cells and tissues. Nat Commun 2024; 15:5386. [PMID: 38918386 PMCID: PMC11199670 DOI: 10.1038/s41467-024-49403-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 06/04/2024] [Indexed: 06/27/2024] Open
Abstract
Aberrantly accumulated metabolites elicit intra- and inter-cellular pro-oncogenic cascades, yet current measurement methods require sample perturbation/disruption and lack spatio-temporal resolution, limiting our ability to fully characterize their function and distribution. Here, we show that Raman spectroscopy (RS) can directly detect fumarate in living cells in vivo and animal tissues ex vivo, and that RS can distinguish between Fumarate hydratase (Fh1)-deficient and Fh1-proficient cells based on fumarate concentration. Moreover, RS reveals the spatial compartmentalization of fumarate within cellular organelles in Fh1-deficient cells: consistent with disruptive methods, we observe the highest fumarate concentration (37 ± 19 mM) in mitochondria, where the TCA cycle operates, followed by the cytoplasm (24 ± 13 mM) and then the nucleus (9 ± 6 mM). Finally, we apply RS to tissues from an inducible mouse model of FH loss in the kidney, demonstrating RS can classify FH status. These results suggest RS could be adopted as a valuable tool for small molecule metabolic imaging, enabling in situ non-destructive evaluation of fumarate compartmentalization.
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Affiliation(s)
- Marlous Kamp
- Department of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 0HE, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
- Department of Chemistry, Utrecht University, 3584 CH, Utrecht, The Netherlands
| | - Jakub Surmacki
- Lodz University of Technology, Institute of Applied Radiation Chemistry, Laboratory of Laser Molecular Spectroscopy, Wroblewskiego 15, 93-590, Lodz, Poland
| | - Marc Segarra Mondejar
- Hutchison/MRC Cancer Unit, University of Cambridge, Biomedical Campus, Cambridge, CB2 0XZ, UK
- CECAD, Joseph-Stelzmann-Straße 26, 50931, Cologne, Germany
| | - Tim Young
- Hutchison/MRC Cancer Unit, University of Cambridge, Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Karolina Chrabaszcz
- Institute of Nuclear Physics, Polish Academy of Sciences, Department of Experimental Physics of Complex Systems, Radzikowskiego 152, 31-342, Krakow, Poland
| | - Fadwa Joud
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
| | - Vincent Zecchini
- Hutchison/MRC Cancer Unit, University of Cambridge, Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Alyson Speed
- Hutchison/MRC Cancer Unit, University of Cambridge, Biomedical Campus, Cambridge, CB2 0XZ, UK
| | - Christian Frezza
- Hutchison/MRC Cancer Unit, University of Cambridge, Biomedical Campus, Cambridge, CB2 0XZ, UK.
- CECAD, Joseph-Stelzmann-Straße 26, 50931, Cologne, Germany.
| | - Sarah E Bohndiek
- Department of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 0HE, UK.
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK.
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12
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Wang K, Margolis S, Cho JM, Wang S, Arianpour B, Jabalera A, Yin J, Hong W, Zhang Y, Zhao P, Zhu E, Reddy S, Hsiai TK. Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2400596. [PMID: 38887178 DOI: 10.1002/advs.202400596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/17/2024] [Indexed: 06/20/2024]
Abstract
Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non-invasive and cost-effective detection method for early-stage NAFLD detection is a public health priority but challenging. In this study, an adhesive, soft on-skin sensor with low electrode-skin contact impedance for early-stage NAFLD detection is fabricated. A method is developed to synthesize platinum nanoparticles and reduced graphene quantum dots onto the on-skin sensor to reduce electrode-skin contact impedance by increasing double-layer capacitance, thereby enhancing detection accuracy. Furthermore, an attention-based deep learning algorithm is introduced to differentiate impedance signals associated with early-stage NAFLD in high-fat-diet-fed low-density lipoprotein receptor knockout (Ldlr-/-) mice compared to healthy controls. The integration of an adhesive, soft on-skin sensor with low electrode-skin contact impedance and the attention-based deep learning algorithm significantly enhances the detection accuracy for early-stage NAFLD, achieving a rate above 97.5% with an area under the receiver operating characteristic curve (AUC) of 1.0. The findings present a non-invasive approach for early-stage NAFLD detection and display a strategy for improved early detection through on-skin electronics and deep learning.
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Affiliation(s)
- Kaidong Wang
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Medicine, Greater Los Angeles Veterans Affairs (VA) Healthcare System, Los Angeles, CA, 90073, USA
| | - Samuel Margolis
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Jae Min Cho
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Shaolei Wang
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Brian Arianpour
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Alejandro Jabalera
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Junyi Yin
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Wen Hong
- Department of Materials Science and Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Yaran Zhang
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Peng Zhao
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Enbo Zhu
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Materials Science and Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Srinivasa Reddy
- Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Tzung K Hsiai
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Sciences, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Department of Medicine, Greater Los Angeles Veterans Affairs (VA) Healthcare System, Los Angeles, CA, 90073, USA
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13
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Man Z, Dong C, Bian J, Lu Z, Lu YQ, Zhang W. Optically Readable, Physically Unclonable Subwavelength Pixel via Multicolor Quantum Dot Printing for Anticounterfeiting. NANO LETTERS 2024; 24:7019-7024. [PMID: 38808680 DOI: 10.1021/acs.nanolett.4c01463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
We present a secure and user-friendly ultraminiaturized anticounterfeiting labeling technique─the color-encoded physical unclonable nanotag. These nanotags consist of subwavelength spots formed by random combinations of multicolor quantum dots, which are fabricated using a cost-efficient printing method developed in this study. The nanotags support over 170,000 different colors and are inherently resistant to cloning. Moreover, their high brightness and color purity, owing to the quantum dots, ensure an ease of readability. Additionally, these nanotags can function as color-encrypted pixels, enabling the incorporation of labels (such as QR codes) into ultrasmall physically unclonable hidden tags with a resolution exceeding 100,000 DPI. The unique blend of compactness, flexibility, and security positions the color-encoded nanotag as a potent and versatile solution for next-generation anticounterfeiting applications.
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Affiliation(s)
- Zaiqin Man
- College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, and MOE Key Laboratory of Intelligent Optical Sensing and Manipulation, Nanjing University, Nanjing, Jiangsu 210023, PR China
| | - Chenyu Dong
- College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, and MOE Key Laboratory of Intelligent Optical Sensing and Manipulation, Nanjing University, Nanjing, Jiangsu 210023, PR China
| | - Jie Bian
- College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, and MOE Key Laboratory of Intelligent Optical Sensing and Manipulation, Nanjing University, Nanjing, Jiangsu 210023, PR China
| | - Zhenda Lu
- College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, and MOE Key Laboratory of Intelligent Optical Sensing and Manipulation, Nanjing University, Nanjing, Jiangsu 210023, PR China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, Jiangsu 210023, PR China
| | - Yan-Qing Lu
- College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, and MOE Key Laboratory of Intelligent Optical Sensing and Manipulation, Nanjing University, Nanjing, Jiangsu 210023, PR China
| | - Weihua Zhang
- College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, and MOE Key Laboratory of Intelligent Optical Sensing and Manipulation, Nanjing University, Nanjing, Jiangsu 210023, PR China
- State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, Jiangsu 210023, PR China
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14
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Hříbek P, Vrtělka O, Králová K, Klasová J, Fousková M, Habartová L, Kubíčková K, Kupsa T, Tůma T, Setnička V, Urbánek P. Efficacy of blood plasma spectroscopy for early liver cancer diagnostics in obese patients. Ann Hepatol 2024; 29:101519. [PMID: 38866366 DOI: 10.1016/j.aohep.2024.101519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 04/12/2024] [Accepted: 05/16/2024] [Indexed: 06/14/2024]
Abstract
INTRODUCTION AND OBJECTIVES Hepatocellular carcinoma (HCC) represents one of the most common cancers worldwide. A considerable proportion of HCC is caused by cirrhosis related to metabolic dysfunction-associated steatohepatitis (MASH). Due to the increasing prevalence of metabolic syndrome, it is estimated that MASH-related HCC will become the most prevalent etiology of HCC. Currently, HCC screening is based on liver ultrasonography; however, the sensitivity of ultrasonography for early HCC stages in obese patients only reaches 23 %. To date, no studied biomarker shows sufficient efficacy for screening purposes. Nevertheless, the usage of spectroscopic methods offers a new perspective, as its potential use would provide cheap, fast analysis of samples such as blood plasma. MATERIAL AND METHODS We employed a combination of conventional and chiroptical spectroscopic methods to study differences between the blood plasma of obese cirrhotic patients with and without HCC. We included 20 subjects with HCC and 17 without evidence of liver cancer, all of them with body mass index ≥ 30. RESULTS Sensitivities and specificities reached values as follows: 0.780 and 0.905 for infrared spectroscopy, 0.700 and 0.767 for Raman spectroscopy, 0.840 and 0.743 for electronic circular dichroism, and 0.805 and 0.923 for Raman optical activity. The final combined classification model based on all spectroscopic methods reached a sensitivity of 0.810 and a specificity of 0.857, with the highest area under the receiver operating characteristic curve among all models (0.961). CONCLUSIONS We suggest that this approach can be used effectively as a diagnostic tool in patients who are not examinable by liver ultrasonography. CLINICAL TRIAL REGISTRATION NCT04221347.
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Affiliation(s)
- Petr Hříbek
- Military University Hospital Prague, Department of Medicine 1st Faculty of Medicine Charles University and Military University Hospital, U Vojenske nemocnice 1200, 16902 Prague, Czechia; Department of Internal Medicine, University of Defense, Faculty of Military Health Sciences, Trebesska 1575, 50001 Hradec Kralove, Czechia.
| | - Ondřej Vrtělka
- Department of Analytical Chemistry, University of Chemistry and Technology, Technicka 5, 16628 Prague, Czechia
| | - Kateřina Králová
- Department of Analytical Chemistry, University of Chemistry and Technology, Technicka 5, 16628 Prague, Czechia
| | - Johana Klasová
- Military University Hospital Prague, Department of Medicine 1st Faculty of Medicine Charles University and Military University Hospital, U Vojenske nemocnice 1200, 16902 Prague, Czechia
| | - Markéta Fousková
- Department of Analytical Chemistry, University of Chemistry and Technology, Technicka 5, 16628 Prague, Czechia
| | - Lucie Habartová
- Department of Analytical Chemistry, University of Chemistry and Technology, Technicka 5, 16628 Prague, Czechia
| | - Kristýna Kubíčková
- Military University Hospital Prague, Department of Medicine 1st Faculty of Medicine Charles University and Military University Hospital, U Vojenske nemocnice 1200, 16902 Prague, Czechia
| | - Tomáš Kupsa
- Department of Internal Medicine, University of Defense, Faculty of Military Health Sciences, Trebesska 1575, 50001 Hradec Kralove, Czechia
| | - Tomáš Tůma
- Department of Internal Medicine, University of Defense, Faculty of Military Health Sciences, Trebesska 1575, 50001 Hradec Kralove, Czechia
| | - Vladimír Setnička
- Department of Analytical Chemistry, University of Chemistry and Technology, Technicka 5, 16628 Prague, Czechia
| | - Petr Urbánek
- Military University Hospital Prague, Department of Medicine 1st Faculty of Medicine Charles University and Military University Hospital, U Vojenske nemocnice 1200, 16902 Prague, Czechia
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15
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Aeindartehran L, Sadri Z, Rahimi F, Alinejad T. Fluorescence in depth: integration of spectroscopy and imaging with Raman, IR, and CD for advanced research. Methods Appl Fluoresc 2024; 12:032002. [PMID: 38697201 DOI: 10.1088/2050-6120/ad46e6] [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: 12/27/2023] [Accepted: 05/02/2024] [Indexed: 05/04/2024]
Abstract
Fluorescence spectroscopy serves as a vital technique for studying the interaction between light and fluorescent molecules. It encompasses a range of methods, each presenting unique advantages and applications. This technique finds utility in various chemical studies. This review discusses Fluorescence spectroscopy, its branches such as Time-Resolved Fluorescence Spectroscopy (TRFS) and Fluorescence Lifetime Imaging Microscopy (FLIM), and their integration with other spectroscopic methods, including Raman, Infrared (IR), and Circular Dichroism (CD) spectroscopies. By delving into these methods, we aim to provide a comprehensive understanding of the capabilities and significance of fluorescence spectroscopy in scientific research, highlighting its diverse applications and the enhanced understanding it brings when combined with other spectroscopic methods. This review looks at each technique's unique features and applications. It discusses the prospects of their combined use in advancing scientific understanding and applications across various domains.
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Affiliation(s)
- Lida Aeindartehran
- Department of Chemistry, Southern Methodist University, Dallas, Texas 75275, United States of America
| | - Zahra Sadri
- Department of Biological Science, Southern Methodist University, Dallas, Texas 75205, United States of America
| | - Fateme Rahimi
- Department of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Tahereh Alinejad
- The Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, South Baixiang, Ouhai District, Wenzhou 325015, Zhejiang, People's Republic of China
- Institute of Cell Growth Factor, Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health), Wenzhou Medical University, Wenzhou 325000, People's Republic of China
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16
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Lu XY, Wu HP, Ma H, Li H, Li J, Liu YT, Pan ZY, Xie Y, Wang L, Ren B, Liu GK. Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives. Anal Chem 2024; 96:7959-7975. [PMID: 38662943 DOI: 10.1021/acs.analchem.4c01639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.
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Affiliation(s)
- Xin-Yu Lu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Hao-Ping Wu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, P. R. China
| | - Hao Ma
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Hui Li
- Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen 361005, P. R. China
| | - Jia Li
- Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, P. R. China
| | - Yan-Ti Liu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Zheng-Yan Pan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Yi Xie
- School of Informatics, Xiamen University, Xiamen 361005, P. R. China
| | - Lei Wang
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361005, P. R. China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Guo-Kun Liu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, P. R. China
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17
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Feng Y, Yang X, Rao Q, Zhang L, Su Y, Lv Y. Persistent Luminescence Lifetime-Based Near-Infrared Nanoplatform via Deep Learning for High-Fidelity Biosensing of Hypochlorite. Anal Chem 2024; 96:7240-7247. [PMID: 38661330 DOI: 10.1021/acs.analchem.4c00899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
In light of deep tissue penetration and ultralow background, near-infrared (NIR) persistent luminescence (PersL) bioprobes have become powerful tools for bioapplications. However, the inhomogeneous signal attenuation may significantly limit its application for precise biosensing owing to tissue absorption and scattering. In this work, a PersL lifetime-based nanoplatform via deep learning was proposed for high-fidelity bioimaging and biosensing in vivo. The persistent luminescence imaging network (PLI-Net), which consisted of a 3D-deep convolutional neural network (3D-CNN) and the PersL imaging system, was logically constructed to accurately extract the lifetime feature from the profile of PersL intensity-based decay images. Significantly, the NIR PersL nanomaterials represented by Zn1+xGa2-2xSnxO4: 0.4 % Cr (ZGSO) were precisely adjusted over their lifetime, enabling the PersL lifetime-based imaging with high-contrast signals. Inspired by the adjustable and reliable PersL lifetime imaging of ZGSO NPs, a proof-of-concept PersL nanoplatform was further developed and showed exceptional analytical performance for hypochlorite detection via a luminescence resonance energy transfer process. Remarkably, on the merits of the dependable and anti-interference PersL lifetimes, this PersL lifetime-based nanoprobe provided highly sensitive and accurate imaging of both endogenous and exogenous hypochlorite. This breakthrough opened up a new way for the development of high-fidelity biosensing in complex matrix systems.
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Affiliation(s)
- Yang Feng
- Analytical & Testing Center, Sichuan University, Chengdu 610064, China
| | - Xinyi Yang
- Analytical & Testing Center, Sichuan University, Chengdu 610064, China
| | - Qianli Rao
- Analytical & Testing Center, Sichuan University, Chengdu 610064, China
| | - Lichun Zhang
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yingying Su
- Analytical & Testing Center, Sichuan University, Chengdu 610064, China
| | - Yi Lv
- Analytical & Testing Center, Sichuan University, Chengdu 610064, China
- Key Laboratory of Green Chemistry & Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, China
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18
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Zhao Z, Jin Z, Wu G, Li C, Yu J. TriFNet: A triple-branch feature fusion network for pH determination by surface-enhanced Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 312:124048. [PMID: 38387412 DOI: 10.1016/j.saa.2024.124048] [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: 11/04/2023] [Revised: 02/11/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024]
Abstract
Due to the acidic tumor microenvironment caused by metabolic changes in tumor cells, the accurate pH detection of extracellular fluid is helpful for doctors in precise tumor resection. The combination of Raman spectroscopy and deep learning provides a solution for pH detection. However, most existing studies use one-dimensional convolutional neural networks (1D-CNNs) for spectral analysis, which limits the performance due to insufficient feature extraction. In this work, we propose a 2D triple-branch feature fusion network (TriFNet) for accurate pH determination using surface-enhanced Raman spectra (SERS). Specifically, we design a triple-branch network structure by converting Raman spectra into three types of images to extensively extract complex patterns in spectra. In addition, an attention fusion module, which leverages the complementarity among features in both space and channel, is designed to obtain the valuable information, achieving further accurate pH determination. On our Raman spectral dataset containing 14,137 samples, we achieved mean absolute error (MAE) of 0.059, standard deviation of the absolute error (SD) of 0.07, root mean squared error (RMSE) of 0.092, and coefficient of determination (R2) of 0.991 on the test set. Compared with other published methods, the four metrics showed an average improvement of 47%, 39%, 43%, and 6%, respectively. In addition, visualization validates the diagnostic capability of our model to correlate with biomolecular signatures. Meanwhile, our model has robustness to different SERS chips. These results prove the potential of our method to develop an effective technology based on Raman spectroscopy for accurate pH determination to guide surgery.
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Affiliation(s)
- Zheng Zhao
- School of Information Science and Technology, Fudan University, Shanghai 200438, China
| | - Ziyi Jin
- School of Pharmacy, Fudan University, Shanghai 201203, China
| | - Guoqing Wu
- School of Information Science and Technology, Fudan University, Shanghai 200438, China
| | - Cong Li
- School of Pharmacy, Fudan University, Shanghai 201203, China.
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai 200438, China.
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19
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Hu J, He L, Wang G, Liu L, Wang Y, Song J, Qu J, Peng X, Yuan Y. Rapid and accurate identification of marine bacteria spores at a single-cell resolution by laser tweezers Raman spectroscopy and deep learning. JOURNAL OF BIOPHOTONICS 2024; 17:e202300510. [PMID: 38302112 DOI: 10.1002/jbio.202300510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/01/2024] [Accepted: 01/04/2024] [Indexed: 02/03/2024]
Abstract
Marine bacteria have been considered as important participants in revealing various carbon/sulfur/nitrogen cycles of marine ecosystem. Thus, how to accurately identify rare marine bacteria without a culture process is significant and valuable. In this work, we constructed a single-cell Raman spectra dataset from five living bacteria spores and utilized convolutional neural network to rapidly, accurately, nondestructively identify bacteria spores. The optimal CNN architecture can provide a prediction accuracy of five bacteria spore as high as 94.93% ± 1.78%. To evaluate the classification weight of extracted spectra features, we proposed a novel algorithm by occluding fingerprint Raman bands. Based on the relative classification weight arranged from large to small, four Raman bands located at 1518, 1397, 1666, and 1017 cm-1 mostly contribute to producing such high prediction accuracy. It can be foreseen that, LTRS combined with CNN approach have great potential for identifying marine bacteria, which cannot be cultured under normal condition.
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Affiliation(s)
- Jianchang Hu
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, Guangdong, China
- School of Electronic Engineering and Intelligentization, Dongguan University of Technology, Dongguan, Guangdong, China
| | - Lin He
- School of Electronic Engineering and Intelligentization, Dongguan University of Technology, Dongguan, Guangdong, China
| | - Guiwen Wang
- Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning, Guangxi, China
| | - Liwei Liu
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, Guangdong, China
| | - Yiping Wang
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, Guangdong, China
| | - Jun Song
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, Guangdong, China
| | - Junle Qu
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, Guangdong, China
- Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiao Peng
- State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, Guangdong, China
| | - Yufeng Yuan
- School of Electronic Engineering and Intelligentization, Dongguan University of Technology, Dongguan, Guangdong, China
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20
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Li X, Liu Y, Fan Y, Tian G, Shen B, Zhang S, Fu X, He W, Tao X, Ding X, Li X, Ding S. Advanced Nanoencapsulation-Enabled Ultrasensitive Analysis: Unraveling Tumor Extracellular Vesicle Subpopulations for Differential Diagnosis of Hepatocellular Carcinoma via DNA Cascade Reactions. ACS NANO 2024; 18:11389-11403. [PMID: 38628141 DOI: 10.1021/acsnano.4c01310] [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: 05/01/2024]
Abstract
Tumor-derived extracellular vesicles (tEVs) hold immense promise as potential biomarkers for the precise diagnosis of hepatocellular carcinoma (HCC). However, their clinical translation is hampered by their inherent characteristics, such as small size and high heterogeneity and complex environment, including non-EV particles and normal cell-derived EVs, which prolong separation procedures and compromise detection accuracy. In this study, we devised a DNA cascade reaction-triggered individual EV nanoencapsulation (DCR-IEVN) strategy to achieve the ultrasensitive and specific detection of tEV subpopulations via routine flow cytometry in a one-pot, one-step fashion. DCR-IEVN enables the direct and selective packaging of multiple tEV subpopulations in clinical serum samples into flower-like particles exceeding 600 nm. This approach bypasses the need for EV isolation, effectively reducing interference from non-EV particles and nontumor EVs. Compared with conventional analytical technologies, DCR-IEVN exhibits superior efficacy in diagnosing HCC owing to its high selectivity for tEVs. Integration of machine learning algorithms with DCR-IEVN resulted in differential diagnosis accuracy of 96.7% for the training cohort (n = 120) and 93.3% for the validation cohort (n = 30), effectively distinguishing HCC, cirrhosis, and healthy donors. This strategy offers a streamlined workflow and rapid assay completion and requires only small-volume serum samples and routine clinical devices, facilitating the clinical translation of tEV-based tumor diagnosis.
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Affiliation(s)
- Xinyu Li
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Yuanjie Liu
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Yunpeng Fan
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, Department of Laboratory Medicine, Chongqing Hospital of Traditional Chinese Medicine, Chongqing 400016, China
| | - Gang Tian
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan 646000, China
| | - Bo Shen
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, Department of Laboratory Medicine, Chongqing Hospital of Traditional Chinese Medicine, Chongqing 400016, China
| | - Songzhi Zhang
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Xuhuai Fu
- Department of Clinical Laboratory, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital and Chongqing Cancer Institute, Chongqing 400030, China
| | - Wen He
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Xingyu Tao
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Xiaojuan Ding
- Department of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xinmin Li
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, Department of Laboratory Medicine, Chongqing Hospital of Traditional Chinese Medicine, Chongqing 400016, China
| | - Shijia Ding
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing 400016, China
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21
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Xu Y, Cao L, Chen Y, Zhang Z, Liu W, Li H, Ding C, Pu J, Qian K, Xu W. Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation. SMALL METHODS 2024:e2400305. [PMID: 38682615 DOI: 10.1002/smtd.202400305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 04/07/2024] [Indexed: 05/01/2024]
Abstract
Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.
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Affiliation(s)
- Yudian Xu
- Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Linlin Cao
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Yifan Chen
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Ziyue Zhang
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Wanshan Liu
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - He Li
- Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Chenhuan Ding
- Department of Traditional Chinese Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Jun Pu
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
| | - Kun Qian
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Wei Xu
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China
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22
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Nandipati M, Fatoki O, Desai S. Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review. MATERIALS (BASEL, SWITZERLAND) 2024; 17:1621. [PMID: 38612135 PMCID: PMC11012965 DOI: 10.3390/ma17071621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 02/05/2024] [Accepted: 03/29/2024] [Indexed: 04/14/2024]
Abstract
Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution-Industry 4.0-as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.
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Affiliation(s)
- Mutha Nandipati
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA; (M.N.); (O.F.)
| | - Olukayode Fatoki
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA; (M.N.); (O.F.)
| | - Salil Desai
- Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA; (M.N.); (O.F.)
- Center of Excellence in Product Design and Advanced Manufacturing, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
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23
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Rubio-García JJ, Mantilla Pinilla AJ, Gil Sánchez S, Villodre Tudela C, Alcázar López C, Melgar Requena P, Rodríguez Laiz G, Irurzun López J, Ramia-Ángel JM. Onyx®, A New Tool for Intraoperative Localization of Liver Lesions. Surg Innov 2024; 31:220-223. [PMID: 38387870 DOI: 10.1177/15533506241236732] [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] [Indexed: 02/24/2024]
Abstract
BACKGROUND Precise preoperative localization of liver tumors facilitates successful surgical procedures, Intraoperative ultrasonography is a sensitive imaging modality. However, the presence of small non-palpable isoechoic intraparenchymal lesions may be challenging intraoperatively. METHODOLOGY AND MATERIAL DESCRIPTION Onyx® is a non-adhesive liquid agent comprised of ethylene-vinyl alcohol usually used dissolved in dimethyl-sulfoxide and suspended micronized tantalum powder to provide contrast for visualization under fluoroscopy and ultrasonography and a macroscopic black shape. This embolization material has been increasingly used for the embolization of intracranial arteriovenous malformations. We present the novel application of Onyx® on liver surgery. CURRENT STATUS We present the case of a female, 55 years-old, whose medical history revealed an elective sigmoidectomy (pT3N1a). After 17 months of follow up, by PET-CT scan, the patient was diagnosed of a small intraparenchymal hypo-attenuated 13 mm tumor located at segment V consistent with metachronous colorectal liver metastasis. Open metastasectomy was performed, ultrasonography-guided Onyx® infusion was delivered the day after, intraoperative ultrasonography showed a palpable hyperechoic material with a posterior acoustic shadowing artifact around the lesion. Onyx® is a promising new tool, without any previous application on liver surgery, feasible with advantages in small not palpable intraparenchymal liver lesions.
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Affiliation(s)
- J J Rubio-García
- Servicio de Cirugía General y Aparato Digestivo, Hospital General Universitario de Alicante, Alicante, Spain
- Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain
| | - A J Mantilla Pinilla
- Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain
- Servicio de Radiodiagnóstico y Radiología Intervencionista, Hospital General Universitario de Alicante, Alicante, Spain
| | - S Gil Sánchez
- Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain
- Servicio de Radiodiagnóstico y Radiología Intervencionista, Hospital General Universitario de Alicante, Alicante, Spain
| | - C Villodre Tudela
- Servicio de Cirugía General y Aparato Digestivo, Hospital General Universitario de Alicante, Alicante, Spain
- Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain
| | - C Alcázar López
- Servicio de Cirugía General y Aparato Digestivo, Hospital General Universitario de Alicante, Alicante, Spain
- Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain
| | - P Melgar Requena
- Servicio de Cirugía General y Aparato Digestivo, Hospital General Universitario de Alicante, Alicante, Spain
- Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain
| | - G Rodríguez Laiz
- Servicio de Cirugía General y Aparato Digestivo, Hospital General Universitario de Alicante, Alicante, Spain
- Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain
| | - J Irurzun López
- Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain
- Servicio de Radiodiagnóstico y Radiología Intervencionista, Hospital General Universitario de Alicante, Alicante, Spain
| | - J M Ramia-Ángel
- Servicio de Cirugía General y Aparato Digestivo, Hospital General Universitario de Alicante, Alicante, Spain
- Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain
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24
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Vázquez-Iglesias L, Stanfoca Casagrande GM, García-Lojo D, Ferro Leal L, Ngo TA, Pérez-Juste J, Reis RM, Kant K, Pastoriza-Santos I. SERS sensing for cancer biomarker: Approaches and directions. Bioact Mater 2024; 34:248-268. [PMID: 38260819 PMCID: PMC10801148 DOI: 10.1016/j.bioactmat.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 12/14/2023] [Accepted: 12/21/2023] [Indexed: 01/24/2024] Open
Abstract
These days, cancer is thought to be more than just one illness, with several complex subtypes that require different screening approaches. These subtypes can be distinguished by the distinct markings left by metabolites, proteins, miRNA, and DNA. Personalized illness management may be possible if cancer is categorized according to its biomarkers. In order to stop cancer from spreading and posing a significant risk to patient survival, early detection and prompt treatment are essential. Traditional cancer screening techniques are tedious, time-consuming, and require expert personnel for analysis. This has led scientists to reevaluate screening methodologies and make use of emerging technologies to achieve better results. Using time and money saving techniques, these methodologies integrate the procedures from sample preparation to detection in small devices with high accuracy and sensitivity. With its proven potential for biomedical use, surface-enhanced Raman scattering (SERS) has been widely used in biosensing applications, particularly in biomarker identification. Consideration was given especially to the potential of SERS as a portable clinical diagnostic tool. The approaches to SERS-based sensing technologies for both invasive and non-invasive samples are reviewed in this article, along with sample preparation techniques and obstacles. Aside from these significant constraints in the detection approach and techniques, the review also takes into account the complexity of biological fluids, the availability of biomarkers, and their sensitivity and selectivity, which are generally lowered. Massive ways to maintain sensing capabilities in clinical samples are being developed recently to get over this restriction. SERS is known to be a reliable diagnostic method for treatment judgments. Nonetheless, there is still room for advancement in terms of portability, creation of diagnostic apps, and interdisciplinary AI-based applications. Therefore, we will outline the current state of technological maturity for SERS-based cancer biomarker detection in this article. The review will meet the demand for reviewing various sample types (invasive and non-invasive) of cancer biomarkers and their detection using SERS. It will also shed light on the growing body of research on portable methods for clinical application and quick cancer detection.
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Affiliation(s)
- Lorena Vázquez-Iglesias
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
| | | | - Daniel García-Lojo
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
| | - Letícia Ferro Leal
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil
- Barretos School of Medicine Dr. Paulo Prata—FACISB, Barretos, 14785-002, Brazil
| | - Tien Anh Ngo
- Vinmec Tissue Bank, Vinmec Health Care System, Hanoi, Viet Nam
| | - Jorge Pérez-Juste
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
| | - Rui Manuel Reis
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil
- Life and Health Sciences Research Institute (ICVS), School of Medicine, Campus de Gualtar, University of Minho, 4710-057 Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, 4710-057, Braga, Portugal
| | - Krishna Kant
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
| | - Isabel Pastoriza-Santos
- CINBIO, Universidade de Vigo, Campus Universitario As Lagoas Marcosende, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), 36310, Vigo, Spain
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25
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Hu J, Chen GJ, Xue C, Liang P, Xiang Y, Zhang C, Chi X, Liu G, Ye Y, Cui D, Zhang D, Yu X, Dang H, Zhang W, Chen J, Tang Q, Guo P, Ho HP, Li Y, Cong L, Shum PP. RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization. LIGHT, SCIENCE & APPLICATIONS 2024; 13:52. [PMID: 38374161 PMCID: PMC10876988 DOI: 10.1038/s41377-024-01394-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 01/02/2024] [Accepted: 01/23/2024] [Indexed: 02/21/2024]
Abstract
Raman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and technology. It is also an emerging omics technique for metabolic profiling to shape precision medicine. However, precisely attributing vibration peaks coupled with specific environmental, instrumental, and specimen noise is problematic. Intelligent Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information extraction. Here, we propose a novel Raman spectral preprocessing scheme based on self-supervised learning (RSPSSL) with high capacity and spectral fidelity. It can preprocess arbitrary Raman spectra without further training at a speed of ~1 900 spectra per second without human interference. The experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88% reduction in root mean square error and a 60% reduction in infinite norm ([Formula: see text]) compared to established techniques. With this advantage, it remarkably enhanced various biomedical applications with a 400% accuracy elevation (ΔAUC) in cancer diagnosis, an average 38% (few-shot) and 242% accuracy improvement in paraquat concentration prediction, and unsealed the chemical resolution of biomedical hyperspectral images, especially in the spectral fingerprint region. It precisely preprocessed various Raman spectra from different spectroscopy devices, laboratories, and diverse applications. This scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput, cross-device, various analyte complexity, and diverse applications.
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Affiliation(s)
- Jiaqi Hu
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Gina Jinna Chen
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Chenlong Xue
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Pei Liang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Yanqun Xiang
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Chuanlun Zhang
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Xiaokeng Chi
- Department of Nephrology, Chaozhou People's Hospital, Chaozhou, 521011, China
| | - Guoying Liu
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Yanfang Ye
- Clinical Research Design Division, Sun Yat-sen Memorial Hospital, Guangzhou, Guangdong, 510120, China
| | - Dongyu Cui
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - De Zhang
- College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018, China
| | - Xiaojun Yu
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China
| | - Hong Dang
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Wen Zhang
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Junfan Chen
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Quan Tang
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Penglai Guo
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Ho-Pui Ho
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Yuchao Li
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, Jinan University, Guangzhou, 511443, China
| | - Longqing Cong
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Perry Ping Shum
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen, 518055, China.
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26
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Lu XY, Wang CY, Tang H, Qin YF, Cui L, Wang X, Liu GK, Ren B. Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information. Anal Chem 2024. [PMID: 38324760 DOI: 10.1021/acs.analchem.3c03889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.
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Affiliation(s)
- Xin-Yu Lu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Chen-Yue Wang
- Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
| | - Hui Tang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yi-Fei Qin
- Xiamen Key Laboratory of Indoor Air and Health, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li Cui
- Xiamen Key Laboratory of Indoor Air and Health, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiang Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, China
| | - Guo-Kun Liu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, China
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Zhou M, Jie W, Tang F, Zhang S, Mao Q, Liu C, Hao Y. Deep learning algorithms for classification and detection of recurrent aphthous ulcerations using oral clinical photographic images. J Dent Sci 2024; 19:254-260. [PMID: 38303872 PMCID: PMC10829559 DOI: 10.1016/j.jds.2023.04.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 04/19/2023] [Indexed: 02/03/2024] Open
Abstract
Background/purpose The application of artificial intelligence diagnosis based on deep learning in the medical field has been widely accepted. We aimed to evaluate convolutional neural networks (CNNs) for automated classification and detection of recurrent aphthous ulcerations (RAU), normal oral mucosa, and other common oral mucosal diseases in clinical oral photographs. Materials and methods The study included 785 clinical oral photographs, which was divided into 251 images of RAU, 271 images of the normal oral mucosa, and 263 images of other common oral mucosal diseases. Four and three CNN models were used for the classification and detection tasks, respectively. 628 images were randomly selected as training data. In addition, 78 and 79 images were assigned as validating and testing data. Main outcome measures included precision, recall, F1, specificity, sensitivity and area under the receiver operating characteristics curve (AUC). Results In the classification task, the Pretrained ResNet50 model had the best performance with a precision of 92.86%, a recall of 91.84%, an F1 score of 92.24%, a specificity of 96.41%, a sensitivity of 91.84% and an AUC of 98.95%. In the detection task, the Pretrained YOLOV5 model had the best performance with a precision of 98.70%, a recall of 79.51%, an F1 score of 88.07% and an AUC of Precision-Recall curve 90.89%. Conclusion The Pretrained ResNet50 and the Pretrained YOLOV5 algorithms were shown to have superior performance and acceptable potential in the classification and detection of RAU lesions based on non-invasive oral images, which may prove useful in clinical practice.
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Affiliation(s)
- Mimi Zhou
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Weiping Jie
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Fan Tang
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Shangjun Zhang
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Qinghua Mao
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Chuanxia Liu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Yilong Hao
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
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Bi X, Lin L, Chen Z, Ye J. Artificial Intelligence for Surface-Enhanced Raman Spectroscopy. SMALL METHODS 2024; 8:e2301243. [PMID: 37888799 DOI: 10.1002/smtd.202301243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.
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Affiliation(s)
- Xinyuan Bi
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Li Lin
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Zhou Chen
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Jian Ye
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
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Li Z, Huang X, Xu L, Peng Z, Yu XX, Shi W, He X, Meng X, Yang D, Tong L, Miao X, Ye L. 2D van der Waals Vertical Heterojunction Transistors for Ternary Neural Networks. NANO LETTERS 2023; 23:11710-11718. [PMID: 37890139 DOI: 10.1021/acs.nanolett.3c03553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/29/2023]
Abstract
Compared with binary systems, ternary computing systems can utilize fewer devices to realize the same information density. However, most ternary computing systems based on binary CMOS circuits require additional devices to bridge binary processing and ternary computing. Exploring new device architectures for direct ternary processing and computing becomes the key to promoting ternary computing systems. Here, we demonstrated a 2D van der Waals vertical heterojunction transistor (V-HTR) with three flat conductance states, which can be the basic cell in ternary circuits to perform ternary processing and computing, without additional devices. A ternary neural network (TNN) and a ternary inverter were demonstrated based on the V-HTRs. The TNN can eliminate fuzzy data and output only clear data by building a ternary quantization function. By demonstrating both ternary logic and a TNN on the same device architecture, the 2D V-HTR shows potential as a basic hardware unit for future ternary computing systems.
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Affiliation(s)
- Zheng Li
- School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xinyu Huang
- School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Langlang Xu
- School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Zhuiri Peng
- School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xiang-Xiang Yu
- School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Wenhao Shi
- School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xiao He
- School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xiaohan Meng
- School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Daohong Yang
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Lei Tong
- Department of Electronic Engineering, Materials Science and Technology Research Center, The Chinese University of Hong Kong, Hong Kong, SAR, China
| | - Xiangshui Miao
- School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Lei Ye
- School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
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30
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Ma’ruf M, Irham LM, Adikusuma W, Sarasmita MA, Khairi S, Purwanto BD, Chong R, Mazaya M, Siswanto LMH. A genomic and bioinformatic-based approach to identify genetic variants for liver cancer across multiple continents. Genomics Inform 2023; 21:e48. [PMID: 38224715 PMCID: PMC10788354 DOI: 10.5808/gi.23067] [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: 08/14/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 01/17/2024] Open
Abstract
Liver cancer is the fourth leading cause of death worldwide. Well-known risk factors include hepatitis B virus and hepatitis C virus, along with exposure to aflatoxins, excessive alcohol consumption, obesity, and type 2 diabetes. Genomic variants play a crucial role in mediating the associations between these risk factors and liver cancer. However, the specific variants involved in this process remain under-explored. This study utilized a bioinformatics approach to identify genetic variants associated with liver cancer from various continents. Single-nucleotide polymorphisms associated with liver cancer were retrieved from the genome-wide association studies catalog. Prioritization was then performed using functional annotation with HaploReg v4.1 and the Ensembl database. The prevalence and allele frequencies of each variant were evaluated using Pearson correlation coefficients. Two variants, rs2294915 and rs2896019, encoded by the PNPLA3 gene, were found to be highly expressed in the liver tissue, as well as in the skin, cell-cultured fibroblasts, and adipose-subcutaneous tissue, all of which contribute to the risk of liver cancer. We further found that these two SNPs (rs2294915 and rs2896019) were positively correlated with the prevalence rate. Positive associations with the prevalence rate were more frequent in East Asian and African populations. We highlight the utility of this population-specific PNPLA3 genetic variant for genetic association studies and for the early prognosis and treatment of liver cancer. This study highlights the potential of integrating genomic databases with bioinformatic analysis to identify genetic variations involved in the pathogenesis of liver cancer. The genetic variants investigated in this study are likely to predispose to liver cancer and could affect its progression and aggressiveness. We recommend future research prioritizing the validation of these variations in clinical settings.
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Affiliation(s)
- Muhammad Ma’ruf
- Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta 55164, Indonesia
| | | | - Wirawan Adikusuma
- Departement of Pharmacy, University of Muhammadiyah Mataram, Mataram 83127, Indonesia
| | - Made Ary Sarasmita
- Department of Clinical Pharmacy, College of Pharmacy, Taipei Medical University, Taipei 110, Taiwan
- Pharmacy Study Program, Faculty of Science and Mathematics, Udayana University, Bali, Indonesia
| | - Sabiah Khairi
- School of Nursing, College of Nursing, Taipei Medical University, Taipei 11031, Taiwan
| | - Barkah Djaka Purwanto
- Faculty of Medicine, Universitas Ahmad Dahlan, Yogyakarta 55191, Indonesia
- PKU Muhammadiyah Bantul Hospital, Bantul, Yogyakarta 55711, Indonesia
| | - Rockie Chong
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Maulida Mazaya
- Research Center for Computing, Research Organization for Electronics and Informatics, National Research and Innovation Agency (BRIN), Cibinong Science Center, Cibinong 16911, Indonesia
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31
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Yin M, Zhao L, Liu S, Tian S, Meng F, Luo L. Conjugation Length-Dependent Raman Scattering Intensity of Conjugated Polymers. Macromol Rapid Commun 2023; 44:e2300412. [PMID: 37713720 DOI: 10.1002/marc.202300412] [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: 07/06/2023] [Revised: 09/12/2023] [Indexed: 09/17/2023]
Abstract
Polydiacetylenes, as a class of conjugated polymers with alternating conjugated C═C and C≡C bonds, have emerged as a promising probe material for biomedical Raman imaging, given their ultrastrong Raman scattering intensity. However, the relationship between the structure, especially the molecular length of polydiacetylenes, and their Raman scattering intensity remains unclear. In this work, a series of water-soluble polydiacetylenes, namely poly(deca-4,6-diynedioic acid) (PDDA) with different molecular weights (MWs), is prepared through controlled polymerization and degradation. The ultraviolet-visible (UV-vis) absorption spectroscopic and Raman spectroscopic studies on these polymers reveal that the Raman scattering intensity of PDDA increases nonlinearly with the MW. The MW-Raman scattering intensity relationship in the polymerization process is completely different from that in the degradation process. In contrast, the Raman scattering intensity increases more linearly with the maximal absorbance of the polymer, and the relationship between the Raman scattering intensity and the maximal absorbance of PDDA in the polymerization process is consistent with that in the degradation process. The Raman scattering intensity of PDDA hence exhibits a better dependence on the effective conjugation length of the polymer, which should guide the future design of conjugated polymers for Raman imaging applications.
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Affiliation(s)
- Mingming Yin
- National Engineering Research Center for Nanomedicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Liyuan Zhao
- National Engineering Research Center for Nanomedicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Sujuan Liu
- National Engineering Research Center for Nanomedicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Sidan Tian
- National Engineering Research Center for Nanomedicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Fanling Meng
- National Engineering Research Center for Nanomedicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Liang Luo
- National Engineering Research Center for Nanomedicine, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
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Liu K, Liu B, Wang Y, Zhao Q, Wu Q, Li B. Evaluation of Raman spectroscopy combined with the gated recurrent unit serum detection method in early screening of gastrointestinal cancer. Analyst 2023; 148:6061-6069. [PMID: 37902303 DOI: 10.1039/d3an01259j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
Gastric and colorectal cancers are significant causes of human mortality. Conventionally, the diagnosis of gastrointestinal tumors has been accomplished through image-based techniques, including endoscopic and biopsy procedures coupled with tissue staining. Most of these methods are invasive. In contrast, Raman spectroscopy has the advantages of being non-invasive and label-free and requiring no additional reagents, making it a potential tool for the detection of serum components. In this study, we collected Raman spectra of serum samples from patients with gastric cancer (n = 93) and colorectal cancer (n = 92) and from healthy individuals (n = 100). Analysis of Raman peak areas revealed that cancer patients had significantly higher peak areas at around 2923 cm-1 compared to normal individuals, which corresponded to the presence of lipids and proteins. We successfully achieved the early screening of gastrointestinal tumors using the improved gated recurrent unit (GRU) algorithm and traditional machine learning methods. The accuracy of identifying digestive tract tumors using different recognition models exceeds 84.72%, with support vector machine (SVM) and GRU achieving 100% accuracy. The use of GRU further demonstrated its ability to differentiate subtypes of gastric and colorectal cancers based on the degree of differentiation and stage, with a recognition accuracy exceeding 95%, which is challenging using traditional machine learning methods. Furthermore, our study revealed that principal component analysis (PCA) dimensionality reduction has a limited impact on the recognition results obtained using different recognition models.
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Affiliation(s)
- Kunxiang Liu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, P. R. China.
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Bo Liu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, P. R. China.
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Yu Wang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, P. R. China.
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Qi Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P. R. China
- Cancer Microbiome Platform, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong 510060, P. R. China
| | - Qinian Wu
- Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510060, P. R. China.
| | - Bei Li
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, P. R. China.
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
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33
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Esposito C, Janneh M, Spaziani S, Calcagno V, Bernardi ML, Iammarino M, Verdone C, Tagliamonte M, Buonaguro L, Pisco M, Aversano L, Cusano A. Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy. Cells 2023; 12:2645. [PMID: 37998378 PMCID: PMC10670489 DOI: 10.3390/cells12222645] [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: 09/20/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells' Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.
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Affiliation(s)
- Concetta Esposito
- Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
| | - Mohammed Janneh
- Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
| | - Sara Spaziani
- Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
| | - Vincenzo Calcagno
- Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
| | - Mario Luca Bernardi
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
- Informatics Group, Engineering Department, University of Sannio, 82100 Benevento, Italy
| | - Martina Iammarino
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
- Informatics Group, Engineering Department, University of Sannio, 82100 Benevento, Italy
| | - Chiara Verdone
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
- Informatics Group, Engineering Department, University of Sannio, 82100 Benevento, Italy
| | - Maria Tagliamonte
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
- National Cancer Institute-IRCCS “Pascale”, Via Mariano Semmola, 52, 80131 Napoli, Italy
| | - Luigi Buonaguro
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
- National Cancer Institute-IRCCS “Pascale”, Via Mariano Semmola, 52, 80131 Napoli, Italy
| | - Marco Pisco
- Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
| | - Lerina Aversano
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
- Informatics Group, Engineering Department, University of Sannio, 82100 Benevento, Italy
| | - Andrea Cusano
- Optoelectronic Division-Engineering Department, University of Sannio, 82100 Benevento, Italy
- Centro Regionale Information Communication Technology (CeRICT Scrl), 82100 Benevento, Italy; (M.L.B.); (L.B.)
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Chen D, Xia Z, Guo Z, Gou W, Zhao J, Zhou X, Tan X, Li W, Zhao S, Tian Z, Qu Y. Bioinspired porous three-coordinated single-atom Fe nanozyme with oxidase-like activity for tumor visual identification via glutathione. Nat Commun 2023; 14:7127. [PMID: 37949885 PMCID: PMC10638392 DOI: 10.1038/s41467-023-42889-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023] Open
Abstract
Inspired by structures of natural metalloenzymes, a biomimetic synthetic strategy is developed for scalable synthesis of porous Fe-N3 single atom nanozymes (pFeSAN) using hemoglobin as Fe-source and template. pFeSAN delivers 3.3- and 8791-fold higher oxidase-like activity than Fe-N4 and Fe3O4 nanozymes. The high catalytic performance is attributed to (1) the suppressed aggregation of atomically dispersed Fe; (2) facilitated mass transfer and maximized exposure of active sites for the created mesopores by thermal removal of hemoglobin (2 ~ 3 nm); and (3) unique electronic configuration of Fe-N3 for the oxygen-to-water oxidation pathway (analogy with natural cytochrome c oxidase). The pFeSAN is successfully demonstrated for the rapid colorimetric detection of glutathione with a low limit of detection (2.4 nM) and wide range (50 nM-1 mM), and further developed as a real-time, facile, rapid (~6 min) and precise visualization analysis methodology of tumors via glutathione level, showing its potentials for diagnostic and clinic applications.
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Affiliation(s)
- Da Chen
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Zhaoming Xia
- Department of Chemistry, Southern University of Science and Technology, 518055, Shenzhen, China
| | - Zhixiong Guo
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Wangyan Gou
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Junlong Zhao
- State Key Laboratory of Cancer Biology, Department of Medical Genetics and Developmental Biology, Fourth Military Medical University, 710032, Xi'an, China
| | - Xuemei Zhou
- Key Laboratory of Carbon Materials of Zhejiang Province, Wenzhou University, 325035, Wenzhou, China
| | - Xiaohe Tan
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Wenbin Li
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, 710072, Xi'an, China
| | - Shoujie Zhao
- State Key Laboratory of Cancer Biology, Department of Medical Genetics and Developmental Biology, Fourth Military Medical University, 710032, Xi'an, China
| | - Zhimin Tian
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, 710072, Xi'an, China.
| | - Yongquan Qu
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, 710072, Xi'an, China.
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Jiang Z, Wang X, Chu K, Smith ZJ. Fast Raman imaging through the combination of context-aware matrix completion and low spectral resolution. Analyst 2023; 148:4710-4720. [PMID: 37622207 DOI: 10.1039/d3an00997a] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
Raman hyperspectral imaging is an effective method for label-free imaging with chemical specificity, yet the weak signals and correspondingly long integration times have hindered its wide adoption as a routine analytical method. Recently, low resolution Raman imaging has been proposed to improve the spectral signal-to-noise ratio, which significantly improves the speed of Raman imaging. In this paper, low resolution Raman spectroscopy is combined with "context-aware" matrix completion, where regions of the sample that are not of interest are skipped, and the regions that are measured are under-sampled, then reconstructed with a low-rank constraint. Both simulations and experiment show that low-resolution Raman boosts the speed and image quality of the computationally-reconstructed Raman images, allowing deeper sub-sampling, reduced exposure time, and an overall >10-fold improvement in imaging speed, without sacrificing chemical specificity or spatial image quality. As the method utilizes traditional point-scan imaging, it retains full confocality and is "backwards-compatible" with pre-existing traditional Raman instruments, broadening the potential scope of Raman imaging applications.
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Affiliation(s)
- Ziling Jiang
- University of Science and Technology of China, Department of Precision Machinery & Precision Instrumentation, Hefei, Anhui, China 230027.
| | - Xianli Wang
- University of Science and Technology of China, Department of Precision Machinery & Precision Instrumentation, Hefei, Anhui, China 230027.
| | - Kaiqin Chu
- University of Science and Technology of China, Suzhou Institute for Advanced Research, Suzhou, Jiangsu, China 215123
| | - Zachary J Smith
- University of Science and Technology of China, Department of Precision Machinery & Precision Instrumentation, Hefei, Anhui, China 230027.
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Oshima Y, Haruki T, Koizumi K, Yonezawa S, Taketani A, Kadowaki M, Saito S. Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy. Int J Mol Sci 2023; 24:12170. [PMID: 37569541 PMCID: PMC10418989 DOI: 10.3390/ijms241512170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 07/23/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.
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Affiliation(s)
- Yusuke Oshima
- Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Medicine, Oita University, Yufu 879-5593, Japan
| | - Takayuki Haruki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Sustainable Design, University of Toyama, Toyama 930-8555, Japan
| | - Keiichi Koizumi
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-8555, Japan
| | - Shota Yonezawa
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Akinori Taketani
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Makoto Kadowaki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Shigeru Saito
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
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Arya SS, Dias SB, Jelinek HF, Hadjileontiadis LJ, Pappa AM. The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics? Biosens Bioelectron 2023; 235:115387. [PMID: 37229842 DOI: 10.1016/j.bios.2023.115387] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 04/11/2023] [Accepted: 05/10/2023] [Indexed: 05/27/2023]
Abstract
Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.
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Affiliation(s)
- Sagar S Arya
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Interdisciplinary Center for Human Performance, Faculdade de Motricidade Humana, Universidade de Lisboa, Portugal.
| | - Herbert F Jelinek
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates; Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR, 54124, Thessaloniki, Greece
| | - Anna-Maria Pappa
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates; Department of Chemical Engineering and Biotechnology, Cambridge University, Cambridge, UK.
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Khristoforova YA, Bratchenko LA, Skuratova MA, Lebedeva EA, Lebedev PA, Bratchenko IA. Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis. JOURNAL OF BIOPHOTONICS 2023:e202300016. [PMID: 36999197 DOI: 10.1002/jbio.202300016] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/09/2023] [Accepted: 03/28/2023] [Indexed: 06/19/2023]
Abstract
This work aims at studying Raman spectroscopy in combination with chemometrics as an alternative fast noninvasive method to detect chronic heart failure (CHF) cases. Optical analysis is focused on the changes in the spectral features associated with the biochemical composition changes of skin tissues. A portable spectroscopy setup with the 785 nm excitation wavelength was used to record skin Raman features. In this in vivo study, 127 patients and 57 healthy volunteers were involved in measuring skin spectral features by Raman spectroscopy. The spectral data were analyzed with a projection on the latent structures and discriminant analysis. 202 skin spectra of patients with CHF and 90 skin spectra of healthy volunteers were classified with 0.888 ROC AUC for the 10-fold cross validated algorithm. To identify CHF cases, the performance of the proposed classifier was verified by means of a new test set that is equal to 0.917 ROC AUC.
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Affiliation(s)
- Yulia A Khristoforova
- Department of Laser and Biotechnical Systems, Samara National Research University, Samara, Russia
| | - Lyudmila A Bratchenko
- Department of Laser and Biotechnical Systems, Samara National Research University, Samara, Russia
| | - Maria A Skuratova
- Cardiology Department, City Clinical Hospital № 1 named after N. I. Pirogov, Samara, Russia
| | - Elena A Lebedeva
- Cardiology Department, City Clinical Hospital № 1 named after N. I. Pirogov, Samara, Russia
| | - Petr A Lebedev
- Therapy chair of Postgraduate Department, Samara State Medical University, Samara, Russia
| | - Ivan A Bratchenko
- Department of Laser and Biotechnical Systems, Samara National Research University, Samara, Russia
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Qiu S, Cai Y, Yao H, Lin C, Xie Y, Tang S, Zhang A. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther 2023; 8:132. [PMID: 36941259 PMCID: PMC10026263 DOI: 10.1038/s41392-023-01399-3] [Citation(s) in RCA: 99] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/22/2023] Open
Abstract
Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.
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Affiliation(s)
- Shi Qiu
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China
| | - Ying Cai
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China
| | - Hong Yao
- First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Chunsheng Lin
- Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150001, China
| | - Yiqiang Xie
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Songqi Tang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Aihua Zhang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China.
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