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Xiong XL, Ma YP, Liu H, Huang CZ, Zhou J. Efficient and Accurate pH Determination with pH Test Strips Based on Machine Learning. Anal Chem 2024; 96:11498-11507. [PMID: 38946253 DOI: 10.1021/acs.analchem.4c02153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The determination of pH values is crucial in various fields, such as analytical chemistry, medical diagnostics, and biochemical research. pH test strips, renowned for their convenience and cost-effectiveness, are commonly utilized for pH qualitative estimation. Recently, quantitative methods for determining pH values using pH test strips have been developed. However, these methods can be prone to errors due to environmental factors, such as lighting conditions, which affect the imaging quality of the pH test strips. To address these challenges, we developed an innovative approach that combines machine learning techniques with pH test strips for the quantitative determination of pH values. Our method involves extracting artificial features from the pH test strip images and combining them across multiple dimensions for comprehensive analysis. To ensure optimal feature selection, we developed a feature selection strategy based on SHAP importance. This strategy helps in identifying the most relevant features that contribute to accurate pH prediction. Furthermore, we integrated multiple machine learning algorithms, employing a robust stacking fusion strategy to establish a highly reliable pH value prediction model. Our proposed method automates the determination of pH values through pH test strips, effectively overcoming the limitations associated with environmental lighting interference. Experimental results demonstrate that this method is convenient, effective, and highly reliable for the determination of pH values.
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Affiliation(s)
- Xiao Long Xiong
- College of Computer and Information Science, Southwest University, Chongqing 400715, China
| | - Yun Peng Ma
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, China
| | - Hui Liu
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, China
| | - Cheng Zhi Huang
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, China
| | - Jun Zhou
- College of Computer and Information Science, Southwest University, Chongqing 400715, China
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2
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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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3
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Gao Y, Zheng P, Meng ZD, Wang HL, You EM, Zhong JH, Tian ZQ, Wang L, He H. Fast Nano-IR Hyperspectral Imaging Empowered by Large-Dataset-Free Miniaturized Spatial-Spectral Network. Anal Chem 2024; 96:9610-9620. [PMID: 38822784 DOI: 10.1021/acs.analchem.4c01211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2024]
Abstract
The emerging field of nanoscale infrared (nano-IR) offers label-free molecular contrast, yet its imaging speed is limited by point-by-point traverse acquisition of a three-dimensional (3D) data cube. Here, we develop a spatial-spectral network (SS-Net), a miniaturized deep-learning model, together with compressive sampling to accelerate the nano-IR imaging. The compressive sampling is performed in both the spatial and spectral domains to accelerate the imaging process. The SS-Net is trained to learn the mapping from small nano-IR image patches to the corresponding spectra. With this elaborated mapping strategy, the training can be finished quickly within several minutes using the subsampled data, eliminating the need for a large-labeled dataset of common deep learning methods. We also designed an efficient loss function, which incorporates the image and spectral similarity to enhance the training. We first validate the SS-Net on an open stimulated Raman-scattering dataset; the results exhibit the potential of 10-fold imaging speed improvement with state-of-the-art performance. We then demonstrate the versatility of this approach on atomic force microscopy infrared (AFM-IR) microscopy with 7-fold imaging speed improvement, even on nanoscale Fourier transform infrared (nano-FTIR) microscopy with up to 261.6 folds faster imaging speed. We further showcase the generalization of this method on AFM-force volume-based multiparametric nanoimaging. This method establishes a paradigm for rapid nano-IR imaging, opening new possibilities for cutting-edge research in materials, photonics, and beyond.
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Affiliation(s)
- Yun Gao
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China
| | - Peng Zheng
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China
| | - Zhao-Dong Meng
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Hai-Long Wang
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361102, China
| | - En-Ming You
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- School of Ocean Information Engineering, Jimei University, Xiamen 361021, China
| | - Jin-Hui Zhong
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Zhong-Qun Tian
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Lei Wang
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China
| | - Hao He
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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4
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Tabata K, Kawagoe H, Taylor JN, Mochizuki K, Kubo T, Clement JE, Kumamoto Y, Harada Y, Nakamura A, Fujita K, Komatsuzaki T. On-the-fly Raman microscopy guaranteeing the accuracy of discrimination. Proc Natl Acad Sci U S A 2024; 121:e2304866121. [PMID: 38483992 PMCID: PMC10962959 DOI: 10.1073/pnas.2304866121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 12/15/2023] [Indexed: 03/19/2024] Open
Abstract
Accelerating the measurement for discrimination of samples, such as classification of cell phenotype, is crucial when faced with significant time and cost constraints. Spontaneous Raman microscopy offers label-free, rich chemical information but suffers from long acquisition time due to extremely small scattering cross-sections. One possible approach to accelerate the measurement is by measuring necessary parts with a suitable number of illumination points. However, how to design these points during measurement remains a challenge. To address this, we developed an imaging technique based on a reinforcement learning in machine learning (ML). This ML approach adaptively feeds back "optimal" illumination pattern during the measurement to detect the existence of specific characteristics of interest, allowing faster measurements while guaranteeing discrimination accuracy. Using a set of Raman images of human follicular thyroid and follicular thyroid carcinoma cells, we showed that our technique requires 3,333 to 31,683 times smaller number of illuminations for discriminating the phenotypes than raster scanning. To quantitatively evaluate the number of illuminations depending on the requisite discrimination accuracy, we prepared a set of polymer bead mixture samples to model anomalous and normal tissues. We then applied a home-built programmable-illumination microscope equipped with our algorithm, and confirmed that the system can discriminate the sample conditions with 104 to 4,350 times smaller number of illuminations compared to standard point illumination Raman microscopy. The proposed algorithm can be applied to other types of microscopy that can control measurement condition on the fly, offering an approach for the acceleration of accurate measurements in various applications including medical diagnosis.
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Affiliation(s)
- Koji Tabata
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo001–0020, Hokkaido, Japan
- Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo001–0021, Hokkaido, Japan
| | - Hiroyuki Kawagoe
- Department of Applied Physics, Osaka University, Suita565–0871, Osaka, Japan
| | - J. Nicholas Taylor
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo001–0020, Hokkaido, Japan
| | - Kentaro Mochizuki
- Department of Pathology and Cell Regulation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto602–8566, Kyoto, Japan
| | - Toshiki Kubo
- Department of Applied Physics, Osaka University, Suita565–0871, Osaka, Japan
| | - Jean-Emmanuel Clement
- Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo001–0021, Hokkaido, Japan
| | - Yasuaki Kumamoto
- Department of Applied Physics, Osaka University, Suita565–0871, Osaka, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita565–0871, Osaka, Japan
| | - Yoshinori Harada
- Department of Pathology and Cell Regulation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto602–8566, Kyoto, Japan
| | - Atsuyoshi Nakamura
- Graduate School of Information Science and Technology, Hokkaido University, Sapporo060–0814, Hokkaido, Japan
| | - Katsumasa Fujita
- Department of Applied Physics, Osaka University, Suita565–0871, Osaka, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita565–0871, Osaka, Japan
- Advanced Photonics and Biosensing Open Innovation Laboratory, AIST-Osaka University, Suita565–0871, Osaka, Japan
| | - Tamiki Komatsuzaki
- Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo001–0020, Hokkaido, Japan
- Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo001–0021, Hokkaido, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita565–0871, Osaka, Japan
- Graduate School of Chemical Sciences and Engineering Materials Chemistry, and Engineering Course, Hokkaido University, Sapporo060–0812, Hokkaido, Japan
- The Institute of Scientific and Industrial Research, Osaka University, Ibaraki567-0047, Osaka, Japan
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5
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He H, Cao M, Gao Y, Zheng P, Yan S, Zhong JH, Wang L, Jin D, Ren B. Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy. Nat Commun 2024; 15:754. [PMID: 38272927 PMCID: PMC10810791 DOI: 10.1038/s41467-024-44864-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 01/05/2024] [Indexed: 01/27/2024] Open
Abstract
The low scattering efficiency of Raman scattering makes it challenging to simultaneously achieve good signal-to-noise ratio (SNR), high imaging speed, and adequate spatial and spectral resolutions. Here, we report a noise learning (NL) approach that estimates the intrinsic noise distribution of each instrument by statistically learning the noise in the pixel-spatial frequency domain. The estimated noise is then removed from the noisy spectra. This enhances the SNR by ca. 10 folds, and suppresses the mean-square error by almost 150 folds. NL allows us to improve the positioning accuracy and spatial resolution and largely eliminates the impact of thermal drift on tip-enhanced Raman spectroscopic nanoimaging. NL is also applicable to enhance SNR in fluorescence and photoluminescence imaging. Our method manages the ground truth spectra and the instrumental noise simultaneously within the training dataset, which bypasses the tedious labelling of huge dataset required in conventional deep learning, potentially shifting deep learning from sample-dependent to instrument-dependent.
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Affiliation(s)
- Hao He
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
- Department of Biomedical Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Maofeng Cao
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Yun Gao
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China
| | - Peng Zheng
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China
| | - Sen Yan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Jin-Hui Zhong
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Lei Wang
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China.
| | - Dayong Jin
- Department of Biomedical Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
- Institute for Biomedical Materials & Devices (IBMD), University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
- Tan Kah Kee Innovation Laboratory, Xiamen, 361104, China.
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6
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Weng S, Zhu R, Wu Y, Wang C, Li P, Zheng L, Liang D, Duan Z. Acceleration of high-quality Raman imaging via a locality enhanced transformer network. Analyst 2023; 148:6282-6291. [PMID: 37971331 DOI: 10.1039/d3an01543b] [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: 11/19/2023]
Abstract
Raman imaging (RI) is an outstanding technique that enables molecular-level medical diagnostics and therapy assessment by providing characteristic fingerprint and morphological information about molecules. However, obtaining high-quality Raman images generally requires a long acquisition time, up to hours, which is prohibitive for RI applications of timely cytopathology and histopathology analyses. To address this issue, image super-resolution (SR) based on deep learning, including convolutional neural networks and transformers, has been widely recognized as an effective solution to reduce the time required for achieving high-quality RI. In this study, a locality enhanced transformer network (LETNet) is proposed to perform Raman image SR. Specifically, the general architecture of the transformer is adopted with the replacement of self-attention by convolution to generate high-fidelity and detailed SR images. Additionally, the convolution in the LETNet is further optimized by utilizing depth-wise convolution to improve the computational efficiency of the model. Experiments on hyperspectral Raman images of breast cancer cells and Raman images of a few channels of brain tumor tissues demonstrate that the LETNet achieves superior 2×, 4×, and 8× SR with fewer parameters compared with other SR methods. Consequently, high-quality Raman images can be obtained with a significant reduction in time, ranging from 4 to 64 times. Overall, the proposed method provides a novel, efficient, and reliable solution to expedite high-quality RI and promote its application in real-time diagnosis and therapy.
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Affiliation(s)
- Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Hefei 230601, China
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, Anhui, China
| | - Rui Zhu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Hefei 230601, China
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, Anhui, China
| | - Yehang Wu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Hefei 230601, China
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, Anhui, China
| | - Cong Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Hefei 230601, China
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, Anhui, China
| | - Pan Li
- Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Ling Zheng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Hefei 230601, China
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, Anhui, China
| | - Dong Liang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Hefei 230601, China
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, Anhui, China
| | - Zhangling Duan
- School of Internet, Anhui University, Hefei 230601, Anhui, China
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7
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Mochizuki K, Kumamoto Y, Maeda S, Tanuma M, Kasai A, Takemura M, Harada Y, Hashimoto H, Tanaka H, Smith NI, Fujita K. High-throughput line-illumination Raman microscopy with multislit detection. BIOMEDICAL OPTICS EXPRESS 2023; 14:1015-1026. [PMID: 36950233 PMCID: PMC10026569 DOI: 10.1364/boe.480611] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/27/2022] [Accepted: 01/02/2023] [Indexed: 06/18/2023]
Abstract
Raman microscopy is an emerging tool for molecular imaging and analysis of living samples. Use of Raman microscopy in life sciences is, however, still limited because of its slow measurement speed for spectral imaging and analysis. We developed a multiline-illumination Raman microscope to achieve ultrafast Raman spectral imaging. A spectrophotometer equipped with a periodic array of confocal slits detects Raman spectra from a sample irradiated by multiple line illuminations. A comb-like Raman hyperspectral image is formed on a two-dimensional detector in the spectrophotometer, and a hyperspectral Raman image is acquired by scanning the sample with multiline illumination array. By irradiating a sample with 21 simultaneous illumination lines, we achieved high-throughput Raman hyperspectral imaging of mouse brain tissue, acquiring 1108800 spectra in 11.4 min. We also measured mouse kidney and liver tissue as well as conducted label-free live-cell molecular imaging. The ultrafast Raman hyperspectral imaging enabled by the presented technique will expand the possible applications of Raman microscopy in biological and medical fields.
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Affiliation(s)
- Kentaro Mochizuki
- Department of Applied Physics, Osaka University, Suita, Osaka 565-0871, Japan
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, Kamigyo-ku, Kyoto 602-8566, Japan
- These authors contributed equally
| | - Yasuaki Kumamoto
- Department of Applied Physics, Osaka University, Suita, Osaka 565-0871, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Osaka 565-0871, Japan
- These authors contributed equally
| | - Shunsuke Maeda
- Department of Applied Physics, Osaka University, Suita, Osaka 565-0871, Japan
| | - Masato Tanuma
- Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, Suita, Osaka 565-0871, Japan
| | - Atsushi Kasai
- Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, Suita, Osaka 565-0871, Japan
| | - Masashi Takemura
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, Kamigyo-ku, Kyoto 602-8566, Japan
| | - Yoshinori Harada
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, Kamigyo-ku, Kyoto 602-8566, Japan
| | - Hitoshi Hashimoto
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Osaka 565-0871, Japan
- Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, Suita, Osaka 565-0871, Japan
- Molecular Research Center for Children’s Mental Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Suita, Osaka 565-0871, Japan
- Institute for Datability Science, Osaka University, Suita, Osaka 565-0871, Japan
- Department of Molecular Pharmaceutical Sciences, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan
| | - Hideo Tanaka
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, Kamigyo-ku, Kyoto 602-8566, Japan
| | - Nicholas Isaac Smith
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Osaka 565-0871, Japan
- Biophotonics Laboratory, Immunology Frontier Research Center, Osaka University, Suita, Osaka 565-0871, Japan
| | - Katsumasa Fujita
- Department of Applied Physics, Osaka University, Suita, Osaka 565-0871, Japan
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, Kamigyo-ku, Kyoto 602-8566, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Osaka 565-0871, Japan
- Advanced Photonics and Biosensing Open Innovation Laboratory, AIST-Osaka University, Osaka University, Suita, Osaka 565-0871, Japan
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8
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Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging. BIOSENSORS 2022; 12:bios12040250. [PMID: 35448310 PMCID: PMC9031282 DOI: 10.3390/bios12040250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/10/2022] [Accepted: 04/12/2022] [Indexed: 11/17/2022]
Abstract
This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information–nucleic acids, proteins, and lipids—from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation.
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9
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Zheng P, He H, Gao Y, Tang P, Wang H, Peng J, Wang L, Su C, Ding S. Speeding up the Topography Imaging of Atomic Force Microscopy by Convolutional Neural Network. Anal Chem 2022; 94:5041-5047. [PMID: 35294191 DOI: 10.1021/acs.analchem.1c05056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Atomic force microscopy (AFM) provides unprecedented insight into surface topography research with ultrahigh spatial resolution at the subnanometer level. However, a slow scanning rate has to be employed to ensure the image quality, which will largely increase the accumulated sample drift, thereby, resulting in the low fidelity of the AFM image. In this paper, we propose a fast imaging method which performs a complete fast Raster scanning and a slow μ-path subsampling together with a deep learning algorithm to rapidly produce an AFM image with high quality and small drift. A supervised convolutional neural network (CNN) model is trained with the slow μ-path subsampled data and its counterpart acquired with fast Raster scan. The fast speed acquired AFM image is then inputted to the well-trained CNN model to output the high quality one. We validate the reliability of this method using a silicon grids sample and further apply it to the fast imaging of a vanadium dioxide thin film. The results demonstrate that this method can largely improve the imaging speed up to 10.3 times with state-of-the-art imaging quality, and reduce the sample drift by 8.9 times in the multiframe AFM imaging of the same area. Furthermore, we prove that this method is also applicable to other scanning imaging techniques such as scanning electrochemical microscopy.
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Affiliation(s)
- Peng Zheng
- School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
| | - Hao He
- School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
| | - Yun Gao
- School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
| | - Peiwen Tang
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.,School of Chemistry and Chemical Engineering, Ningxia University, Ningxia 750021, China
| | - Hailong Wang
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Juan Peng
- School of Chemistry and Chemical Engineering, Ningxia University, Ningxia 750021, China
| | - Lei Wang
- School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
| | - Chanmin Su
- Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
| | - Songyuan Ding
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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10
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Song MK, Ma YP, Liu H, Hu PP, Huang CZ, Zhou J. High Resolution of Plasmonic Resonance Scattering Imaging with Deep Learning. Anal Chem 2022; 94:4610-4616. [PMID: 35275492 DOI: 10.1021/acs.analchem.1c04330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The dark-field microscopy (DFM) imaging technology has the advantage of a high signal-to-noise ratio, and it is often used for real-time monitoring of plasmonic resonance scattering and biological imaging at the single-nanoparticle level. Due to the limitation of the optical diffraction limit, it is still a challenging task to accurately distinguish two or more nanoparticles whose distance is less than the diffraction limit. Here, we propose a computational strategy based on a deep learning framework (NanoNet), which will realize the effective segmentation of the scattered light spots in diffraction-limited DFM images and obtain high-resolution plasmonic light scattering imaging. A small data set of DFM and the corresponding scanning electron microscopy (SEM) image pairs are used to learn for obtaining a highly resolved semantic imaging model using NanoNet, and thus highly resolved DFM images matching the resolution of those acquired using SEM can be obtained. Our method has the ability to transform diffraction-limited DFM images to highly resolved ones without adding a complex optical system. As a proof of concept, a highly resolved DFM image of living cells through the NanoNet technique is successfully made, opening up a new avenue for high-resolution optical nanoscopic imaging.
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Affiliation(s)
- Ming Ke Song
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China
| | - Yun Peng Ma
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China
| | - Hui Liu
- Key Laboratory of Luminescent and Real-Time Analytical System (Southwest University), Chongqing Science and Technology Bureau, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, P. R. China
| | - Ping Ping Hu
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, P. R. China
| | - Cheng Zhi Huang
- Key Laboratory of Luminescent and Real-Time Analytical System (Southwest University), Chongqing Science and Technology Bureau, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, P. R. China
| | - Jun Zhou
- Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China.,Key Laboratory of Luminescent and Real-Time Analytical System (Southwest University), Chongqing Science and Technology Bureau, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, P. R. China
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11
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Qi Y, Yang L, Liu B, Liu L, Liu Y, Zheng Q, Liu D, Luo J. Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 265:120400. [PMID: 34547683 DOI: 10.1016/j.saa.2021.120400] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/07/2021] [Accepted: 09/10/2021] [Indexed: 06/13/2023]
Abstract
Intraoperative detection of the marginal tissues is the last and most important step to complete the resection of adenocarcinoma and squamous cell carcinoma. However, the current intraoperative diagnosis is time-consuming and requires numerous steps including staining. In this paper, we present the use of Raman spectroscopy with deep learning to achieve accurate diagnosis with stain-free process. To make the spectrum more suitable for deep learning, we utilize an unusual way of thinking which regards Raman spectral signal as a sequence and then converts it into two-dimensional Raman spectrogram by short-time Fourier transform as input. The normal-adenocarcinoma deep learning model and normal-squamous carcinoma deep learning model both achieve more than 96% accuracy, 95% sensitivity and 98% specificity when test, which higher than the conventional principal components analysis-linear discriminant analysis method with normal-adenocarcinoma model (0.896 accuracy, 0.867 sensitivity, 0.926 specificity) and normal-squamous carcinoma model (0.821 accuracy, 0.776 sensitivity, 1.000 specificity). The high performance of deep learning models provides a reliable way for intraoperative detection of marginal tissue, and is expected to reduce the detection time and save human lives.
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Affiliation(s)
- Yafeng Qi
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Lin Yang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bangxu Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Li Liu
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yuhong Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Qingfeng Zheng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Dameng Liu
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China.
| | - Jianbin Luo
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
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12
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Horgan CC, Jensen M, Nagelkerke A, St-Pierre JP, Vercauteren T, Stevens MM, Bergholt MS. High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy. Anal Chem 2021; 93:15850-15860. [PMID: 34797972 PMCID: PMC9286315 DOI: 10.1021/acs.analchem.1c02178] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
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Raman spectroscopy
enables nondestructive, label-free imaging with
unprecedented molecular contrast, but is limited by slow data acquisition,
largely preventing high-throughput imaging applications. Here, we
present a comprehensive framework for higher-throughput molecular
imaging via deep-learning-enabled Raman spectroscopy, termed DeepeR,
trained on a large data set of hyperspectral Raman images, with over
1.5 million spectra (400 h of acquisition) in total. We first perform
denoising and reconstruction of low signal-to-noise ratio Raman molecular
signatures via deep learning, with a 10× improvement in the mean-squared
error over common Raman filtering methods. Next, we develop a neural
network for robust 2–4× spatial super-resolution of hyperspectral
Raman images that preserve molecular cellular information. Combining
these approaches, we achieve Raman imaging speed-ups of up to 40–90×,
enabling good-quality cellular imaging with a high-resolution, high
signal-to-noise ratio in under 1 min. We further demonstrate Raman
imaging speed-up of 160×, useful for lower resolution imaging
applications such as the rapid screening of large areas or for spectral
pathology. Finally, transfer learning is applied to extend DeepeR
from cell to tissue-scale imaging. DeepeR provides a foundation that
will enable a host of higher-throughput Raman spectroscopy and molecular
imaging applications across biomedicine.
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Affiliation(s)
- Conor C Horgan
- Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, U.K.,Department of Materials, Department of Bioengineering, and Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, U.K
| | - Magnus Jensen
- Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, U.K
| | - Anika Nagelkerke
- Groningen Research Institute of Pharmacy, Pharmaceutical Analysis, University of Groningen, P.O. Box 196, XB20, Groningen 9700 AD, The Netherlands.,Department of Materials, Department of Bioengineering, and Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, U.K
| | - Jean-Philippe St-Pierre
- Department of Chemical and Biological Engineering, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada.,Department of Materials, Department of Bioengineering, and Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, U.K
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences, King's College London, London WC2R 2LS, U.K
| | - Molly M Stevens
- Department of Materials, Department of Bioengineering, and Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, U.K
| | - Mads S Bergholt
- Centre for Craniofacial and Regenerative Biology, King's College London, London SE1 9RT, U.K
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13
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14
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Bakthavatsalam S, Dodo K, Sodeoka M. A decade of alkyne-tag Raman imaging (ATRI): applications in biological systems. RSC Chem Biol 2021; 2:1415-1429. [PMID: 34704046 PMCID: PMC8496067 DOI: 10.1039/d1cb00116g] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022] Open
Abstract
Alkyne functional groups have Raman signatures in a region (1800 cm-1 to 2800 cm-1) that is free from interference from cell components, known as the "silent region", and alkyne signals in this region were first utilized a decade ago to visualize the nuclear localization of a thymidine analogue EdU. Since then, the strategy of Raman imaging of biological samples by using alkyne functional groups, called alkyne-tag Raman imaging (ATRI), has become widely used. This article reviews the applications of ATRI in biological samples ranging from organelles to whole animal models, and briefly discusses the prospects for this technique.
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Affiliation(s)
- Subha Bakthavatsalam
- Synthetic Organic Chemistry Laboratory, RIKEN Cluster for Pioneering Research Wako Saitama 351-0198 Japan
| | - Kosuke Dodo
- Synthetic Organic Chemistry Laboratory, RIKEN Cluster for Pioneering Research Wako Saitama 351-0198 Japan
- RIKEN Center for Sustainable Resource Science 2-1 Hirosawa Wako Saitama 351-0198 Japan
| | - Mikiko Sodeoka
- Synthetic Organic Chemistry Laboratory, RIKEN Cluster for Pioneering Research Wako Saitama 351-0198 Japan
- RIKEN Center for Sustainable Resource Science 2-1 Hirosawa Wako Saitama 351-0198 Japan
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15
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Yu S, Li X, Lu W, Li H, Fu YV, Liu F. Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens. Anal Chem 2021; 93:11089-11098. [PMID: 34339167 DOI: 10.1021/acs.analchem.1c00431] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, we have proposed two new methods that involve Raman spectroscopy combined with a long short-term memory (LSTM) neural network and compared them with a method using a normal convolutional neural network (CNN). We used eight strains isolated from the marine organism Urechis unicinctus, including four kinds of pathogens. After the models were configured and trained, the LSTM methods that we proposed achieved average isolation-level accuracies exceeding 94%, not only meeting the requirement for identification but also indicating that the proposed methods were faster and more accurate than the normal CNN models. Finally, through a computational approach, we designed a loss function to explore the mechanism reflected by the Raman data, finding the Raman segments that most likely exhibited the characteristics of nucleic acids. These novel experimental results provide insights for developing additional deep learning methods to accurately analyze complex Raman data.
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Affiliation(s)
- Shixiang Yu
- Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Xin Li
- Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Weilai Lu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Hanfei Li
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Yu Vincent Fu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Fanghua Liu
- Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China.,National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, P. R. China
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16
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Taking the leap between analytical chemistry and artificial intelligence: A tutorial review. Anal Chim Acta 2021; 1161:338403. [DOI: 10.1016/j.aca.2021.338403] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 01/01/2023]
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17
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Hu C, Wang X, Liu L, Fu C, Chu K, Smith ZJ. Fast confocal Raman imaging via context-aware compressive sensing. Analyst 2021; 146:2348-2357. [PMID: 33624650 DOI: 10.1039/d1an00088h] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Raman hyperspectral imaging is a powerful method to obtain detailed chemical information about a wide variety of organic and inorganic samples noninvasively and without labels. However, due to the weak, nonresonant nature of spontaneous Raman scattering, acquiring a Raman imaging dataset is time-consuming and inefficient. In this paper we utilize a compressive imaging strategy coupled with a context-aware image prior to improve Raman imaging speed by 5- to 10-fold compared to classic point-scanning Raman imaging, while maintaining the traditional benefits of point scanning imaging, such as isotropic resolution and confocality. With faster data acquisition, large datasets can be acquired in reasonable timescales, leading to more reliable downstream analysis. On standard samples, context-aware Raman compressive imaging (CARCI) was able to reduce the number of measurements by ∼85% while maintaining high image quality (SSIM >0.85). Using CARCI, we obtained a large dataset of chemical images of fission yeast cells, showing that by collecting 5-fold more cells in a given experiment time, we were able to get more accurate chemical images, identification of rare cells, and improved biochemical modeling. For example, applying VCA to nearly 100 cells' data together, cellular organelles were resolved that were not faithfully reconstructed by a single cell's dataset.
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Affiliation(s)
- Chuanzhen Hu
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, China.
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18
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Liu Y, Zhou S, Han W, Li C, Liu W, Qiu Z, Chen H. Detection of Adulteration in Infant Formula Based on Ensemble Convolutional Neural Network and Near-Infrared Spectroscopy. Foods 2021; 10:foods10040785. [PMID: 33917308 PMCID: PMC8067368 DOI: 10.3390/foods10040785] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 03/17/2021] [Accepted: 03/22/2021] [Indexed: 11/16/2022] Open
Abstract
Adulteration in dairy products has received world-wide attention, and at the same time, near infrared (NIR) spectroscopy has proven to be a promising tool for adulteration detection given its advantages of real-time response and non-destructive analysis. Regardless, the accurate and robust NIR model for adulteration detection is hard to achieve in practice. Convolutional neural network (CNN), as a promising deep learning architecture, is difficult to apply to such chemometrics tasks due to the high risk of overfitting, despite the breakthroughs it has made in other fields. In this paper, the ensemble learning method based on CNN estimators was developed to address the overfitting and random initialization problems of CNN and applied to the determination of two infant formula adulterants, namely hydrolyzed leather protein (HLP) and melamine. Moreover, a probabilistic wavelength selection method based on the attention mechanism was proposed for the purpose of finding the best trade-off between the accuracy and the diversity of the sub-models in ensemble learning. The overall results demonstrate that the proposed method yielded superiority regression performance over the comparison methods for both studied data sets, and determination coefficients (R2) of 0.961 and 0.995 were obtained for the HLP and the melamine data sets, respectively.
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19
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He H, Yan S, Lyu D, Xu M, Ye R, Zheng P, Lu X, Wang L, Ren B. Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives. Anal Chem 2021; 93:3653-3665. [PMID: 33599125 DOI: 10.1021/acs.analchem.0c04671] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics method. Deep learning, which utilizes very large data sets for finding hidden features therein and for making accurate predictions for a wide range of applications, has been applied in an unbelievable pace in biospectroscopy and biospectral imaging in the recent 3 years. In this Feature, we first introduce the background and basic knowledge of deep learning. We then focus on the emerging applications of deep learning in the data preprocessing, feature detection, and modeling of the biological samples for spectral analysis and spectroscopic imaging. Finally, we highlight the challenges and limitations in deep learning and the outlook for future directions.
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Affiliation(s)
- Hao He
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Sen Yan
- 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
| | - Danya Lyu
- 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
| | - Mengxi Xu
- 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
| | - Ruiqian Ye
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Peng Zheng
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, China
| | - Xinyu 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
| | - Lei Wang
- School of Aerospace Engineering, Xiamen University, Xiamen, 361000, 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
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20
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Kim K, Kashefi-Kheyrabadi L, Joung Y, Kim K, Dang H, Chavan SG, Lee MH, Choo J. Recent advances in sensitive surface-enhanced Raman scattering-based lateral flow assay platforms for point-of-care diagnostics of infectious diseases. SENSORS AND ACTUATORS. B, CHEMICAL 2021; 329:129214. [PMID: 36568647 PMCID: PMC9759493 DOI: 10.1016/j.snb.2020.129214] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 11/03/2020] [Accepted: 11/05/2020] [Indexed: 05/03/2023]
Abstract
This review reports the recent advances in surface-enhanced Raman scattering (SERS)-based lateral flow assay (LFA) platforms for the diagnosis of infectious diseases. As observed through the recent infection outbreaks of COVID-19 worldwide, a timely diagnosis of the disease is critical for preventing the spread of a disease and to ensure epidemic preparedness. In this regard, an innovative point-of-care diagnostic method is essential. Recently, SERS-based assay platforms have received increasing attention in medical communities owing to their high sensitivity and multiplex detection capability. In contrast, LFAs provide a user-friendly and easily accessible sensing platform. Thus, the combination of LFAs with a SERS detection system provides a new diagnostic modality for accurate and rapid diagnoses of infectious diseases. In this context, we briefly discuss the recent application of LFA platforms for the POC diagnosis of SARS-CoV-2. Thereafter, we focus on the recent advances in SERS-based LFA platforms for the early diagnosis of infectious diseases and their applicability for the rapid diagnosis of SARS-CoV-2. Finally, the key issues that need to be addressed to accelerate the clinical translation of SERS-based LFA platforms from the research laboratory to the bedside are discussed.
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Key Words
- AuNPs, gold nanoparticles
- BA, bacillary angiomatosis
- CRISPR, Clustered Regularly Interspaced Short Palindromic Repeat
- HIV, human immunodeficiency virus
- IFA, indirect immunofluorescence assay
- IgG, immunoglobulin G
- IgM, immunoglobulin M
- In vitro diagnostics (IVD)
- Infectious disease
- KSHV, Kaposi’s sarcoma herpes virus
- LFA, lateral flow assay
- Lateral flow assay (LFA)
- NC, nitrocellulose
- NS1, nonstructural protein 1
- POC, point-of-care
- PRV, pseudorabies virus
- Point-of-care (POC)
- RT-PCR, real-time polymerase chain reaction
- SARS-CoV-2
- SARS-CoV-2, severe acute respiratory syndrome-coronavirus-2
- SEB, staphylococcal enterotoxin
- SERS, surface-enhanced Raman scattering
- Si-AuNPs, silica-encapsulated AuNPs
- Surface-enhanced Raman scattering (SERS)
- crRNAs, CRISPR RNAs
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Affiliation(s)
- Kihyun Kim
- Department of Chemistry, Chung-Ang University, Seoul, 06974, South Korea
| | | | - Younju Joung
- Department of Chemistry, Chung-Ang University, Seoul, 06974, South Korea
| | - Kyeongnyeon Kim
- Department of Chemistry, Chung-Ang University, Seoul, 06974, South Korea
| | - Hajun Dang
- Department of Chemistry, Chung-Ang University, Seoul, 06974, South Korea
| | - Sachin Ganpat Chavan
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, South Korea
| | - Min-Ho Lee
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, South Korea
| | - Jaebum Choo
- Department of Chemistry, Chung-Ang University, Seoul, 06974, South Korea
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21
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Song MK, Chen SX, Hu PP, Huang CZ, Zhou J. Automated Plasmonic Resonance Scattering Imaging Analysis via Deep Learning. Anal Chem 2021; 93:2619-2626. [PMID: 33427440 DOI: 10.1021/acs.analchem.0c04763] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Plasmonic nanoparticles, which have excellent local surface plasmon resonance (LSPR) optical and chemical properties, have been widely used in biology, chemistry, and photonics. The single-particle light scattering dark-field microscopy (DFM) imaging technique based on a color-coded analytical method is a promising approach for high-throughput plasmonic nanoparticle scatterometry. Due to the interference of high noise levels, accurately extracting real scattering light of plasmonic nanoparticles in living cells is still a challenging task, which hinders its application for intracellular analysis. Herein, we propose an automatic and high-throughput LSPR scatterometry technique using a U-Net convolutional deep learning neural network. We use the deep neural networks to recognize the scattering light of nanoparticles from background interference signals in living cells, which have a dynamic and complicated environment, and construct a DFM image semantic analytical model based on the U-Net convolutional neural network. Compared with traditional methods, this method can achieve higher accuracy, stronger generalization ability, and robustness. As a proof of concept, the change of intracellular cytochrome c in MCF-7 cells under UV light-induced apoptosis was monitored through the fast and high-throughput analysis of the plasmonic nanoparticle scattering light, providing a new strategy for scatterometry study and imaging analysis in chemistry.
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Affiliation(s)
- Ming Ke Song
- A Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China
| | - Shan Xiong Chen
- A Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China
| | - Ping Ping Hu
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, P. R. China
| | - Cheng Zhi Huang
- Key Laboratory of Luminescent and Real-Time Analytical System (Southwest University), Chongqing Science and Technology Bureau, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, P. R. China
| | - Jun Zhou
- A Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University), Ministry of Education, College of Computer and Information Science, Southwest University, Chongqing 400715, P. R. China.,Key Laboratory of Luminescent and Real-Time Analytical System (Southwest University), Chongqing Science and Technology Bureau, College of Pharmaceutical Sciences, Southwest University, Chongqing 400715, P. R. China
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22
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Tabish TA, Dey P, Mosca S, Salimi M, Palombo F, Matousek P, Stone N. Smart Gold Nanostructures for Light Mediated Cancer Theranostics: Combining Optical Diagnostics with Photothermal Therapy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2020; 7:1903441. [PMID: 32775148 PMCID: PMC7404179 DOI: 10.1002/advs.201903441] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 03/24/2020] [Indexed: 05/13/2023]
Abstract
Nanotheranostics, which combines optical multiplexed disease detection with therapeutic monitoring in a single modality, has the potential to propel the field of nanomedicine toward genuine personalized medicine. Currently employed mainstream modalities using gold nanoparticles (AuNPs) in diagnosis and treatment are limited by a lack of specificity and potential issues associated with systemic toxicity. Light-mediated nanotheranostics offers a relatively non-invasive alternative for cancer diagnosis and treatment by using AuNPs of specific shapes and sizes that absorb near infrared (NIR) light, inducing plasmon resonance for enhanced tumor detection and generating localized heat for tumor ablation. Over the last decade, significant progress has been made in the field of nanotheranostics, however the main biological and translational barriers to nanotheranostics leading to a new paradigm in anti-cancer nanomedicine stem from the molecular complexities of cancer and an incomplete mechanistic understanding of utilization of Au-NPs in living systems. This work provides a comprehensive overview on the biological, physical and translational barriers facing the development of nanotheranostics. It will also summarise the recent advances in engineering specific AuNPs, their unique characteristics and, importantly, tunability to achieve the desired optical/photothermal properties.
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Affiliation(s)
| | - Priyanka Dey
- School of Physics and AstronomyUniversity of ExeterExeterEX4 4QLUK
| | - Sara Mosca
- Central Laser FacilitySTFC Rutherford Appleton LaboratoryOxfordOX11 0QXUK
| | - Marzieh Salimi
- School of Physics and AstronomyUniversity of ExeterExeterEX4 4QLUK
| | | | - Pavel Matousek
- Central Laser FacilitySTFC Rutherford Appleton LaboratoryOxfordOX11 0QXUK
| | - Nicholas Stone
- School of Physics and AstronomyUniversity of ExeterExeterEX4 4QLUK
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23
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Guo S, Mayerhöfer T, Pahlow S, Hübner U, Popp J, Bocklitz T. Deep learning for 'artefact' removal in infrared spectroscopy. Analyst 2020; 145:5213-5220. [PMID: 32579623 DOI: 10.1039/d0an00917b] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
It has been well recognized that infrared spectra of microscopically heterogeneous media do not merely reflect the absorption of the sample but are influenced also by geometric factors and the wave nature of light causing scattering, reflection, interference, etc. These phenomena often occur simultaneously in complex samples like tissues and manifest themselves as intense baseline profiles, fringes, band distortion and band intensity changes in a measured IR spectrum. The information on the molecular level contained in IR spectra is thus entangled with the geometric structure of a sample and the optical model behind it, which largely hinders the data interpretation and in many cases renders the Beer-Lambert law invalid. It is required to recover the pure absorption (i.e., absorbance) of the sample from the measurement (i.e., apparent absorbance), that is, to remove the 'artefacts' caused merely by optical influences. To do so, we propose an artefact removal approach based on a deep convolutional neural network (CNN), specifically a 1-dimensional U-shape convolutional neural network (1D U-Net), and based our study on poly(methyl methacrylate) (PMMA) as materials. To start, a simulated dataset composed of apparent absorbance and absorbance pairs was generated according to the Mie-theory for PMMA spheres. After a data augmentation procedure, this dataset was utilized to train the 1D U-Net aiming to transform the input apparent absorbance into the corrected absorbance. The performance of the artefact removal was evaluated by the hit-quality-index (HQI) between the corrected and the true absorbance. Based on the prediction and the HQI of two experimental and one simulated independent testing datasets, we could demonstrate that the network was able to retrieve the absorbance very well, even in cases where the absorbance is completely overwhelmed by extremely large 'artefacts'. As the testing datasets bear different patterns of absorbance and 'artefacts' to the training data, the promising correction also indicated a good generalization performance of the 1D U-Net. Finally, the reliability and computational mechanism of the trained network were illustrated via two interpretation approaches including a direct visualization of layer-wise outputs as well as a saliency-based method.
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Affiliation(s)
- Shuxia Guo
- Leibniz Institute of Photonic Technology Jena (IPHT Jena), Member of Leibniz Health Technologies, 07745 Jena, Germany.
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24
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Pradhan P, Guo S, Ryabchykov O, Popp J, Bocklitz TW. Deep learning a boon for biophotonics? JOURNAL OF BIOPHOTONICS 2020; 13:e201960186. [PMID: 32167235 DOI: 10.1002/jbio.201960186] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 02/22/2020] [Accepted: 03/10/2020] [Indexed: 06/10/2023]
Abstract
This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.
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Affiliation(s)
- Pranita Pradhan
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
| | - Shuxia Guo
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
| | - Oleg Ryabchykov
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
| | - Juergen Popp
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
| | - Thomas W Bocklitz
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany
- Leibniz Institute of Photonic Technology (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
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25
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Pérez-Jiménez AI, Lyu D, Lu Z, Liu G, Ren B. Surface-enhanced Raman spectroscopy: benefits, trade-offs and future developments. Chem Sci 2020; 11:4563-4577. [PMID: 34122914 PMCID: PMC8159237 DOI: 10.1039/d0sc00809e] [Citation(s) in RCA: 276] [Impact Index Per Article: 69.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopy technique with sensitivity down to the single molecule level that provides fine molecular fingerprints, allowing for direct identification of target analytes. Extensive theoretical and experimental research, together with continuous development of nanotechnology, has significantly broadened the scope of SERS and made it a hot research field in chemistry, physics, materials, biomedicine, and so on. However, SERS has not been developed into a routine analytical technique, and continuous efforts have been made to address the problems preventing its real-world application. The present minireview focuses on analyzing current and potential strategies to tackle problems and realize the SERS performance necessary for translation to practical applications.
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Affiliation(s)
- Ana Isabel Pérez-Jiménez
- 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
| | - Danya Lyu
- 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
| | - Zhixuan 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
| | - Guokun 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 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 P. R. China
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Lin H, Luo Y, Sun Q, Deng K, Chen Y, Wang Z, Huang P. Determination of causes of death via spectrochemical analysis of forensic autopsies-based pulmonary edema fluid samples with deep learning algorithm. JOURNAL OF BIOPHOTONICS 2020; 13:e201960144. [PMID: 31957147 DOI: 10.1002/jbio.201960144] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 12/22/2019] [Accepted: 01/15/2020] [Indexed: 06/10/2023]
Abstract
This study investigated whether infrared spectroscopy combined with a deep learning algorithm could be a useful tool for determining causes of death by analyzing pulmonary edema fluid from forensic autopsies. A newly designed convolutional neural network-based deep learning framework, named DeepIR and eight popular machine learning algorithms, were used to construct classifiers. The prediction performances of these classifiers demonstrated that DeepIR outperformed the machine learning algorithms in establishing classifiers to determine the causes of death. Moreover, DeepIR was generally less dependent on preprocessing procedures than were the machine learning algorithms; it provided the validation accuracy with a narrow range from 0.9661 to 0.9856 and the test accuracy ranging from 0.8774 to 0.9167 on the raw pulmonary edema fluid spectral dataset and the nine preprocessing protocol-based datasets in our study. In conclusion, this study demonstrates that the deep learning-equipped Fourier transform infrared spectroscopy technique has the potential to be an effective aid for determining causes of death.
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Affiliation(s)
- Hancheng Lin
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
- Department of Forensic Pathology, Xi'an Jiaotong University, Xi'an, China
| | - Yiwen Luo
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Qiran Sun
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Kaifei Deng
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Yijiu Chen
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Zhenyuan Wang
- Department of Forensic Pathology, Xi'an Jiaotong University, Xi'an, China
| | - Ping Huang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
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