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Rodrigues J, Amin A, Chandra S, Mulla NJ, Nayak GS, Rai S, Ray S, Mahato KK. Machine Learning Enabled Photoacoustic Spectroscopy for Noninvasive Assessment of Breast Tumor Progression In Vivo: A Preclinical Study. ACS Sens 2024; 9:589-601. [PMID: 38288735 PMCID: PMC10897932 DOI: 10.1021/acssensors.3c01085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 11/25/2023] [Accepted: 01/17/2024] [Indexed: 02/24/2024]
Abstract
Breast cancer is a dreaded disease affecting women the most in cancer-related deaths over other cancers. However, early diagnosis of the disease can help increase survival rates. The existing breast cancer diagnosis tools do not support the early diagnosis of the disease. Therefore, there is a great need to develop early diagnostic tools for this cancer. Photoacoustic spectroscopy (PAS), being very sensitive to biochemical changes, can be relied upon for its application in detecting breast tumors in vivo. With this motivation, in the current study, an aseptic chamber integrated photoacoustic (PA) probe was designed and developed to monitor breast tumor progression in vivo, established in nude mice. The device served the dual purpose of transporting tumor-bearing animals to the laboratory from the animal house and performing PA experiments in the same chamber, maintaining sterility. In the current study, breast tumor was induced in the nude mice by MCF-7 cells injection and the corresponding PA spectra at different time points (day 0, 5, 10, 15, and 20) of tumor progression in vivo in the same animals. The recorded photoacoustic spectra were subsequently preprocessed, wavelet-transformed, and subjected to filter-based feature selection algorithm. The selected top 20 features, by minimum redundancy maximum relevance (mRMR) algorithm, were then used to build an input feature matrix for machine learning (ML)-based classification of the data. The performance of classification models demonstrated 100% specificity, whereas the sensitivity of 95, 100, 92.5, and 85% for the time points, day 5, 10, 15, and 20, respectively. These results suggest the potential of PA signal-based classification of breast tumor progression in a preclinical model. The PA signal contains information on the biochemical changes associated with disease progression, emphasizing its translational strength toward early disease diagnosis.
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Affiliation(s)
- Jackson Rodrigues
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Ashwini Amin
- Department
of Computer Science and Engineering, Manipal
Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Subhash Chandra
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Nitufa J. Mulla
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - G. Subramanya Nayak
- Department
of Electronics and Communication, Manipal
Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Sharada Rai
- Department
of Pathology, Kasturba Medical College Mangalore,
Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Satadru Ray
- Department
of Surgery, Kasturba Medical College, Manipal
Academy of Higher Education, Karnataka,Manipal 576104, India
| | - Krishna Kishore Mahato
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
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Chikkanayakanahalli Mukunda D, Rodrigues J, Chandra S, Mazumder N, Vitkin A, Kishore Mahato K. Protein classification by autofluorescence spectral shape analysis using machine learning. Talanta 2024; 267:125167. [PMID: 37714041 DOI: 10.1016/j.talanta.2023.125167] [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: 05/27/2023] [Revised: 08/23/2023] [Accepted: 09/04/2023] [Indexed: 09/17/2023]
Abstract
Depending on the relative numbers and spatial arrangement of Tryptophan (Trp; W) and Tyrosine (Tyr; Y) residues, different proteins produce distinct autofluorescence (AF) spectral shapes when excited at ∼280 nm. Yet, considering the vast number and heterogeneous forms in nature, visual analysis and precise identification of proteins based on their AF spectra is challenging and further compounded in cases when different proteins produce substantially similar AF spectral shapes. There is, thus, a serious need to develop a methodology to address this problem. The current study proposes a practical technology to quickly identify proteins using machine learning (ML) algorithms based on their AF spectra. Specifically, AF spectra of fifteen different standard proteins of varying origin with distinct structural and Trp/Tyr compositions were recorded; based on the spectral features selected by the Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm, a multiclass Support Vector Machine (SVM) learning model with Radial Basis Function (RBF), Polynomial, and Linear kernels classified the proteins with high accuracy of 99.06%, 99.03%, and 98.29% respectively. Since protein identification is the key to understand biological functions and disease diagnosis, the proposed methodology could offer a viable alternative to and improve the existing protein identification techniques.
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Affiliation(s)
| | - Jackson Rodrigues
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India
| | - Subhash Chandra
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India
| | - Nirmal Mazumder
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India
| | - Alex Vitkin
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5G 1L7, Canada
| | - Krishna Kishore Mahato
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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Zhang M, Wen L, Zhou C, Pan J, Wu S, Wang P, Zhang H, Chen P, Chen Q, Wang X, Cheng Q. Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:065004. [PMID: 37325191 PMCID: PMC10261702 DOI: 10.1117/1.jbo.28.6.065004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/19/2023] [Accepted: 05/22/2023] [Indexed: 06/17/2023]
Abstract
Significance Collagen and lipid are important components of tumor microenvironments (TME) and participates in tumor development and invasion. It has been reported that collagen and lipid can be used as a hallmark to diagnosis and differentiate tumors. Aim We aim to introduce photoacoustic spectral analysis (PASA) method that can provide both the content and structure distribution of endogenous chromophores in biological tissues to characterize the tumor-related features for identifying different types of tumors. Approach Ex vivo human tissues with suspected squamous cell carcinoma (SCC), suspected basal cell carcinoma (BCC), and normal tissue were used in this study. The relative lipid and collagen contents in the TME were assessed based on the PASA parameters and compared with histology. Support vector machine (SVM), one of the simplest machine learning tools, was applied for automatic skin cancer type detection. Results The PASA results showed that the lipid and collagen levels of the tumors were significantly lower than those of the normal tissue, and there was a statistical difference between SCC and BCC (p < 0.05 ), consistent with the histopathological results. The SVM-based categorization achieved diagnostic accuracies of 91.7% (normal), 93.3% (SCC), and 91.7% (BCC). Conclusions We verified the potential use of collagen and lipid in the TME as biomarkers of tumor diversity and achieved accurate tumor classification based on the collagen and lipid content using PASA. The proposed method provides a new way to diagnose tumors.
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Affiliation(s)
- Mengjiao Zhang
- Tongji University, Institute of Acoustics, School of Physics Science and Engineering, Shanghai, China
| | - Long Wen
- Tongji University, Institute of Photomedicine, Shanghai Skin Disease Hospital, School of Medicine, Shanghai, China
| | - Chu Zhou
- Tongji University, Institute of Photomedicine, Shanghai Skin Disease Hospital, School of Medicine, Shanghai, China
| | - Jing Pan
- Tongji University, Institute of Acoustics, School of Physics Science and Engineering, Shanghai, China
| | - Shiying Wu
- Tongji University, Institute of Acoustics, School of Physics Science and Engineering, Shanghai, China
| | - Peiru Wang
- Tongji University, Institute of Photomedicine, Shanghai Skin Disease Hospital, School of Medicine, Shanghai, China
| | - Haonan Zhang
- Tongji University, Institute of Acoustics, School of Physics Science and Engineering, Shanghai, China
- Tongji University, Institute of Photomedicine, Shanghai Skin Disease Hospital, School of Medicine, Shanghai, China
| | - Panpan Chen
- Tongji University, Institute of Acoustics, School of Physics Science and Engineering, Shanghai, China
| | - Qi Chen
- Tongji University, Institute of Photomedicine, Shanghai Skin Disease Hospital, School of Medicine, Shanghai, China
| | - Xiuli Wang
- Tongji University, Institute of Photomedicine, Shanghai Skin Disease Hospital, School of Medicine, Shanghai, China
| | - Qian Cheng
- Tongji University, Institute of Acoustics, School of Physics Science and Engineering, Shanghai, China
- National Key Laboratory of Autonomous Intelligent Unmanned Systems, Shanghai, China
- Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, China
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Application of data augmentation techniques towards metabolomics. Comput Biol Med 2022; 148:105916. [DOI: 10.1016/j.compbiomed.2022.105916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/11/2022] [Accepted: 07/23/2022] [Indexed: 11/22/2022]
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Jin Y, Yin Y, Li C, Liu H, Shi J. Non-Invasive Monitoring of Human Health by Photoacoustic Spectroscopy. SENSORS 2022; 22:s22031155. [PMID: 35161900 PMCID: PMC8839463 DOI: 10.3390/s22031155] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/27/2022] [Accepted: 01/27/2022] [Indexed: 12/24/2022]
Abstract
For certain diseases, the continuous long-term monitoring of the physiological condition is crucial. Therefore, non-invasive monitoring methods have attracted widespread attention in health care. This review aims to discuss the non-invasive monitoring technologies for human health based on photoacoustic spectroscopy. First, the theoretical basis of photoacoustic spectroscopy and related devices are reported. Furthermore, this article introduces the monitoring methods for blood glucose, blood oxygen, lipid, and tumors, including differential continuous-wave photoacoustic spectroscopy, microscopic photoacoustic spectroscopy, mid-infrared photoacoustic detection, wavelength-modulated differential photoacoustic spectroscopy, and others. Finally, we present the limitations and prospects of photoacoustic spectroscopy.
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Affiliation(s)
- Yongyong Jin
- College of Automation, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China;
- Zhejiang Lab, Hangzhou 311121, Zhejiang, China; (Y.Y.); (C.L.)
| | - Yonggang Yin
- Zhejiang Lab, Hangzhou 311121, Zhejiang, China; (Y.Y.); (C.L.)
| | - Chiye Li
- Zhejiang Lab, Hangzhou 311121, Zhejiang, China; (Y.Y.); (C.L.)
| | - Hongying Liu
- College of Automation, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China;
- Correspondence: (H.L.); (J.S.)
| | - Junhui Shi
- Zhejiang Lab, Hangzhou 311121, Zhejiang, China; (Y.Y.); (C.L.)
- Correspondence: (H.L.); (J.S.)
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Raghushaker CR, Rodrigues J, Nayak SG, Ray S, Urala AS, Satyamoorthy K, Mahato KK. Fluorescence and Photoacoustic Spectroscopy-Based Assessment of Mitochondrial Dysfunction in Oral Cancer Together with Machine Learning: A Pilot Study. Anal Chem 2021; 93:16520-16527. [PMID: 34846862 DOI: 10.1021/acs.analchem.1c03650] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The current study reports an integrated approach of machine learning and tryptophan fluorescence and photoacoustic spectral properties to assess the mitochondrial status under oral pathological conditions. The mitochondria in the study were isolated from oral cancer tissues and adjacent normal counterparts, and the corresponding fluorescence and photoacoustic spectra of tryptophan were recorded at 281 nm pulsed laser excitations. A set of features were selected from the pre-processed spectra and were used to classify the data using support vector machine (SVM) learning in the MATLAB platform. SVM analysis demonstrated clear differentiation between mitochondria isolated from normal and cancer tissues for fluorescence (sensitivity, 86.6%; specificity, 90%) and photoacoustic (sensitivity, 86.6%; specificity, 96.6%) measurements. Further investigation into the influence of change in protein conformation on the nature of tryptophan spectral properties was evaluated by 8-anilino-1-naphthalene sulfonic acid (ANS) fluorescence assay. The impact of protein structural changes on the mitochondrial functions was also estimated by mitochondrial membrane potential (MMP), reactive oxygen species (ROS), and cytochrome c oxidase (COX) assays, suggesting an altered mitochondrial function. The findings indicate that tryptophan fluorescence and photoacoustic spectral properties together with machine learning algorithms may delineate the mitochondrial functional status in vitro, indicating its translational potential.
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Affiliation(s)
| | - Jackson Rodrigues
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal 576104, India
| | - Subramanya G Nayak
- Department of Electronics & Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Satadru Ray
- Department of Surgery, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Mangalore 575001, India
| | - Arun S Urala
- Department of Orthodontics and Dentofacial Orthopaedics, Manipal College of Dental Sciences, Manipal Academy of Higher Education, Manipal 576104, India
| | - Kapaettu Satyamoorthy
- Department of Cell and Molecular Biology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal 576104, India
| | - Krishna Kishore Mahato
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal 576104, India
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