1
|
Wang X, Wang X, Cheng Y, Luo C, Xia W, Gao Z, Bu W, Jiang Y, Fei Y, Shi W, Tang J, Liu L, Zhu J, Zhao X. Construction of metal interpretable scoring system and identification of tungsten as a novel risk factor in COPD. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 283:116842. [PMID: 39106568 DOI: 10.1016/j.ecoenv.2024.116842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 07/24/2024] [Accepted: 08/02/2024] [Indexed: 08/09/2024]
Abstract
Numerous studies have highlighted the correlation between metal intake and deteriorated pulmonary function, emphasizing its pivotal role in the progression of Chronic Obstructive Pulmonary Disease (COPD). However, the efficacy of traditional models is often compromised due to overfitting and high bias in datasets with low-level exposure, rendering them ineffective in delineating the contemporary risk trends associated with pulmonary diseases. To address these limitations, we embarked on developing advanced, interpretable models, crucial for elucidating the intricate mechanisms of metal toxicity and enriching the domain knowledge embedded in toxicity models. In this endeavor, we scrutinized extensive, long-term metal exposure datasets from NHANES to explore the interplay between metal and pulmonary functionality. Employing a variety of machine-learning approaches, we opted for the "Mixer of Experts" model for its proficiency in identifying a myriad of toxicological trends and sensitivities. We conceptualized and illustrated the TSAP (Toxicity Score at Population-level), a metal interpretable scoring system offering performance nearly equivalent to the amalgamation of standard interpretable methods addressing the "black box" conundrum. This streamlined, bifurcated procedural analysis proved instrumental in discerning established risk factors, thereby uncovering Tungsten as a novel contributor to COPD risk. SYNOPSIS: TSAP achieved satisfied performance with transparent interpretability, suggesting tungsten intake need further action for COPD prevention.
Collapse
Affiliation(s)
- Xuehai Wang
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Xiangdong Wang
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Yulan Cheng
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Chao Luo
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Weiyi Xia
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Zhengnan Gao
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Wenxia Bu
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Yichen Jiang
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Yue Fei
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Weiwei Shi
- Nantong Hospital to Nanjing University of Chinese Medicine, China
| | - Juan Tang
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China
| | - Lei Liu
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China; Department of Pathology, Affiliated Hospital of Nantong University, Nantong 226001, China.
| | - Jinfeng Zhu
- Nantong Hospital to Nanjing University of Chinese Medicine, China.
| | - Xinyuan Zhao
- Department of Occupational Medicine and Environmental Toxicology, Nantong Key Laboratory of Environmental Toxicology, School of Public Health, Nantong University, Nantong 226019, China.
| |
Collapse
|
2
|
Yao R, DiSpirito A, Jang H, McGarraugh CT, Nguyen VT, Shi L, Yao J. Virtual-point-based deconvolution for optical-resolution photoacoustic microscopy. JOURNAL OF BIOPHOTONICS 2024; 17:e202400078. [PMID: 38934081 PMCID: PMC11330737 DOI: 10.1002/jbio.202400078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/08/2024] [Accepted: 05/27/2024] [Indexed: 06/28/2024]
Abstract
Optical-resolution photoacoustic microscopy (OR-PAM) has been increasingly utilized for in vivo imaging of biological tissues, offering structural, functional, and molecular information. In OR-PAM, it is often necessary to make a trade-off between imaging depth, lateral resolution, field of view, and imaging speed. To improve the lateral resolution without sacrificing other performance metrics, we developed a virtual-point-based deconvolution algorithm for OR-PAM (VP-PAM). VP-PAM has achieved a resolution improvement ranging from 43% to 62.5% on a single-line target. In addition, it has outperformed Richardson-Lucy deconvolution with 15 iterations in both structural similarity index and peak signal-to-noise ratio on an OR-PAM image of mouse brain vasculature. When applied to an in vivo glass frog image obtained by a deep-penetrating OR-PAM system with compromised lateral resolution, VP-PAM yielded enhanced resolution and contrast with better-resolved microvessels.
Collapse
Affiliation(s)
- Rui Yao
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Anthony DiSpirito
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Hongje Jang
- Department of Biomedical Engineering, University of California San Diego, La Jolla, California, USA
| | | | - Van Tu Nguyen
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Lingyan Shi
- Department of Biomedical Engineering, University of California San Diego, La Jolla, California, USA
| | - Junjie Yao
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| |
Collapse
|
3
|
Jiang D, Zhu L, Tong S, Shen Y, Gao F, Gao F. Photoacoustic imaging plus X: a review. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:S11513. [PMID: 38156064 PMCID: PMC10753847 DOI: 10.1117/1.jbo.29.s1.s11513] [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: 09/06/2023] [Revised: 11/14/2023] [Accepted: 12/11/2023] [Indexed: 12/30/2023]
Abstract
Significance Photoacoustic (PA) imaging (PAI) represents an emerging modality within the realm of biomedical imaging technology. It seamlessly blends the wealth of optical contrast with the remarkable depth of penetration offered by ultrasound. These distinctive features of PAI hold tremendous potential for various applications, including early cancer detection, functional imaging, hybrid imaging, monitoring ablation therapy, and providing guidance during surgical procedures. The synergy between PAI and other cutting-edge technologies not only enhances its capabilities but also propels it toward broader clinical applicability. Aim The integration of PAI with advanced technology for PA signal detection, signal processing, image reconstruction, hybrid imaging, and clinical applications has significantly bolstered the capabilities of PAI. This review endeavor contributes to a deeper comprehension of how the synergy between PAI and other advanced technologies can lead to improved applications. Approach An examination of the evolving research frontiers in PAI, integrated with other advanced technologies, reveals six key categories named "PAI plus X." These categories encompass a range of topics, including but not limited to PAI plus treatment, PAI plus circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. Results After conducting a comprehensive review of the existing literature and research on PAI integrated with other technologies, various proposals have emerged to advance the development of PAI plus X. These proposals aim to enhance system hardware, improve imaging quality, and address clinical challenges effectively. Conclusions The progression of innovative and sophisticated approaches within each category of PAI plus X is positioned to drive significant advancements in both the development of PAI technology and its clinical applications. Furthermore, PAI not only has the potential to integrate with the above-mentioned technologies but also to broaden its applications even further.
Collapse
Affiliation(s)
- Daohuai Jiang
- ShanghaiTech University, School of Information Science and Technology, Shanghai, China
- Fujian Normal University, College of Photonic and Electronic Engineering, Fuzhou, China
| | - Luyao Zhu
- ShanghaiTech University, School of Information Science and Technology, Shanghai, China
| | - Shangqing Tong
- ShanghaiTech University, School of Information Science and Technology, Shanghai, China
| | - Yuting Shen
- ShanghaiTech University, School of Information Science and Technology, Shanghai, China
| | - Feng Gao
- ShanghaiTech University, School of Information Science and Technology, Shanghai, China
| | - Fei Gao
- ShanghaiTech University, School of Information Science and Technology, Shanghai, China
- Shanghai Engineering Research Center of Energy Efficient and Custom AI IC, Shanghai, China
- Shanghai Clinical Research and Trial Center, Shanghai, China
| |
Collapse
|
4
|
Zhang Z, Jin H, Zhang W, Lu W, Zheng Z, Sharma A, Pramanik M, Zheng Y. Adaptive enhancement of acoustic resolution photoacoustic microscopy imaging via deep CNN prior. PHOTOACOUSTICS 2023; 30:100484. [PMID: 37095888 PMCID: PMC10121479 DOI: 10.1016/j.pacs.2023.100484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 03/29/2023] [Indexed: 05/03/2023]
Abstract
Acoustic resolution photoacoustic microscopy (AR-PAM) is a promising medical imaging modality that can be employed for deep bio-tissue imaging. However, its relatively low imaging resolution has greatly hindered its wide applications. Previous model-based or learning-based PAM enhancement algorithms either require design of complex handcrafted prior to achieve good performance or lack the interpretability and flexibility that can adapt to different degradation models. However, the degradation model of AR-PAM imaging is subject to both imaging depth and center frequency of ultrasound transducer, which varies in different imaging conditions and cannot be handled by a single neural network model. To address this limitation, an algorithm integrating both learning-based and model-based method is proposed here so that a single framework can deal with various distortion functions adaptively. The vasculature image statistics is implicitly learned by a deep convolutional neural network, which served as plug and play (PnP) prior. The trained network can be directly plugged into the model-based optimization framework for iterative AR-PAM image enhancement, which fitted for different degradation mechanisms. Based on physical model, the point spread function (PSF) kernels for various AR-PAM imaging situations are derived and used for the enhancement of simulation and in vivo AR-PAM images, which collectively proved the effectiveness of proposed method. Quantitatively, the PSNR and SSIM values have all achieve best performance with the proposed algorithm in all three simulation scenarios; The SNR and CNR values have also significantly raised from 6.34 and 5.79 to 35.37 and 29.66 respectively in an in vivo testing result with the proposed algorithm.
Collapse
Affiliation(s)
- Zhengyuan Zhang
- Nanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore
| | - Haoran Jin
- Zhejiang University, College of Mechanical Engineering, The State Key Laboratory of Fluid Power and Mechatronic Systems, Hangzhou 310027, China
| | - Wenwen Zhang
- Nanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore
| | - Wenhao Lu
- Nanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore
| | - Zesheng Zheng
- Nanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore
| | - Arunima Sharma
- Johns Hopkins University, Electrical and Computer Engineering, Baltimore, MD 21218, USA
| | - Manojit Pramanik
- Iowa State University, Department of Electrical and Computer Engineering, Ames, Iowa, USA
| | - Yuanjin Zheng
- Nanyang Technological University, School of Electrical and Electronic Engineering, 639798, Singapore
- Corresponding author.
| |
Collapse
|