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Arumugam S, Ma J, Macar U, Han G, McAulay K, Ingram D, Ying A, Chellani HH, Chern T, Reilly K, Colburn DAM, Stanciu R, Duffy C, Williams A, Grys T, Chang SF, Sia SK. Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics. COMMUNICATIONS MEDICINE 2023; 3:91. [PMID: 37353603 DOI: 10.1038/s43856-023-00312-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 06/01/2023] [Indexed: 06/25/2023] Open
Abstract
BACKGROUND Point-of-care diagnostic devices, such as lateral-flow assays, are becoming widely used by the public. However, efforts to ensure correct assay operation and result interpretation rely on hardware that cannot be easily scaled or image processing approaches requiring large training datasets, necessitating large numbers of tests and expert labeling with validated specimens for every new test kit format. METHODS We developed a software architecture called AutoAdapt POC that integrates automated membrane extraction, self-supervised learning, and few-shot learning to automate the interpretation of POC diagnostic tests using smartphone cameras in a scalable manner. A base model pre-trained on a single LFA kit is adapted to five different COVID-19 tests (three antigen, two antibody) using just 20 labeled images. RESULTS Here we show AutoAdapt POC to yield 99% to 100% accuracy over 726 tests (350 positive, 376 negative). In a COVID-19 drive-through study with 74 untrained users self-testing, 98% found image collection easy, and the rapidly adapted models achieved classification accuracies of 100% on both COVID-19 antigen and antibody test kits. Compared with traditional visual interpretation on 105 test kit results, the algorithm correctly identified 100% of images; without a false negative as interpreted by experts. Finally, compared to a traditional convolutional neural network trained on an HIV test kit, the algorithm showed high accuracy while requiring only 1/50th of the training images. CONCLUSIONS The study demonstrates how rapid domain adaptation in machine learning can provide quality assurance, linkage to care, and public health tracking for untrained users across diverse POC diagnostic tests.
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Affiliation(s)
- Siddarth Arumugam
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Jiawei Ma
- Department of Computer Science, Columbia University, New York, NY, 10027, USA
| | - Uzay Macar
- Department of Computer Science, Columbia University, New York, NY, 10027, USA
| | - Guangxing Han
- Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA
| | - Kathrine McAulay
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | | | - Alex Ying
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | | | - Terry Chern
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Kenta Reilly
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - David A M Colburn
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Robert Stanciu
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Craig Duffy
- Safe Health Systems, Inc., Los Angeles, CA, 90036, USA
| | | | - Thomas Grys
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Shih-Fu Chang
- Department of Computer Science, Columbia University, New York, NY, 10027, USA.
- Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA.
| | - Samuel K Sia
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
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2
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Transmission schedule for jointly optimizing remote state estimation and wireless sensor network lifetime. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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3
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Recent advances in the rapid detection of microRNA with lateral flow assays. Biosens Bioelectron 2022; 211:114345. [DOI: 10.1016/j.bios.2022.114345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/18/2022] [Accepted: 05/03/2022] [Indexed: 12/14/2022]
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Liangming C, Xiaoqiong C, Min D, Binxin M, Minfen L, Zhicheng Z, Shumin L, Yuxin R, Qiaolin H, Shuqin Y. OSA Patient Monitoring Based on the Beidou System. Front Public Health 2021; 9:745524. [PMID: 34869160 PMCID: PMC8634951 DOI: 10.3389/fpubh.2021.745524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 10/12/2021] [Indexed: 01/10/2023] Open
Abstract
This paper presents an OSA patient interactive monitoring system based on the Beidou system. This system allows OSA patients to get timely rescue when they become sleepy outside. Because the Beidou position marker has an interactive function, it can reduce the anxiety of the patient while waiting for the rescue. At the same time, if a friend helps the OSA patients to call the doctor, the friend can also report the patient's condition in time. This system uses the popular IoT framework. At the bottom is the data acquisition layer, which uses wearable sensors to collect vital signs from patients, with a focus on ECG and SpO2 signals. The middle layer is the network layer that transmits the collected physiological signals to the Beidou indicator using the Bluetooth Low Energy (BLE) protocol. The top layer is the application layer, and the application layer uses the mature rescue interactive platform of Beidou. The Beidou system was developed by China itself, the main coverage of the satellite is in Asia, and is equipped with a high-density ground-based augmentation system. Therefore, the Beidou model improves the positioning accuracy and is equipped with a special communication satellite, which increases the short message interaction function. Therefore, patients can report disease progression in time while waiting for a rescue. After our simulation test, the effectiveness of the OSA patient rescue monitoring system based on the Beidou system and the positioning accuracy of OSA patients have been greatly improved. Especially when OSA patients work outdoors, the cell phone base station signal coverage is relatively weak. The satellite signal is well-covered, plus the SMS function of the Beidou indicator. Therefore, the system can be used to provide timely patient progress and provide data support for the medical rescue team to provide a more accurate rescue plan. After a comparative trial, the rescue rate of OSA patients using the detection device of this system was increased by 15 percentage points compared with the rescue rate using only GPS satellite phones.
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Affiliation(s)
- Cai Liangming
- School of Physics and Information Engineering, Zhicheng College, Fuzhou University, Fuzhou, China
| | - Cai Xiaoqiong
- Third Institute of Oceanography, State Oceanic Administration, Xiamen, China
| | - Du Min
- Laboratory of Eco-Industrial Green Technology Wuyi College, Wuyishan, China
| | - Miao Binxin
- College of Zhicheng, Fuzhou University, Fuzhou, China
| | - Lin Minfen
- College of Zhicheng, Fuzhou University, Fuzhou, China
| | - Zeng Zhicheng
- College of Zhicheng, Fuzhou University, Fuzhou, China
| | - Li Shumin
- College of Zhicheng, Fuzhou University, Fuzhou, China
| | - Ruan Yuxin
- College of Zhicheng, Fuzhou University, Fuzhou, China
| | - Hu Qiaolin
- College of Zhicheng, Fuzhou University, Fuzhou, China
| | - Yang Shuqin
- College of Zhicheng, Fuzhou University, Fuzhou, China
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Xie H, Song J, Zhong Y, Li J, Gu C, Choi KS. Extended Kalman Filter Nonlinear Finite Element Method for Nonlinear Soft Tissue Deformation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105828. [PMID: 33199083 DOI: 10.1016/j.cmpb.2020.105828] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 10/31/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Soft tissue modelling is crucial to surgery simulation. This paper introduces an innovative approach to realistic simulation of nonlinear deformation behaviours of biological soft tissues in real time. METHODS This approach combines the traditional nonlinear finite-element method (NFEM) and nonlinear Kalman filtering to address both physical fidelity and real-time performance for soft tissue modelling. It defines tissue mechanical deformation as a nonlinear filtering process for dynamic estimation of nonlinear deformation behaviours of biological tissues. Tissue mechanical deformation is discretized in space using NFEM in accordance with nonlinear elastic theory and in time using the central difference scheme to establish the nonlinear state-space models for dynamic filtering. RESULTS An extended Kalman filter is established to dynamically estimate nonlinear mechanical deformation of biological tissues. Interactive deformation of biological soft tissues with haptic feedback is accomplished as well for surgery simulation. CONCLUSIONS The proposed approach conquers the NFEM limitation of step computation but without trading off the modelling accuracy. It not only has a similar level of accuracy as NFEM, but also meets the real-time requirement for soft tissue modelling.
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Affiliation(s)
- Hujin Xie
- School of Engineering, RMIT University, Australia.
| | - Jialu Song
- School of Engineering, RMIT University, Australia
| | | | - Jiankun Li
- School of Engineering, RMIT University, Australia
| | - Chengfan Gu
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong
| | - Kup-Sze Choi
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong
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Kumar Y, Narsaiah K. Rapid point-of-care testing methods/devices for meat species identification: A review. Compr Rev Food Sci Food Saf 2020; 20:900-923. [PMID: 33443804 DOI: 10.1111/1541-4337.12674] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 08/30/2020] [Accepted: 10/25/2020] [Indexed: 12/15/2022]
Abstract
The authentication of animal species is an important issue due to an increasing trend of adulteration and mislabeling of animal species in processed meat products. Polymerase chain reaction is the most sensitive and specific technique for nucleic acid-based animal species detection. However, it is a time-consuming technique that requires costly thermocyclers and sophisticated labs. In recent times, there is a need of on-site detection by point-of-care (POC) testing methods and devices under low-resource settings. These POC devices must be affordable, sensitive, specific, user-friendly, rapid and robust, equipment free, and delivered to the end users. POC devices should also confirm the concept of micro total analysis system. This review discusses POC testing methods and devices that have been developed for meat species identification. Recent developments in lateral flow assay-based devices for the identification of animal species in meat products are also reviewed. Advancements in increasing the efficiency of lateral flow detection are also discussed.
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Affiliation(s)
- Yogesh Kumar
- Department of Agricultural Structures and Environmental Control, ICAR-Central Institute of Post-Harvest Engineering and Technology (CIPHET), Ludhiana, India
| | - Kairam Narsaiah
- Department of Agricultural Structures and Environmental Control, ICAR-Central Institute of Post-Harvest Engineering and Technology (CIPHET), Ludhiana, India
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7
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Liu X, Yang X. A variational Bayesian approach for robust identification of linear parameter varying systems using mixture laplace distributions. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.01.088] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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8
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Qin Q, Wang K, Yang J, Xu H, Cao B, Wo Y, Jin Q, Cui D. Algorithms for immunochromatographic assay: review and impact on future application. Analyst 2020; 144:5659-5676. [PMID: 31417996 DOI: 10.1039/c9an00964g] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Lateral flow immunoassay (LFIA) is a critical choice for applications of point-of-care testing (POCT) in clinical and laboratory environments because of its excellent features and versatility. To obtain authentic values of analyte concentrations and reliable detection results, the relevant research has featured the application of a diversity of methods of mathematical analysis to technical analysis to allow for use with a small quantity of data. Accordingly, a number of signal and image processing strategies have also emerged for the application of gold immunochromatographic and fluorescent strips to improve sensitivity and overcome the limitations of correlative hardware systems. Instead of traditional methods to solve the problem, researchers nowadays are interested in machine learning and its more powerful variant, deep learning technology, for LFIA detection. This review emphasizes different models for the POCT of accurate labels as well as signal processing strategies that use artificial intelligence and machine learning. We focus on the analytical mechanism, procedural flow, and the results of the assay, and conclude by summarizing the advantages and limitations of each algorithm. We also discuss the potential for application of and directions of future research on LFIA technology when combined with Artificial Intelligence and deep learning.
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Affiliation(s)
- Qi Qin
- Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai Engineering Research Center for Intelligent diagnosis and treatment instrument, Key Laboratory of Thin Film and Microfabrication (Ministry of Education), Shanghai 200240, China.
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9
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Saatci E, Saatci E, Akan A. Analysis of linear lung models based on state-space models. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 183:105094. [PMID: 31586787 DOI: 10.1016/j.cmpb.2019.105094] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 08/22/2019] [Accepted: 09/23/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVES Linear parametric respiratory system models have been used in the model-based analysis of the respiratory system. Although there are studies exploring the physiological correctness and fitting accuracy of the models, they are not analysed in terms of interaction between parameters and dynamics of the model. In this study we propose to use state-space modelling to yield the time-varying nature of the system incorporated by the parameters. METHODS We tested controllability, observability and stability characteristics of the equation of motion, 2-comp. parallel, 2-comp. series, viscoelastic, 6-element and mead models while using the parameters given in the literature. In the sensitivity analysis we proposed to use dual Desensitized Linear Kalman Filter (DKF) and Extended Kalman Filter (EKF) method. In this method, state error covariance revealed the parameter sensitivities for each model. RESULTS Results showed that all models, except 2-comp. parallel and mead models, are both controllable and observable models. On the other hand all models, except mead model, are stable models. Regarding to the sensitivity analysis, dual DKF - EKF method estimated states of the models successfully with a low estimation error. Sensitivity analysis results showed that airway parameters have higher effects on the state estimation than the other parameters have. CONCLUSION We proved that state-space evaluation of the previously proposed parametric models of the respiratory system led us to quantitative and qualitative assessments of the respiratory models. Moreover parameter values found in the literature have different effects on the models.
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Affiliation(s)
- Esra Saatci
- Department of Electrical and Electronics Engineering, Istanbul Kultur University, Bakirkoy, Istanbul.
| | - Ertugrul Saatci
- Department of Electrical and Electronics Engineering, Istanbul Kultur University, Bakirkoy, Istanbul.
| | - Aydin Akan
- Department of Biomedical Engineering, Izmir Katip Celebi University, Izmir.
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10
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A momentum-incorporated latent factorization of tensors model for temporal-aware QoS missing data prediction. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.08.026] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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11
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12
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Gasperino D, Baughman T, Hsieh HV, Bell D, Weigl BH. Improving Lateral Flow Assay Performance Using Computational Modeling. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2018; 11:219-244. [PMID: 29595992 DOI: 10.1146/annurev-anchem-061417-125737] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
The performance, field utility, and low cost of lateral flow assays (LFAs) have driven a tremendous shift in global health care practices by enabling diagnostic testing in previously unserved settings. This success has motivated the continued improvement of LFAs through increasingly sophisticated materials and reagents. However, our mechanistic understanding of the underlying processes that drive the informed design of these systems has not received commensurate attention. Here, we review the principles underpinning LFAs and the historical evolution of theory to predict their performance. As this theory is integrated into computational models and becomes testable, the criteria for quantifying performance and validating predictive power are critical. The integration of computational design with LFA development offers a promising and coherent framework to choose from an increasing number of novel materials, techniques, and reagents to deliver the low-cost, high-fidelity assays of the future.
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Affiliation(s)
- David Gasperino
- Intellectual Ventures Laboratory, Bellevue, Washington 98007, USA
| | - Ted Baughman
- Intellectual Ventures Laboratory, Bellevue, Washington 98007, USA
| | - Helen V Hsieh
- Intellectual Ventures Laboratory, Bellevue, Washington 98007, USA
| | - David Bell
- Intellectual Ventures Laboratory, Bellevue, Washington 98007, USA
| | - Bernhard H Weigl
- Intellectual Ventures Laboratory, Bellevue, Washington 98007, USA
- Department of Bioengineering, University of Washington, Seattle, Washington 98195, USA
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Sotnikov DV, Zherdev AV, Dzantiev BB. Mathematical Modeling of Bioassays. BIOCHEMISTRY (MOSCOW) 2018. [PMID: 29523069 DOI: 10.1134/s0006297917130119] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The high affinity and specificity of biological receptors determine the demand for and the intensive development of analytical systems based on use of these receptors. Therefore, theoretical concepts of the mechanisms of these systems, quantitative parameters of their reactions, and relationships between their characteristics and ligand-receptor interactions have become extremely important. Many mathematical models describing different bioassay formats have been proposed. However, there is almost no information on the comparative characteristics of these models, their assumptions, and predictive insights. In this review we suggested a set of criteria to classify various bioassays and reviewed classical and contemporary publications on these bioassays with special emphasis on immunochemical analysis systems as the most common and in-demand techniques. The possibilities of analytical and numerical modeling are discussed, as well as estimations of the minimum concentrations that may be detected in bioassays and recommendations for the choice of assay conditions.
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Affiliation(s)
- D V Sotnikov
- Bach Institute of Biochemistry, Research Center for Biotechnology, Russian Academy of Sciences, Moscow, 119071, Russia.
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14
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Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach. Comput Biol Med 2018; 92:168-175. [DOI: 10.1016/j.compbiomed.2017.11.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 11/15/2017] [Accepted: 11/15/2017] [Indexed: 01/12/2023]
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15
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Zhu HJ, You ZH, Zhu ZX, Shi WL, Chen X, Cheng L. DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.07.030] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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16
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Li Y, Chen J, Jiang L, Zeng N, Jiang H, Du M. The p53–Mdm2 regulation relationship under different radiation doses based on the continuous–discrete extended Kalman filter algorithm. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.08.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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17
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Liu Z, Qu Z, Tang R, He X, Yang H, Bai D, Xu F. An improved detection limit and working range of lateral flow assays based on a mathematical model. Analyst 2018; 143:2775-2783. [DOI: 10.1039/c8an00179k] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The detection limit and working range of lateral flow assays are investigated experimentally and numerically.
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Affiliation(s)
- Zhi Liu
- Key Laboratory of Thermo-Fluid Science and Engineering of Ministry of Education
- School of Energy and Power Engineering
- Xi'an Jiaotong University
- Xi'an 710049
- P.R. China
| | - Zhiguo Qu
- Key Laboratory of Thermo-Fluid Science and Engineering of Ministry of Education
- School of Energy and Power Engineering
- Xi'an Jiaotong University
- Xi'an 710049
- P.R. China
| | - Ruihua Tang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education
- School of Life Science and Technology
- Xi'an Jiaotong University
- Xi'an 710049
- P.R. China
| | - Xiaocong He
- Bioinspired Engineering and Biomechanics Center (BEBC)
- Xi'an Jiaotong University
- Xi'an 710049
- P.R. China
- Key Laboratory of Biomedical Information Engineering of Ministry of Education
| | - Hui Yang
- School of Life Sciences
- Northwestern Polytechnical University
- Xi'an 710072
- P.R. China
| | - Dan Bai
- Key Laboratory of Biomedical Information Engineering of Ministry of Education
- School of Life Science and Technology
- Xi'an Jiaotong University
- Xi'an 710049
- P.R. China
| | - Feng Xu
- Bioinspired Engineering and Biomechanics Center (BEBC)
- Xi'an Jiaotong University
- Xi'an 710049
- P.R. China
- Key Laboratory of Biomedical Information Engineering of Ministry of Education
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19
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Yuan Y, Luo X, Shang MS. Effects of preprocessing and training biases in latent factor models for recommender systems. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.10.040] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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20
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Long-term performance of collaborative filtering based recommenders in temporally evolving systems. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.06.026] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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SHIN JAEHYUN, ZHONG YONGMIN, SMITH JULIAN, GU CHENGFAN. ADAPTIVE UNSCENTED KALMAN FILTER FOR ONLINE SOFT TISSUES CHARACTERIZATION. J MECH MED BIOL 2017. [DOI: 10.1142/s0219519417400140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Online soft tissue characterization is important for robotic-assisted minimally invasive surgery to achieve precise and stable robotic control with haptic feedback. This paper presents a new adaptive unscented Kalman filter based on the nonlinear Hunt–Crossley model for online soft tissue characterization without requiring the characteristics of system noise. This filter incorporates the concept of Sage windowing in the traditional unscented Kalman filter to adaptively estimate system noise covariance using predicted residuals within a time window. In order to account for the inherent relationship between the current and previous states of soft tissue deformation involved in robotic-assisted surgery and improve the estimation performance, a recursive estimation of system noise covariance is further constructed by introducing a fading scaling factor to control the contributions between noise covariance estimations at current and previous time points. The proposed adaptive unscented Kalman filter overcomes the limitation of the traditional unscented Kalman filter in requiring the characteristics of system noise. Simulations and comparisons show the efficacy of the suggested nonlinear adaptive unscented Kalman filter for online soft tissue characterization.
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Affiliation(s)
- JAEHYUN SHIN
- School of Engineering, RMIT University, Bundoora, VIC 3083, Australia
| | - YONGMIN ZHONG
- School of Engineering, RMIT University, Bundoora, VIC 3083, Australia
| | - JULIAN SMITH
- Department of Surgery, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia
| | - CHENGFAN GU
- School of Engineering, RMIT University, Bundoora, VIC 3083, Australia
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22
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Denoising and deblurring gold immunochromatographic strip images via gradient projection algorithms. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.03.056] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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23
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Sotnikov DV, Zherdev AV, Dzantiev BB. Mathematical Model of Serodiagnostic Immunochromatographic Assay. Anal Chem 2017; 89:4419-4427. [PMID: 28337911 DOI: 10.1021/acs.analchem.6b03635] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
This article describes the mathematical model for an immunochromatographic assay for the detection of specific immunoglobulins against a target antigen (antibodies) in blood/serum (serodiagnosis). The model utilizes an analytical (non-numerical) approach and allows the calculation of the kinetics of immune complexes' formation in a continuous-flow system using commonly available software, such as Microsoft Excel. The developed model could identify the nature of the influence of immunochemical interaction constants and reagent concentrations on the kinetics of the formation of the detected target complex. On the basis of the model, recommendations are developed to decrease the detection limit for an immunochromatographic assay of specific immunoglobulins.
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Affiliation(s)
- Dmitriy V Sotnikov
- A.N. Bach Institute of Biochemistry, Federal Research Centre "Fundamentals of Biotechnology", Russian Academy of Sciences , Leninsky Prospect 33, Moscow 119071, Russia
| | - Anatoly V Zherdev
- A.N. Bach Institute of Biochemistry, Federal Research Centre "Fundamentals of Biotechnology", Russian Academy of Sciences , Leninsky Prospect 33, Moscow 119071, Russia
| | - Boris B Dzantiev
- A.N. Bach Institute of Biochemistry, Federal Research Centre "Fundamentals of Biotechnology", Russian Academy of Sciences , Leninsky Prospect 33, Moscow 119071, Russia
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24
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Hossain S, Mohammadi FA. Tumor parameter estimation considering the body geometry by thermography. Comput Biol Med 2016; 76:80-93. [DOI: 10.1016/j.compbiomed.2016.06.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Revised: 06/22/2016] [Accepted: 06/23/2016] [Indexed: 12/01/2022]
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25
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Zeng N, Wang Z, Zhang H, Liu W, Alsaadi FE. Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip. Cognit Comput 2016. [DOI: 10.1007/s12559-016-9404-x] [Citation(s) in RCA: 97] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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26
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A Novel Switching Delayed PSO Algorithm for Estimating Unknown Parameters of Lateral Flow Immunoassay. Cognit Comput 2016. [DOI: 10.1007/s12559-016-9396-6] [Citation(s) in RCA: 101] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Sajid M, Kawde AN, Daud M. Designs, formats and applications of lateral flow assay: A literature review. JOURNAL OF SAUDI CHEMICAL SOCIETY 2015. [DOI: 10.1016/j.jscs.2014.09.001] [Citation(s) in RCA: 444] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Zeng N, Wang Z, Zineddin B, Li Y, Du M, Xiao L, Liu X, Young T. Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1129-1136. [PMID: 24770917 DOI: 10.1109/tmi.2014.2305394] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time.
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Wang Z, Eatock J, McClean S, Liu D, Liu X, Young T. Modeling Throughput of Emergency Departments via Time Series. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2013. [DOI: 10.1145/2544105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
In this article, the expectation maximization (EM) algorithm is applied for modeling the throughput of emergency departments via available time-series data. The dynamics of emergency department throughput is developed and evaluated, for the first time, as a stochastic dynamic model that consists of the noisy measurement and first-order autoregressive (AR) stochastic dynamic process. By using the EM algorithm, the model parameters, the actual throughput, as well as the noise intensity, can be identified simultaneously. Four real-world time series collected from an emergency department in West London are employed to demonstrate the effectiveness of the introduced algorithm. Several quantitative indices are proposed to evaluate the inferred models. The simulation shows that the identified model fits the data very well.
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Affiliation(s)
| | | | | | - Dongmei Liu
- Nanjing University of Science and Technology, China
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Zeng N, Wang Z, Li Y, Du M, Cao J, Liu X. Time Series Modeling of Nano-Gold Immunochromatographic Assay via Expectation Maximization Algorithm. IEEE Trans Biomed Eng 2013; 60:3418-24. [PMID: 23629840 DOI: 10.1109/tbme.2013.2260160] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this paper, the expectation maximization (EM) algorithm is applied to the modeling of the nano-gold immunochromatographic assay (nano-GICA) via available time series of the measured signal intensities of the test and control lines. The model for the nano-GICA is developed as the stochastic dynamic model that consists of a first-order autoregressive stochastic dynamic process and a noisy measurement. By using the EM algorithm, the model parameters, the actual signal intensities of the test and control lines, as well as the noise intensity can be identified simultaneously. Three different time series data sets concerning the target concentrations are employed to demonstrate the effectiveness of the introduced algorithm. Several indices are also proposed to evaluate the inferred models. It is shown that the model fits the data very well.
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Murtuza Baker S, Poskar CH, Schreiber F, Junker BH. An improved constraint filtering technique for inferring hidden states and parameters of a biological model. ACTA ACUST UNITED AC 2013; 29:1052-9. [PMID: 23434837 DOI: 10.1093/bioinformatics/btt097] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION In systems biology, kinetic models represent the biological system using a set of ordinary differential equations (ODEs). The correct values of the parameters within these ODEs are critical for a reliable study of the dynamic behaviour of such systems. Typically, it is only possible to experimentally measure a fraction of these parameter values. The rest must be indirectly determined from measurements of other quantities. In this article, we propose a novel statistical inference technique to computationally estimate these unknown parameter values. By characterizing the ODEs with non-linear state-space equations, this inference technique models the unknown parameters as hidden states, which can then be estimated from noisy measurement data. RESULTS Here we extended the square-root unscented Kalman filter SR-UKF proposed by Merwe and Wan to include constraints with the state estimation process. We developed the constrained square-root unscented Kalman filter (CSUKF) to estimate parameters of non-linear state-space models. This probabilistic inference technique was successfully used to estimate parameters of a glycolysis model in yeast and a gene regulatory network. We showed that our method is numerically stable and can reliably estimate parameters within a biologically meaningful parameter space from noisy observations. When compared with the two common non-linear extensions of Kalman filter in addition to four widely used global optimization algorithms, CSUKF is shown to be both accurate and computationally efficient. With CSUKF, statistical analysis is straightforward, as it directly provides the uncertainty on the estimation result. AVAILABILITY AND IMPLEMENTATION Matlab code available upon request from the author. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Syed Murtuza Baker
- Systems Biology Group, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
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Sheng C, Zhao J, Liu Y, Wang W. Prediction for noisy nonlinear time series by echo state network based on dual estimation. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2011.11.021] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Zeng N, Wang Z, Li Y, Du M, Liu X. A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2011; 9:321-329. [PMID: 22025755 DOI: 10.1109/tcbb.2011.140] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
In this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.
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