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Farjami E, Mahjoob M. Multiscale modeling of the dynamic growth of cancerous tumors under the influence of chemotherapy drugs. Comput Methods Biomech Biomed Engin 2024; 27:919-930. [PMID: 37227061 DOI: 10.1080/10255842.2023.2215368] [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: 10/25/2022] [Accepted: 05/12/2023] [Indexed: 05/26/2023]
Abstract
A comprehensive model of chemotherapy treatment of cancer can help us to optimize the drug administration/dosage and improve the treatment outcome. In the present study, a multiscale mathematical model of tumor growth during chemotherapy treatment is developed to predict its response to the medication and cancer progression. The modeling is a continuous multiscale simulation consisting of three tissue phases including cancer cells, normal cells, and extracellular matrix. In addition to the drug administration, the impacts of immune cells, programmed cell death, nutrient competition, and glucose concentration are included. The outputs of our mathematical model conform to the published experimental and clinical data, and it can be used in optimizing chemotherapy, and personalized cancer treatment.
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Affiliation(s)
- Emad Farjami
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mohammad Mahjoob
- Department of Engineering, School of Engineering, Science and Technology, CCSU, New Britain, CT, USA
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Stephan S, Galland S, Labbani Narsis O, Shoji K, Vachenc S, Gerart S, Nicolle C. Agent-based approaches for biological modeling in oncology: A literature review. Artif Intell Med 2024; 152:102884. [PMID: 38703466 DOI: 10.1016/j.artmed.2024.102884] [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: 07/01/2023] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/06/2024]
Abstract
CONTEXT Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions. METHODOLOGY This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023. RESULTS Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.
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Affiliation(s)
- Simon Stephan
- UTBM, CIAD UMR 7533, Belfort, F-90010, France; Université de Bourgogne, CIAD UMR 7533, Dijon, F-21000, France.
| | | | | | - Kenji Shoji
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | - Sébastien Vachenc
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | - Stéphane Gerart
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
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3
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Ahlquist KD, Sugden LA, Ramachandran S. Enabling interpretable machine learning for biological data with reliability scores. PLoS Comput Biol 2023; 19:e1011175. [PMID: 37235578 PMCID: PMC10249903 DOI: 10.1371/journal.pcbi.1011175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 06/08/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Machine learning tools have proven useful across biological disciplines, allowing researchers to draw conclusions from large datasets, and opening up new opportunities for interpreting complex and heterogeneous biological data. Alongside the rapid growth of machine learning, there have also been growing pains: some models that appear to perform well have later been revealed to rely on features of the data that are artifactual or biased; this feeds into the general criticism that machine learning models are designed to optimize model performance over the creation of new biological insights. A natural question arises: how do we develop machine learning models that are inherently interpretable or explainable? In this manuscript, we describe the SWIF(r) reliability score (SRS), a method building on the SWIF(r) generative framework that reflects the trustworthiness of the classification of a specific instance. The concept of the reliability score has the potential to generalize to other machine learning methods. We demonstrate the utility of the SRS when faced with common challenges in machine learning including: 1) an unknown class present in testing data that was not present in training data, 2) systemic mismatch between training and testing data, and 3) instances of testing data that have missing values for some attributes. We explore these applications of the SRS using a range of biological datasets, from agricultural data on seed morphology, to 22 quantitative traits in the UK Biobank, and population genetic simulations and 1000 Genomes Project data. With each of these examples, we demonstrate how the SRS can allow researchers to interrogate their data and training approach thoroughly, and to pair their domain-specific knowledge with powerful machine-learning frameworks. We also compare the SRS to related tools for outlier and novelty detection, and find that it has comparable performance, with the advantage of being able to operate when some data are missing. The SRS, and the broader discussion of interpretable scientific machine learning, will aid researchers in the biological machine learning space as they seek to harness the power of machine learning without sacrificing rigor and biological insight.
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Affiliation(s)
- K. D. Ahlquist
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
- Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, Rhode Island, United States of America
| | - Lauren A. Sugden
- Department of Mathematics and Computer Science, Duquesne University, Pittsburgh, Pennsylvania, United States of America
| | - Sohini Ramachandran
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
- Department of Ecology, Evolution and Organismal Biology, Brown University, Providence, Rhode Island, United States of America
- Data Science Initiative, Brown University, Providence, Rhode Island, United States of America
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Uleman JF, Mancini E, Al-Shama RF, te Velde AA, Kraneveld AD, Castiglione F. A multiscale hybrid model for exploring the effect of Resolvin D1 on macrophage polarization during acute inflammation. Math Biosci 2023; 359:108997. [PMID: 36996999 DOI: 10.1016/j.mbs.2023.108997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 03/23/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023]
Abstract
Dysregulated inflammation underlies various diseases. Specialized pro-resolving mediators (SPMs) like Resolvin D1 (RvD1) have been shown to resolve inflammation and halt disease progression. Macrophages, key immune cells that drive inflammation, respond to the presence of RvD1 by polarizing to an anti-inflammatory type (M2). However, RvD1's mechanisms, roles, and utility are not fully understood. This paper introduces a gene-regulatory network (GRN) model that contains pathways for RvD1 and other SPMs and proinflammatory molecules like lipopolysaccharides. We couple this GRN model to a partial differential equation - agent-based hybrid model using a multiscale framework to simulate an acute inflammatory response with and without the presence of RvD1. We calibrate and validate the model using experimental data from two animal models. The model reproduces the dynamics of key immune components and the effects of RvD1 during acute inflammation. Our results suggest RvD1 can drive macrophage polarization through the G protein-coupled receptor 32 (GRP32) pathway. The presence of RvD1 leads to an earlier and increased M2 polarization, reduced neutrophil recruitment, and faster apoptotic neutrophil clearance. These results support a body of literature that suggests that RvD1 is a promising candidate for promoting the resolution of acute inflammation. We conclude that once calibrated and validated on human data, the model can identify critical sources of uncertainty, which could be further elucidated in biological experiments and assessed for clinical use.
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Wu C, Lorenzo G, Hormuth DA, Lima EABF, Slavkova KP, DiCarlo JC, Virostko J, Phillips CM, Patt D, Chung C, Yankeelov TE. Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. BIOPHYSICS REVIEWS 2022; 3:021304. [PMID: 35602761 PMCID: PMC9119003 DOI: 10.1063/5.0086789] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/29/2022] [Indexed: 12/11/2022]
Abstract
Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.
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Affiliation(s)
- Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | | | | | | | - Kalina P. Slavkova
- Department of Physics, The University of Texas at Austin, Austin, Texas 78712, USA
| | | | | | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Debra Patt
- Texas Oncology, Austin, Texas 78731, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas 77030, USA
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Shou Y, Johnson SC, Quek YJ, Li X, Tay A. Integrative lymph node-mimicking models created with biomaterials and computational tools to study the immune system. Mater Today Bio 2022; 14:100269. [PMID: 35514433 PMCID: PMC9062348 DOI: 10.1016/j.mtbio.2022.100269] [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: 02/17/2022] [Revised: 04/16/2022] [Accepted: 04/18/2022] [Indexed: 11/17/2022] Open
Abstract
The lymph node (LN) is a vital organ of the lymphatic and immune system that enables timely detection, response, and clearance of harmful substances from the body. Each LN comprises of distinct substructures, which host a plethora of immune cell types working in tandem to coordinate complex innate and adaptive immune responses. An improved understanding of LN biology could facilitate treatment in LN-associated pathologies and immunotherapeutic interventions, yet at present, animal models, which often have poor physiological relevance, are the most popular experimental platforms. Emerging biomaterial engineering offers powerful alternatives, with the potential to circumvent limitations of animal models, for in-depth characterization and engineering of the lymphatic and adaptive immune system. In addition, mathematical and computational approaches, particularly in the current age of big data research, are reliable tools to verify and complement biomaterial works. In this review, we first discuss the importance of lymph node in immunity protection followed by recent advances using biomaterials to create in vitro/vivo LN-mimicking models to recreate the lymphoid tissue microstructure and microenvironment, as well as to describe the related immuno-functionality for biological investigation. We also explore the great potential of mathematical and computational models to serve as in silico supports. Furthermore, we suggest how both in vitro/vivo and in silico approaches can be integrated to strengthen basic patho-biological research, translational drug screening and clinical personalized therapies. We hope that this review will promote synergistic collaborations to accelerate progress of LN-mimicking systems to enhance understanding of immuno-complexity.
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Key Words
- ABM, agent-based model
- APC, antigen-presenting cell
- BV, blood vessel
- Biomaterials
- CPM, Cellular Potts model
- Computational models
- DC, dendritic cell
- ECM, extracellular matrix
- FDC, follicular dendritic cell
- FRC, fibroblastic reticular cell
- Immunotherapy
- LEC, lymphatic endothelial cell
- LN, lymph node
- LV, lymphatic vessel
- Lymph node
- Lymphatic system
- ODE, ordinary differential equation
- PDE, partial differential equation
- PDMS, polydimethylsiloxane
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Affiliation(s)
- Yufeng Shou
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
| | - Sarah C. Johnson
- Department of Bioengineering, Stanford University, CA, 94305, USA
- Department of Bioengineering, Imperial College London, South Kensington, SW72AZ, UK
| | - Ying Jie Quek
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
- Singapore Immunology Network, Agency for Science, Technology and Research, 138648, Singapore
| | - Xianlei Li
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
| | - Andy Tay
- Department of Biomedical Engineering, National University of Singapore, 117583, Singapore
- Institute for Health Innovation & Technology, National University of Singapore, 117599, Singapore
- NUS Tissue Engineering Program, National University of Singapore, 117510, Singapore
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7
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Deep Neural Networks for Neuro-oncology: Towards Patient Individualized Design of Chemo-Radiation Therapy for Glioblastoma Patients. J Biomed Inform 2022; 127:104006. [DOI: 10.1016/j.jbi.2022.104006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/04/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
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Soltani M. Capillary network formation and structure in a modified discrete mathematical model of angiogenesis. Biomed Phys Eng Express 2021; 8. [PMID: 34883475 DOI: 10.1088/2057-1976/ac4175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 12/09/2021] [Indexed: 01/01/2023]
Abstract
Angiogenesis, as part of cancer development, involves hierarchical complicated events and processes. Multiple studies have revealed the significance of the formation and structure of tumor-induced capillary networks. In this study, a discrete mathematical model of angiogenesis is studied and modified to capture the realistic physics of capillary network formation. Modifications are performed on the mathematical foundations of an existing discrete model of angiogenesis. The main modifications are the imposition of the matrix density effect, implementation of realistic boundary and initial conditions, and improvement of the method of governing equations based on physical observation. Results show that endothelial cells accelerate angiogenesis and capillary formation as they migrate toward the tumor and clearly exhibit the physical concept of haptotactic movement. On the other hand, consideration of blood flow-induced stress leads to a dynamic adaptive vascular network of capillaries which intelligibly reflects the brush border effect . The present modified model of capillary network formation is based on the physical rationale that defines a clear mathematical and physical interpretation of angiogenesis, which is likely to be used in cancer development modeling and anti-angiogenic therapies.
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Affiliation(s)
- M Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.,Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada.,Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, Ontario, Canada.,Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Tehran Province, Iran
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Peng GCY, Alber M, Tepole AB, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, Kuhl E. Multiscale modeling meets machine learning: What can we learn? ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2021; 28:1017-1037. [PMID: 34093005 PMCID: PMC8172124 DOI: 10.1007/s11831-020-09405-5] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 02/09/2020] [Indexed: 05/10/2023]
Abstract
Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.
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Affiliation(s)
| | - Mark Alber
- University of California, Riverside, USA
| | | | - William R Cannon
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Suvranu De
- Rensselaer Polytechnic Institute, Troy, New York, USA
| | | | | | | | | | | | - Linda Petzold
- University of California, Santa Barbara, California, USA
| | - Ellen Kuhl
- Stanford University, Stanford, California, USA
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10
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Mathematical modelling of the dynamics of prostate cancer with a curative vaccine. SCIENTIFIC AFRICAN 2021. [DOI: 10.1016/j.sciaf.2021.e00715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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Valentim CA, Rabi JA, David SA. Fractional Mathematical Oncology: On the potential of non-integer order calculus applied to interdisciplinary models. Biosystems 2021; 204:104377. [PMID: 33610556 DOI: 10.1016/j.biosystems.2021.104377] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/04/2021] [Accepted: 02/04/2021] [Indexed: 12/22/2022]
Abstract
Mathematical Oncology investigates cancer-related phenomena through mathematical models as comprehensive as possible. Accordingly, an interdisciplinary approach involving concepts from biology to materials science can provide a deeper understanding of biological systems pertaining the disease. In this context, fractional calculus (also referred to as non-integer order) is a branch in mathematical analysis whose tools can describe complex phenomena comprising different time and space scales. Fractional-order models may allow a better description and understanding of oncological particularities, potentially contributing to decision-making in areas of interest such as tumor evolution, early diagnosis techniques and personalized treatment therapies. By following a phenomenological (i.e. mechanistic) approach, the present study surveys and explores different aspects of Fractional Mathematical Oncology, reviewing and discussing recent developments in view of their prospective applications.
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Affiliation(s)
- Carlos A Valentim
- Department of Biosystems Engineering, University of São Paulo, Pirassununga Campus, Brazil.
| | - José A Rabi
- Department of Biosystems Engineering, University of São Paulo, Pirassununga Campus, Brazil.
| | - Sergio A David
- Department of Biosystems Engineering, University of São Paulo, Pirassununga Campus, Brazil.
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12
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients. J Biomed Inform 2021; 115:103694. [PMID: 33545332 DOI: 10.1016/j.jbi.2021.103694] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 01/30/2023]
Abstract
BACKGROUND AND OBJECTIVES Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and adjuvant Temozolomide (TMZ) constitute standard treatment of GBM. Existing models for GBM growth do not consider the effect of different schedules on tumor growth and patient survival. However, clinical trials show that treatment schedule and drug dosage significantly affect patient survival. The goal is to provide a patient calibrated model for predicting survival according to the treatment schedule. METHODS We propose a top-down method based on artificial neural networks (ANN) and genetic algorithm (GA) to predict survival of GBM patients. A feed forward undercomplete Autoencoder network is integrated with the neuro-evolutionary (NE) algorithm in order to extract a compressed representation of input clinical data. The proposed NE algorithm uses GA to obtain optimal architecture of a multi-layer perceptron (MLP). Taguchi L16 orthogonal design of experiments is used to tune parameters of the proposed NE algorithm. Finally, the optimal MLP is used to predict survival of GBM patients. RESULTS Data from 8 related clinical trials have been collected and integrated to train the model. From 847 evaluable cases, 719 were used for train and validation and the remaining 128 cases were used to test the model. Mean absolute error of the predictions on the test data is 0.087 months which shows excellent performance of the proposed model in predicting survival of the patients. Also, the results show that the proposed NE algorithm is superior to other existing models in both the mean and variability of the prediction error.
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Affiliation(s)
- Amir Ebrahimi Zade
- Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran
| | | | - M Soltani
- Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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Kim HS, Lee T, Yun J, Lee G, Hong Y. Cancer protein biomarker identification and quantification using nanoforest substrate and hand-held Raman spectrometer. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105632] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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14
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Reinforcement learning for optimal scheduling of Glioblastoma treatment with Temozolomide. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 193:105443. [PMID: 32311510 DOI: 10.1016/j.cmpb.2020.105443] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 02/17/2020] [Accepted: 03/08/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Glioblastoma multiforme (GBM) is the most frequent primary brain tumor in adults and Temozolomide (TMZ) is an effective chemotherapeutic agent for its treatment. In Silico models of GBM growth provide an appropriate foundation for analysis and comparison of different regimens. We propose a mathematical frame for patient specific design of optimal chemotherapy regimens for GBM patients. METHODS The proposed frame includes online interaction of a virtual GBM with an optimizing agent. Spatiotemporal dynamics of GBM growth and its response to TMZ are simulated with a three dimensional hybrid cellular automaton. Q learning is tailored to the virtual GBM for treatment optimization aimed at minimizing tumor size at the end of treatment course. Q learning consists of a learning agent that interacts with the virtual GBM. System state is affected by the agent decisions and the obtained rewards guide Q learning to the optimal schedule. RESULTS Computational results confirm that the optimal chemotherapy schedule depends on some patient specific parameters including body weight, tumor size and its position in the brain. Furthermore, the algorithm is used for scheduling 2100 mg of TMZ on a virtual GBM and the obtained schedule is to administer150 mg of TMZ every other day. The obtained schedule is compared to the standard 7/14 regimen and the results show that it is superior to the 7/14 regimen in minimizing tumor size. CONCLUSION The proposed frame is an appropriate decision support system for patient specific design of TMZ administration regimens on GBM patients. Also, since the obtained optimal schedule outperforms the standard 7/14 regimen, it is worthy of further clinical testing.
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Affiliation(s)
- Amir Ebrahimi Zade
- Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran
| | | | - Madjid Soltani
- Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1969764499, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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15
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Kourou K, Rigas G, Papaloukas C, Mitsis M, Fotiadis DI. Cancer classification from time series microarray data through regulatory Dynamic Bayesian Networks. Comput Biol Med 2019; 116:103577. [PMID: 32001012 DOI: 10.1016/j.compbiomed.2019.103577] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 12/05/2019] [Accepted: 12/05/2019] [Indexed: 01/05/2023]
Abstract
Genomic profiling of cancer studies has generated comprehensive gene expression patterns for diverse phenotypes. Computational methods which employ transcriptomics datasets have been proposed to model gene expression data. Dynamic Bayesian Networks (DBNs) have been used for modeling time series datasets and for the inference of regulatory networks. Furthermore, cancer classification through DBN-based approaches could reveal the importance of exploiting knowledge from statistically significant genes and key regulatory molecules. Although microarray datasets have been employed extensively by several classification methods for decision making, the use of new knowledge from the pathway level has not been addressed adequately in the literature in terms of DBNs for cancer classification. In the present study, we identify the genes that act as regulators and mediate the activity of transcription factors that have been found in all promoters of our differentially expressed gene sets. These features serve as potential priors for distinguishing tumor from normal samples using a DBN-based classification approach. We employed three microarray datasets from the Gene Expression Omnibus (GEO) public functional repository and performed differential expression analysis. Promoter and pathway analysis of the identified genes revealed the key regulators which influence the transcription mechanisms of these genes. We applied the DBN algorithm on selected genes and identified the features that can accurately classify the samples into tumors and controls. Both accuracy and Area Under the Curve (AUC) were high for the gene sets comprising of the differentially expressed genes along with their master regulators (accuracy: 70.8%-98.5%; AUC: 0.562-0.985).
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Affiliation(s)
- Konstantina Kourou
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, GR 45110, Greece; Dept. of Biological Applications and Technology, University of Ioannina, Ioannina, GR, 45110, Greece
| | - George Rigas
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, GR 45110, Greece
| | - Costas Papaloukas
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, GR 45110, Greece; Dept. of Biological Applications and Technology, University of Ioannina, Ioannina, GR, 45110, Greece
| | - Michalis Mitsis
- Dept. of Surgery and Cancer Biobank Center, School of Health Sciences, Faculty of Medicine, University of Ioannina, 45110, Ioannina, GR 45110, Greece
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, GR 45110, Greece; Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, GR 45110, Greece.
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16
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Mathematical modelling of cell migration. Essays Biochem 2019; 63:631-637. [PMID: 31654055 DOI: 10.1042/ebc20190020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 09/05/2019] [Accepted: 09/10/2019] [Indexed: 11/17/2022]
Abstract
The complexity of biological systems creates challenges for fully understanding their behaviour. This is particularly true for cell migration which requires the co-ordinated activity of hundreds of individual components within cells. Mathematical modelling can help understand these complex systems by breaking the system into discrete steps which can then be interrogated in silico. In this review, we highlight scenarios in cell migration where mathematical modelling can be applied and discuss what types of modelling are most suited. Almost any aspect of cell migration is amenable to mathematical modelling from the modelling of intracellular processes such as chemokine receptor signalling and actin filament branching to larger scale processes such as the movement of individual cells or populations of cells through their environment. Two common ways of approaching this modelling are the use of models based on differential equations or agent-based modelling. The application of both these approaches to cell migration are discussed with specific examples along with common software tools to facilitate the process for non-mathematicians. We also highlight the challenges of modelling cell migration and the need for rigorous experimental work to effectively parameterise a model.
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Nawn D, Chatterjee S, Anura A, Bag S, Chakraborty D, Pal M, Paul RR, Chatterjee J. Elucidation of Differential Nano-Textural Attributes for Normal Oral Mucosa and Pre-Cancer. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2019; 25:1224-1233. [PMID: 31526400 DOI: 10.1017/s1431927619014867] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Computational analysis on altered micro-nano-textural attributes of the oral mucosa may provide precise diagnostic information about oral potentially malignant disorders (OPMDs) instead of an existing handful of qualitative reports. This study evaluated micro-nano-textural features of oral epithelium from scanning electron microscopic (SEM) images and the sub-epithelial connective tissue from light microscopic (LM) and atomic force microscopic (AFM) images for normal and OPMD (namely oral sub-mucous fibrosis, i.e., OSF). Objective textural descriptors, namely discrete wavelet transform, gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP), were extracted and fed to standard classifiers. Best classification accuracy of 87.28 and 93.21%; sensitivity of 93 and 96%; specificity of 80 and 91% were achieved, respectively, for SEM and AFM. In the study groups, SEM analysis showed a significant (p < 0.01) variation for all the considered textural descriptors, while for AFM, a remarkable alteration (p < 0.01) was only found in GLCM and LBP. Interestingly, sub-epithelial collagen nanoscale and microscale textural information from AFM and LM images, respectively, were complementary, namely microlevel contrast was more in normal (0.251) than OSF (0.193), while nanolevel contrast was more in OSF (0.283) than normal (0.204). This work, thus, illustrated differential micro-nano-textural attributes for oral epithelium and sub-epithelium to distinguish OPMD precisely and may be contributory in early cancer diagnostics.
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Affiliation(s)
- Debaleena Nawn
- Advanced Technology Development Centre, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
| | - Saunak Chatterjee
- School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
| | - Anji Anura
- School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
| | - Swarnendu Bag
- Tata Medical Center, Kolkata 700160, West Bengal, India
| | - Debjani Chakraborty
- Department of Mathematics, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
| | - Mousumi Pal
- Guru Nanak Institute of Dental Sciences and Research, Kolkata 700114, West Bengal, India
| | - Ranjan Rashmi Paul
- Guru Nanak Institute of Dental Sciences and Research, Kolkata 700114, West Bengal, India
| | - Jyotirmoy Chatterjee
- School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
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Suhail Y, Cain MP, Vanaja K, Kurywchak PA, Levchenko A, Kalluri R, Kshitiz. Systems Biology of Cancer Metastasis. Cell Syst 2019; 9:109-127. [PMID: 31465728 PMCID: PMC6716621 DOI: 10.1016/j.cels.2019.07.003] [Citation(s) in RCA: 229] [Impact Index Per Article: 45.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 04/29/2019] [Accepted: 06/28/2019] [Indexed: 12/12/2022]
Abstract
Cancer metastasis is no longer viewed as a linear cascade of events but rather as a series of concurrent, partially overlapping processes, as successfully metastasizing cells assume new phenotypes while jettisoning older behaviors. The lack of a systemic understanding of this complex phenomenon has limited progress in developing treatments for metastatic disease. Because metastasis has traditionally been investigated in distinct physiological compartments, the integration of these complex and interlinked aspects remains a challenge for both systems-level experimental and computational modeling of metastasis. Here, we present some of the current perspectives on the complexity of cancer metastasis, the multiscale nature of its progression, and a systems-level view of the processes underlying the invasive spread of cancer cells. We also highlight the gaps in our current understanding of cancer metastasis as well as insights emerging from interdisciplinary systems biology approaches to understand this complex phenomenon.
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Affiliation(s)
- Yasir Suhail
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT, USA; Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT, USA
| | - Margo P Cain
- Department of Cancer Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Kiran Vanaja
- Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT, USA
| | - Paul A Kurywchak
- Department of Cancer Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Andre Levchenko
- Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT, USA
| | - Raghu Kalluri
- Department of Cancer Biology, MD Anderson Cancer Center, Houston, TX, USA
| | - Kshitiz
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT, USA; Cancer Systems Biology @ Yale (CaSB@Yale), Yale University, West Haven, CT, USA.
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Forster JC, Marcu LG, Bezak E. Approaches to combat hypoxia in cancer therapy and the potential for in silico models in their evaluation. Phys Med 2019; 64:145-156. [PMID: 31515013 DOI: 10.1016/j.ejmp.2019.07.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 06/17/2019] [Accepted: 07/09/2019] [Indexed: 02/07/2023] Open
Abstract
AIM The negative impact of tumour hypoxia on cancer treatment outcome has been long-known, yet there has been little success combating it. This paper investigates the potential role of in silico modelling to help test emerging hypoxia-targeting treatments in cancer therapy. METHODS A Medline search was undertaken on the current landscape of in silico models that simulate cancer therapy and evaluate their ability to test hypoxia-targeting treatments. Techniques and treatments to combat tumour hypoxia and their current challenges are also presented. RESULTS Hypoxia-targeting treatments include tumour reoxygenation, hypoxic cell radiosensitization with nitroimidazoles, hypoxia-activated prodrugs and molecular targeting. Their main challenges are toxicity and not achieving adequate delivery to hypoxic regions of the tumour. There is promising research toward combining two or more of these techniques. Different types of in silico therapy models have been developed ranging from temporal to spatial and from stochastic to deterministic models. Numerous models have compared the effectiveness of different radiotherapy fractionation schedules for controlling hypoxic tumours. Similarly, models could help identify and optimize new treatments for overcoming hypoxia that utilize novel hypoxia-targeting technology. CONCLUSION Current therapy models should attempt to incorporate more sophisticated modelling of tumour angiogenesis/vasculature and vessel perfusion in order to become more useful for testing hypoxia-targeting treatments, which typically rely upon the tumour vasculature for delivery of additional oxygen, (pro)drugs and nanoparticles.
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Affiliation(s)
- Jake C Forster
- SA Medical Imaging, Department of Nuclear Medicine, The Queen Elizabeth Hospital, Woodville South, SA 5011, Australia; Department of Physics, University of Adelaide, North Terrace, Adelaide SA 5005, Australia
| | - Loredana G Marcu
- Faculty of Science, University of Oradea, Oradea 410087, Romania; Cancer Research Institute and School of Health Sciences, University of South Australia, Adelaide SA 5001, Australia.
| | - Eva Bezak
- Department of Physics, University of Adelaide, North Terrace, Adelaide SA 5005, Australia; Cancer Research Institute and School of Health Sciences, University of South Australia, Adelaide SA 5001, Australia
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López-Marín N, Mulet R, Rodríguez R. Photodynamic therapy: Toward a systemic computational model. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY B-BIOLOGY 2018; 189:201-213. [PMID: 30396131 DOI: 10.1016/j.jphotobiol.2018.10.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 10/03/2018] [Accepted: 10/25/2018] [Indexed: 01/13/2023]
Abstract
We have designed a systemic model to understand the effect of Photodynamic Therapy (PDT) on long time scales. The model takes into account cell necrosis due to oxygen reactive species, cell apoptosis through the caspase pathway and the competition between healthy and tumor cells. We attempted to describe the system using state of the art computational techniques (necrosis and apoptosis) and simple models that allow a deeper understanding of the long time scale processes involved (healing and tumor growth). We analyzed the influence of the surface and tumor depth on the effectiveness of different treatment plans and we proposed, for the set of parameters used in this work, an optimum timing between sessions of PDT.
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Affiliation(s)
- N López-Marín
- Group of Complex Systems and Statistical Physics, Department of General Physics, Physics Faculty, University of Havana, La Habana, CP 10400, Cuba.
| | - R Mulet
- Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, Physics Faculty, University of Havana, La Habana, CP 10400, Cuba.
| | - R Rodríguez
- Department of Computational Medicine, National Institute of Nephrology, La Habana CP 10600, Cuba
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Oduola WO, Li X. Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System. Cancer Inform 2018; 17:1176935118790262. [PMID: 30083052 PMCID: PMC6073835 DOI: 10.1177/1176935118790262] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 06/16/2018] [Indexed: 12/16/2022] Open
Abstract
Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.
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Affiliation(s)
- Wasiu Opeyemi Oduola
- Department of Electrical and Computer Engineering (ECE), Prairie View A&M University, Prairie View, TX, USA
| | - Xiangfang Li
- Department of Electrical and Computer Engineering (ECE), Prairie View A&M University, Prairie View, TX, USA
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Norton KA, Jin K, Popel AS. Modeling triple-negative breast cancer heterogeneity: Effects of stromal macrophages, fibroblasts and tumor vasculature. J Theor Biol 2018; 452:56-68. [PMID: 29750999 DOI: 10.1016/j.jtbi.2018.05.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 04/13/2018] [Accepted: 05/03/2018] [Indexed: 12/20/2022]
Abstract
A hallmark of breast tumors is its spatial heterogeneity that includes its distribution of cancer stem cells and progenitor cells, but also heterogeneity in the tumor microenvironment. In this study we focus on the contributions of stromal cells, specifically macrophages, fibroblasts, and endothelial cells on tumor progression. We develop a computational model of triple-negative breast cancer based on our previous work and expand it to include macrophage infiltration, fibroblasts, and angiogenesis. In vitro studies have shown that the secretomes of tumor-educated macrophages and fibroblasts increase both the migration and proliferation rates of triple-negative breast cancer cells. In vivo studies also demonstrated that blocking signaling of selected secreted factors inhibits tumor growth and metastasis in mouse xenograft models. We investigate the influences of increased migration and proliferation rates on tumor growth, the effect of the presence on fibroblasts or macrophages on growth and morphology, and the contributions of macrophage infiltration on tumor growth. We find that while the presence of macrophages increases overall tumor growth, the increase in macrophage infiltration does not substantially increase tumor growth and can even stifle tumor growth at excessive rates.
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Affiliation(s)
| | - Kideok Jin
- Department of Biomedical Engineering; Department of Pharmaceutical Science, Albany College of Pharmacy and Health Science, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering; Department of Oncology and the Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, USA
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