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Hashemi A, Ezati M, Nasr MP, Zumberg I, Provaznik V. Extracellular Vesicles and Hydrogels: An Innovative Approach to Tissue Regeneration. ACS OMEGA 2024; 9:6184-6218. [PMID: 38371801 PMCID: PMC10870307 DOI: 10.1021/acsomega.3c08280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/27/2023] [Accepted: 12/19/2023] [Indexed: 02/20/2024]
Abstract
Extracellular vesicles have emerged as promising tools in regenerative medicine due to their inherent ability to facilitate intercellular communication and modulate cellular functions. These nanosized vesicles transport bioactive molecules, such as proteins, lipids, and nucleic acids, which can affect the behavior of recipient cells and promote tissue regeneration. However, the therapeutic application of these vesicles is frequently constrained by their rapid clearance from the body and inability to maintain a sustained presence at the injury site. In order to overcome these obstacles, hydrogels have been used as extracellular vesicle delivery vehicles, providing a localized and controlled release system that improves their therapeutic efficacy. This Review will examine the role of extracellular vesicle-loaded hydrogels in tissue regeneration, discussing potential applications, current challenges, and future directions. We will investigate the origins, composition, and characterization techniques of extracellular vesicles, focusing on recent advances in exosome profiling and the role of machine learning in this field. In addition, we will investigate the properties of hydrogels that make them ideal extracellular vesicle carriers. Recent studies utilizing this combination for tissue regeneration will be highlighted, providing a comprehensive overview of the current research landscape and potential future directions.
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Affiliation(s)
- Amir Hashemi
- Department
of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082/12, 61600 Brno, Czech Republic
| | - Masoumeh Ezati
- Department
of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082/12, 61600 Brno, Czech Republic
| | - Minoo Partovi Nasr
- Department
of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082/12, 61600 Brno, Czech Republic
| | - Inna Zumberg
- Department
of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082/12, 61600 Brno, Czech Republic
| | - Valentine Provaznik
- Department
of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3082/12, 61600 Brno, Czech Republic
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Gong D, Ben-Akiva E, Singh A, Yamagata H, Est-Witte S, Shade JK, Trayanova NA, Green JJ. Machine learning guided structure function predictions enable in silico nanoparticle screening for polymeric gene delivery. Acta Biomater 2022; 154:349-358. [PMID: 36206976 PMCID: PMC11185862 DOI: 10.1016/j.actbio.2022.09.072] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 09/10/2022] [Accepted: 09/28/2022] [Indexed: 12/14/2022]
Abstract
Developing highly efficient non-viral gene delivery reagents is still difficult for many hard-to-transfect cell types and, to date, has mostly been conducted via brute force screening routines. High throughput in silico methods of evaluating biomaterials can enable accelerated optimization and development of devices or therapeutics by exploring large chemical design spaces quickly and at low cost. This work reports application of state-of-the-art machine learning algorithms to a dataset of synthetic biodegradable polymers, poly(beta-amino ester)s (PBAEs), which have shown exciting promise for therapeutic gene delivery in vitro and in vivo. The data set includes polymer properties as inputs as well as polymeric nanoparticle transfection performance and nanoparticle toxicity in a range of cells as outputs. This data was used to train and evaluate several state-of-the-art machine learning algorithms for their ability to predict transfection and understand structure-function relationships. By developing an encoding scheme for vectorizing the structure of a PBAE polymer in a machine-readable format, we demonstrate that a random forest model can satisfactorily predict DNA transfection in vitro based on the chemical structure of the constituent PBAE polymer in a cell line dependent manner. Based on the model, we synthesized PBAE polymers and used them to form polymeric gene delivery nanoparticles that were predicted in silico to be successful. We validated the computational predictions in two cell lines in vitro, RAW 264.7 macrophages and Hep3B liver cancer cells, and found that the Spearman's R correlation between predicted and experimental transfection was 0.57 and 0.66 respectively. Thus, a computational approach that encoded chemical descriptors of polymers was able to demonstrate that in silico computational screening of polymeric nanomedicine compositions had utility in predicting de novo biological experiments. STATEMENT OF SIGNIFICANCE: Developing highly efficient non-viral gene delivery reagents is difficult for many hard-to-transfect cell types and, to date, has mostly been explored via brute force screening routines. High throughput in silico methods of evaluating biomaterials can enable accelerated optimization and development for therapeutic or biomanufacturing purposes by exploring large chemical design spaces quickly and at low cost. This work reports application of state-of-the-art machine learning algorithms to a large compiled PBAE DNA gene delivery nanoparticle dataset across many cell types to develop predictive models for transfection and nanoparticle cytotoxicity. We develop a novel computational pipeline to encode PBAE nanoparticles with chemical descriptors and demonstrate utility in a de novo experimental context.
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Affiliation(s)
- Dennis Gong
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Elana Ben-Akiva
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Arshdeep Singh
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Hannah Yamagata
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Savannah Est-Witte
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Julie K Shade
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Jordan J Green
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD 21205, USA; Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
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Suwardi A, Wang F, Xue K, Han MY, Teo P, Wang P, Wang S, Liu Y, Ye E, Li Z, Loh XJ. Machine Learning-Driven Biomaterials Evolution. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2102703. [PMID: 34617632 DOI: 10.1002/adma.202102703] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 07/09/2021] [Indexed: 06/13/2023]
Abstract
Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to achieve desired biological responses. While there is constant evolution and innovation in materials with time, biomaterials research has been hampered by the relatively long development period required. In recent years, driven by the need to accelerate materials development, the applications of machine learning in materials science has progressed in leaps and bounds. The combination of machine learning with high-throughput theoretical predictions and high-throughput experiments (HTE) has shifted the traditional Edisonian (trial and error) paradigm to a data-driven paradigm. In this review, each type of biomaterial and their key properties and use cases are systematically discussed, followed by how machine learning can be applied in the development and design process. The discussions are classified according to various types of materials used including polymers, metals, ceramics, and nanomaterials, and implants using additive manufacturing. Last, the current gaps and potential of machine learning to further aid biomaterials discovery and application are also discussed.
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Affiliation(s)
- Ady Suwardi
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - FuKe Wang
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Kun Xue
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Ming-Yong Han
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Peili Teo
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Pei Wang
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Shijie Wang
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Ye Liu
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Enyi Ye
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Zibiao Li
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Xian Jun Loh
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
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Kim J, Mirando AC, Popel AS, Green JJ. Gene delivery nanoparticles to modulate angiogenesis. Adv Drug Deliv Rev 2017; 119:20-43. [PMID: 27913120 PMCID: PMC5449271 DOI: 10.1016/j.addr.2016.11.003] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2016] [Revised: 10/01/2016] [Accepted: 11/24/2016] [Indexed: 01/19/2023]
Abstract
Angiogenesis is naturally balanced by many pro- and anti-angiogenic factors while an imbalance of these factors leads to aberrant angiogenesis, which is closely associated with many diseases. Gene therapy has become a promising strategy for the treatment of such a disordered state through the introduction of exogenous nucleic acids that express or silence the target agents, thereby engineering neovascularization in both directions. Numerous non-viral gene delivery nanoparticles have been investigated towards this goal, but their clinical translation has been hampered by issues associated with safety, delivery efficiency, and therapeutic effect. This review summarizes key factors targeted for therapeutic angiogenesis and anti-angiogenesis gene therapy, non-viral nanoparticle-mediated approaches to gene delivery, and recent gene therapy applications in pre-clinical and clinical trials for ischemia, tissue regeneration, cancer, and wet age-related macular degeneration. Enhanced nanoparticle design strategies are also proposed to further improve the efficacy of gene delivery nanoparticles to modulate angiogenesis.
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Affiliation(s)
- Jayoung Kim
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Translational Tissue Engineering Center and Institute for Nanobiotechnology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Adam C Mirando
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Department of Oncology and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Department of Oncology and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Jordan J Green
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Translational Tissue Engineering Center and Institute for Nanobiotechnology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Department of Oncology and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Departments of Ophthalmology, Neurosurgery, and Materials Science & Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
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Developing a Suitable Model for Water Uptake for Biodegradable Polymers Using Small Training Sets. Int J Biomater 2016; 2016:6273414. [PMID: 27200091 PMCID: PMC4856915 DOI: 10.1155/2016/6273414] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 04/03/2016] [Indexed: 11/24/2022] Open
Abstract
Prediction of the dynamic properties of water uptake across polymer libraries can accelerate polymer selection for a specific application. We first built semiempirical models using Artificial Neural Networks and all water uptake data, as individual input. These models give very good correlations (R2 > 0.78 for test set) but very low accuracy on cross-validation sets (less than 19% of experimental points within experimental error). Instead, using consolidated parameters like equilibrium water uptake a good model is obtained (R2 = 0.78 for test set), with accurate predictions for 50% of tested polymers. The semiempirical model was applied to the 56-polymer library of L-tyrosine-derived polyarylates, identifying groups of polymers that are likely to satisfy design criteria for water uptake. This research demonstrates that a surrogate modeling effort can reduce the number of polymers that must be synthesized and characterized to identify an appropriate polymer that meets certain performance criteria.
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Groen N, Guvendiren M, Rabitz H, Welsh WJ, Kohn J, de Boer J. Stepping into the omics era: Opportunities and challenges for biomaterials science and engineering. Acta Biomater 2016; 34:133-142. [PMID: 26876875 DOI: 10.1016/j.actbio.2016.02.015] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Revised: 01/22/2016] [Accepted: 02/10/2016] [Indexed: 12/11/2022]
Abstract
The research paradigm in biomaterials science and engineering is evolving from using low-throughput and iterative experimental designs towards high-throughput experimental designs for materials optimization and the evaluation of materials properties. Computational science plays an important role in this transition. With the emergence of the omics approach in the biomaterials field, referred to as materiomics, high-throughput approaches hold the promise of tackling the complexity of materials and understanding correlations between material properties and their effects on complex biological systems. The intrinsic complexity of biological systems is an important factor that is often oversimplified when characterizing biological responses to materials and establishing property-activity relationships. Indeed, in vitro tests designed to predict in vivo performance of a given biomaterial are largely lacking as we are not able to capture the biological complexity of whole tissues in an in vitro model. In this opinion paper, we explain how we reached our opinion that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field. STATEMENT OF SIGNIFICANCE In this opinion paper, we postulate that converging genomics and materiomics into a new field would enable a significant acceleration of the development of new and improved medical devices. The use of computational modeling to correlate high-throughput gene expression profiling with high throughput combinatorial material design strategies would add power to the analysis of biological effects induced by material properties. We believe that this extra layer of complexity on top of high-throughput material experimentation is necessary to tackle the biological complexity and further advance the biomaterials field.
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Affiliation(s)
- Nathalie Groen
- Department of Tissue Regeneration, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Murat Guvendiren
- New Jersey Center for Biomaterials, Rutgers University, Piscataway, NJ, USA
| | - Herschel Rabitz
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - William J Welsh
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
| | - Joachim Kohn
- New Jersey Center for Biomaterials, Rutgers University, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, New Jersey Center for Biomaterials, Rutgers University, Piscataway, NJ, USA
| | - Jan de Boer
- Department of Tissue Regeneration, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
- cBITE Lab, Merln Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, The Netherlands
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Molla MR, Levkin PA. Combinatorial Approach to Nanoarchitectonics for Nonviral Delivery of Nucleic Acids. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2016; 28:1159-1175. [PMID: 26608939 DOI: 10.1002/adma.201502888] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Revised: 09/01/2015] [Indexed: 06/05/2023]
Abstract
Nanoparticles based on cationic polymers, lipids or lipidoids are of great interest in the field of gene delivery applications. The research on these nanosystems is rapidly growing as they hold promise to treat wide variety of human diseases ranging from viral infections to genetic disorders and cancer. Recently, combinatorial design principles have been adopted for rapid generation of large numbers of chemically diverse polymers and lipids capable of forming multifunctional nanocarriers for the use in gene delivery applications. At the same time, current high-throughput screening systems as well as convenient cell assays and readout techniques allow for fast evaluation of cell transfection efficiencies and toxicities of libraries of novel gene delivery agents. This allows for a rapid evaluation of structure-function relationship as well as identification of novel efficient nanocarriers for cell transfection and gene therapy. Here, the recent contribution of high-throughput synthesis to the development of novel nanocarriers for gene delivery applications is described.
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Affiliation(s)
- Mijanur Rahaman Molla
- Institute of Toxicology and Genetics, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Pavel A Levkin
- Institute of Toxicology and Genetics, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- University of Heidelberg, Department of Applied Physical Chemistry, 69120, Heidelberg, Germany
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Gubskaya AV, Khan IJ, Valenzuela LM, Lisnyak YV, Kohn J. Investigating the Release of a Hydrophobic Peptide from Matrices of Biodegradable Polymers: An Integrated Method Approach. POLYMER 2013; 54:3806-3820. [PMID: 24039300 DOI: 10.1016/j.polymer.2013.05.038] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
The objectives of this work were: (1) to select suitable compositions of tyrosine-derived polycarbonates for controlled delivery of voclosporin, a potent drug candidate to treat ocular diseases, (2) to establish a structure-function relationship between key molecular characteristics of biodegradable polymer matrices and drug release kinetics, and (3) to identify factors contributing in the rate of drug release. For the first time, the experimental study of polymeric drug release was accompanied by a hierarchical sequence of three computational methods. First, suitable polymer compositions used in subsequent neural network modeling were determined by means of response surface methodology (RSM). Second, accurate artificial neural network (ANN) models were built to predict drug release profiles for fifteen polymers located outside the initial design space. Finally, thermodynamic properties and hydrogen-bonding patterns of model drug-polymer complexes were studied using molecular dynamics (MD) technique to elucidate a role of specific interactions in drug release mechanism. This research presents further development of methodological approaches to meet challenges in the design of polymeric drug delivery systems.
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Affiliation(s)
- Anna V Gubskaya
- New Jersey Center for Biomaterials, Rutgers, The State University of New Jersey, Piscataway, NJ 08854-8087, USA
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Chikalov I, Lozin V, Lozina I, Moshkov M, Nguyen HS, Skowron A, Zielosko B. Logical Analysis of Data: Theory, Methodology and Applications. INTELLIGENT SYSTEMS REFERENCE LIBRARY 2013. [DOI: 10.1007/978-3-642-28667-4_3] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
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