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Short WD, Olutoye OO, Padon BW, Parikh UM, Colchado D, Vangapandu H, Shams S, Chi T, Jung JP, Balaji S. Advances in non-invasive biosensing measures to monitor wound healing progression. Front Bioeng Biotechnol 2022; 10:952198. [PMID: 36213059 PMCID: PMC9539744 DOI: 10.3389/fbioe.2022.952198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/12/2022] [Indexed: 01/09/2023] Open
Abstract
Impaired wound healing is a significant financial and medical burden. The synthesis and deposition of extracellular matrix (ECM) in a new wound is a dynamic process that is constantly changing and adapting to the biochemical and biomechanical signaling from the extracellular microenvironments of the wound. This drives either a regenerative or fibrotic and scar-forming healing outcome. Disruptions in ECM deposition, structure, and composition lead to impaired healing in diseased states, such as in diabetes. Valid measures of the principal determinants of successful ECM deposition and wound healing include lack of bacterial contamination, good tissue perfusion, and reduced mechanical injury and strain. These measures are used by wound-care providers to intervene upon the healing wound to steer healing toward a more functional phenotype with improved structural integrity and healing outcomes and to prevent adverse wound developments. In this review, we discuss bioengineering advances in 1) non-invasive detection of biologic and physiologic factors of the healing wound, 2) visualizing and modeling the ECM, and 3) computational tools that efficiently evaluate the complex data acquired from the wounds based on basic science, preclinical, translational and clinical studies, that would allow us to prognosticate healing outcomes and intervene effectively. We focus on bioelectronics and biologic interfaces of the sensors and actuators for real time biosensing and actuation of the tissues. We also discuss high-resolution, advanced imaging techniques, which go beyond traditional confocal and fluorescence microscopy to visualize microscopic details of the composition of the wound matrix, linearity of collagen, and live tracking of components within the wound microenvironment. Computational modeling of the wound matrix, including partial differential equation datasets as well as machine learning models that can serve as powerful tools for physicians to guide their decision-making process are discussed.
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Affiliation(s)
- Walker D. Short
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
| | - Oluyinka O. Olutoye
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
| | - Benjamin W. Padon
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
| | - Umang M. Parikh
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
| | - Daniel Colchado
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
| | - Hima Vangapandu
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
| | - Shayan Shams
- Department of Applied Data Science, San Jose State University, San Jose, CA, United States
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States
| | - Taiyun Chi
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States
| | - Jangwook P. Jung
- Department of Biological Engineering, Louisiana State University, Baton Rouge, LA, United States
| | - Swathi Balaji
- Laboratory for Regenerative Tissue Repair, Division of Pediatric Surgery, Department of Surgery, Texas Children’s Hospital and Baylor College of Medicine, Houston, TX, United States
- *Correspondence: Swathi Balaji,
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Bekiryazici Z, Merdan M, Kesemen T. Modification of the random differential transformation method and its applications to compartmental models. COMMUN STAT-THEOR M 2020. [DOI: 10.1080/03610926.2020.1713372] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Zafer Bekiryazici
- Department of Mathematics, Recep Tayyip Erdogan University, Rize, Turkey
| | - Mehmet Merdan
- Department of Mathematical Engineering, Gumushane University, Gumushane, Turkey
| | - Tulay Kesemen
- Department of Mathematics, Karadeniz Technical University, Trabzon, Turkey
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