Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Advances in Non-invasive Biosensing Measures to Monitor Wound Healing Progression

Walker Short
* ORCID logo ,
Oluyinka Olutoye
,
Benjamin Padon
,
Umang Parikh
,
Daniel Colchado
,
Hima Vangapandu
,
Shayan Shams
,
Taiyun Chi
,
Jangwook Jung
,
Swathi Balaji
*
Version 1 : Received: 18 March 2022 / Approved: 22 March 2022 / Online: 22 March 2022 (03:50:47 CET)

How to cite: Short, W.; Olutoye, O.; Padon, B.; Parikh, U.; Colchado, D.; Vangapandu, H.; Shams, S.; Chi, T.; Jung, J.; Balaji, S. Advances in Non-invasive Biosensing Measures to Monitor Wound Healing Progression. Preprints 2022, 2022030293. https://doi.org/10.20944/preprints202203.0293.v1 Short, W.; Olutoye, O.; Padon, B.; Parikh, U.; Colchado, D.; Vangapandu, H.; Shams, S.; Chi, T.; Jung, J.; Balaji, S. Advances in Non-invasive Biosensing Measures to Monitor Wound Healing Progression. Preprints 2022, 2022030293. https://doi.org/10.20944/preprints202203.0293.v1

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 include bacterial contamination, tissue perfusion, and 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 non-invasive detection of biologic and physiologic factors of the healing wound, visualizing and modeling the ECM, and computational tools to efficiently evaluate the complex data acquired from the wounds 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. 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 matrix, linearity of collagen, and live tracking of components within the ECM. Computational modeling of the 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.

Keywords

extracellular matrix; biosensing; machine learning; wound healing

Subject

Medicine and Pharmacology, Pulmonary and Respiratory Medicine

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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