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Temporal blood flow changes measured by diffuse correlation tomography predict murine femoral graft healing

Blood flow changes during bone graft healing have the potential to provide important information about graft success, as the nutrients, oxygen, circulating cells and growth factors essential for integration are delivered by blood. However, longitudinal monitoring of blood flow changes during graft h...

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Autores principales: Han, Songfeng, Proctor, Ashley R., Ren, Jingxuan, Benoit, Danielle S. W., Choe, Regine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5973582/
https://www.ncbi.nlm.nih.gov/pubmed/29813078
http://dx.doi.org/10.1371/journal.pone.0197031
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author Han, Songfeng
Proctor, Ashley R.
Ren, Jingxuan
Benoit, Danielle S. W.
Choe, Regine
author_facet Han, Songfeng
Proctor, Ashley R.
Ren, Jingxuan
Benoit, Danielle S. W.
Choe, Regine
author_sort Han, Songfeng
collection PubMed
description Blood flow changes during bone graft healing have the potential to provide important information about graft success, as the nutrients, oxygen, circulating cells and growth factors essential for integration are delivered by blood. However, longitudinal monitoring of blood flow changes during graft healing has been a challenge due to limitations in current techniques. To this end, non-invasive diffuse correlation tomography (DCT) was investigated to enable longitudinal monitoring of three-dimensional blood flow changes in deep tissue. Specific to this study, longitudinal blood flow changes were utilized to predict healing outcomes of common interventions for massive bone defects using a common mouse femoral defect model. Weekly blood flow changes were non-invasively measured using a diffuse correlation tomography system for 9 weeks in three types of grafts: autografts (N = 7), allografts (N = 6) and tissue-engineered allografts (N = 6). Healing outcomes were quantified using an established torsion testing method 9 weeks after transplantation. Analysis of the spatial and temporal blood flow reveals that major differences among the three groups were captured in weeks 1–5 after graft transplantation. A multivariate model to predict maximum torque by relative blood flow changes over 5 weeks after graft transplantation was built using partial least squares regression. The results reveal lower bone strength correlates with greater cumulative blood flow over an extended period of time (i.e., 1–5 weeks). The current research demonstrates that DCT-measured blood flow changes after graft transplantation can be utilized to predict long-term healing outcomes in a mouse femoral graft model.
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spelling pubmed-59735822018-06-08 Temporal blood flow changes measured by diffuse correlation tomography predict murine femoral graft healing Han, Songfeng Proctor, Ashley R. Ren, Jingxuan Benoit, Danielle S. W. Choe, Regine PLoS One Research Article Blood flow changes during bone graft healing have the potential to provide important information about graft success, as the nutrients, oxygen, circulating cells and growth factors essential for integration are delivered by blood. However, longitudinal monitoring of blood flow changes during graft healing has been a challenge due to limitations in current techniques. To this end, non-invasive diffuse correlation tomography (DCT) was investigated to enable longitudinal monitoring of three-dimensional blood flow changes in deep tissue. Specific to this study, longitudinal blood flow changes were utilized to predict healing outcomes of common interventions for massive bone defects using a common mouse femoral defect model. Weekly blood flow changes were non-invasively measured using a diffuse correlation tomography system for 9 weeks in three types of grafts: autografts (N = 7), allografts (N = 6) and tissue-engineered allografts (N = 6). Healing outcomes were quantified using an established torsion testing method 9 weeks after transplantation. Analysis of the spatial and temporal blood flow reveals that major differences among the three groups were captured in weeks 1–5 after graft transplantation. A multivariate model to predict maximum torque by relative blood flow changes over 5 weeks after graft transplantation was built using partial least squares regression. The results reveal lower bone strength correlates with greater cumulative blood flow over an extended period of time (i.e., 1–5 weeks). The current research demonstrates that DCT-measured blood flow changes after graft transplantation can be utilized to predict long-term healing outcomes in a mouse femoral graft model. Public Library of Science 2018-05-29 /pmc/articles/PMC5973582/ /pubmed/29813078 http://dx.doi.org/10.1371/journal.pone.0197031 Text en © 2018 Han et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Han, Songfeng
Proctor, Ashley R.
Ren, Jingxuan
Benoit, Danielle S. W.
Choe, Regine
Temporal blood flow changes measured by diffuse correlation tomography predict murine femoral graft healing
title Temporal blood flow changes measured by diffuse correlation tomography predict murine femoral graft healing
title_full Temporal blood flow changes measured by diffuse correlation tomography predict murine femoral graft healing
title_fullStr Temporal blood flow changes measured by diffuse correlation tomography predict murine femoral graft healing
title_full_unstemmed Temporal blood flow changes measured by diffuse correlation tomography predict murine femoral graft healing
title_short Temporal blood flow changes measured by diffuse correlation tomography predict murine femoral graft healing
title_sort temporal blood flow changes measured by diffuse correlation tomography predict murine femoral graft healing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5973582/
https://www.ncbi.nlm.nih.gov/pubmed/29813078
http://dx.doi.org/10.1371/journal.pone.0197031
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