Imaging approaches in functional assessment of implantable myogenic biomaterials and engineered muscle tissue

Edmunds, Kyle J.; Gargiulo, Paolo

doi:10.4081/bam.2015.2.63

Cited by 10 publications

(3 citation statements)

References 105 publications

(145 reference statements)

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“…The concomitant loss of muscle mass and increase in adipose tissue in aging individuals suggest the use of quantitative imaging techniques, such as X-ray computed tomography (CT) or magnetic resonance imaging (MRI) to characterize overall changes in skeletal muscle [19][20][21] . Indeed, another defining characteristic of aging is the loss of muscle strength from both the reduction of dense contractile myofibers and the infiltration of non-contractile adipose tissue -a phenomenon known as myosteatosis 22 .…”

mentioning

confidence: 99%

Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions

Ricciardi

Edmunds

Recenti

et al. 2020

Sci Rep

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The nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT distributions was recently developed and assessed for the quantification of lower extremity function and nutritional parameters in aging subjects. However, the use of the NTRA method for building predictive models of cardiovascular health was not explored; in this regard, the present study reports the use of NTRA parameters for classifying elderly subjects with coronary heart disease (CHD), cardiovascular disease (CVD), and chronic heart failure (CHF) using multivariate logistic regression and three tree-based machine learning (ML) algorithms. Results from each model were assembled as a typology of four classification metrics: total classification score, classification by tissue type, tissuebased feature importance, and classification by age. The predictive utility of this method was modelled using CHF incidence data. ML models employing the random forests algorithm yielded the highest classification performance for all analyses, and overall classification scores for all three conditions were excellent: CHD (AUCROC: 0.936); CVD (AUCROC: 0.914); CHF (AUCROC: 0.994). Longitudinal assessment for modelling the prediction of CHF incidence was likewise robust (AUCROC: 0.993). The present work introduces a substantial step forward in the construction of non-invasive, standardizable tools for associating adipose, loose connective, and lean tissue changes with cardiovascular health outcomes in elderly individuals.The progressive degeneration of skeletal muscle is consistently identified as an independent risk factor for significant morbidity, disability, and mortality in aging individuals 1-7 . Defined as sarcopenia, recent literature has interrogated its mediating and moderating roles in a wide range of adverse health outcomes, including its role in the etiology of cardiovascular pathophysiology. Catabolic inflammatory cytokine production and characteristic adiposity from the progression of sarcopenia have been linked with the onset of diabetes 8 , hypertension 9 , and dyslipidemia 10 -all of which are well-established risk factors for coronary heart disease (CHD) 11 and all-type cardiovascular disease (CVD) [12][13][14][15] . Chronic heart failure (CHF) patients frequently develop cardiac cachexia, a similar muscle wasting condition whose advanced stage has been implicated as an accelerated analogue of sarcopenic muscle degeneration 16 . Indeed, the progression of sarcopenia in older CHF patients may be considerably entangled with embedded cachexic effects 16,17 . While literature cites the associations and potential causal mechanisms between cardiovascular pathophysiology and downstream changes in skeletal muscle form and function 18 , validating standardized predictive models for these conditions remains debated. Furthermore, incorporating more nuanced quantitative methods for the non-invasive prediction of these events remains a priority in literature. Identifying such a methodology would further establish the generalizability of ...

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confidence: 99%

Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions

Ricciardi

Edmunds

Recenti

et al. 2020

Sci Rep

Self Cite

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“…Several researchers have used myosin SHG to examine native skeletal muscle, extracting measures of sarcomeric spacing correlated to contractile function. 13,[23][24][25] In one such study, muscle biopsies from healthy patients and those with Pompe disease were analyzed by myosin SHG. 23 Pompe disease is characterized by enlarged, glycogen-filled lysosomes and autophagic debris in muscle fibers, resulting in widespread sarcomere disruption.…”

Section: Introductionmentioning

confidence: 99%

“…Further, the majority of published data examines native tissues, not engineered tissues. 9,10,13,23,24 Thus, there exists a lack of knowledge about label-free, nonlinear OMI in engineered tissues. In this study, we combine these two complementary OMI methods to obtain RR values that are indicative of metabolic activity and SHG of structural organization as measures of the overall integrity of our tissue-engineered skeletal muscle units (SMUs).…”

Section: Introductionmentioning

confidence: 99%

Quantitative, Label-Free Evaluation of Tissue-Engineered Skeletal Muscle Through Multiphoton Microscopy

Syverud

Mycek²,

Larkin³

2017

Tissue Engineering Part C: Methods

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The lack of tools for assessing engineered tissue viability and function in a noninvasive manner is a major regulatory and translational challenge facing tissue engineers. Label-free, nonlinear optical molecular imaging (OMI) has utilized endogenous nicotinamide adenine dinucleotide and flavin adenine dinucleotide fluorescence to indicate metabolic activity. Similarly, second harmonic generation (SHG) signals from myosin and collagen can measure overall muscle structural integrity and function. The purpose of this study was to demonstrate these OMI techniques for the first time in engineered skeletal muscle and to develop a novel method for evaluating our engineered skeletal muscle units (SMUs) before implantation. Three experimental groups were studied: Control, Steroid Supplemented, and Metabolically Stressed SMUs. After imaging and analysis in ImageJ, a redox ratio (RR) metric was calculated to indicate metabolic activity, and a structure ratio metric was calculated to reflect structural composition. In addition, function was evaluated as tetanic force production in response to electrical stimulation. In living tissues, the RRs successfully distinguished control and metabolically stressed SMUs in both monolayer and 3D form. OMI of myosin and collagen SHG similarly differentiated control SMUs from the steroid supplemented group. With respect to function, steroid supplementation significantly increased active force generation. When comparing functional and OMI measures, a significant correlation was present between overall myosin density and active force generation. This work demonstrates the potential for using label-free OMI to evaluate tissue-engineered skeletal muscle constructs. The positive correlation between structural OMI measures and force production suggests that OMI could potentially serve as an accurate predictor of functional behaviors, such as integration and tissue regeneration, after implantation. This noninvasive OMI methodology, demonstrated for the first time in engineered skeletal muscle, could prove invaluable for assessing our tissue engineering technology and confirming release criteria for validation.

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Muscle Fiber Regeneration in Long-Term Denervated Muscles: Basics and Clinical Perspectives

Carraro

Kern

Zampieri

et al. 2019

Regenerative Medicine and Plastic Surgery

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Imaging approaches in functional assessment of implantable myogenic biomaterials and engineered muscle tissue

Cited by 10 publications

References 105 publications

Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions

Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions

Quantitative, Label-Free Evaluation of Tissue-Engineered Skeletal Muscle Through Multiphoton Microscopy

Muscle Fiber Regeneration in Long-Term Denervated Muscles: Basics and Clinical Perspectives

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