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Biology subjects

Earl, C. C.

Publications and source records attributed to Earl, C. C..

2 recordsLinked to original sources

Functional cardiac consequences of β-adrenergic stress-induced injury in the mdx mouse model of Duchenne muscular dystrophy

Cardiomyopathy is the leading cause of death in Duchenne muscular dystrophy (DMD), however, in the mdx mouse model of DMD, the cardiac phenotype differs from that seen in DMD-associated cardiomyopathy. Although some have used pharmacologic stress to enhance the cardiac phenotype in the mdx model, many methods lead to high mortality, variable cardiac outcomes, and do not recapitulate the structural and functional cardiac changes seen in human disease. Here, we describe a simple and effective method to enhance the cardiac phenotype model in mdx mice using advanced 2D and 4D high-frequency ultrasound to monitor cardiac dysfunction progression in vivo. For our study, mdx and wild-type (WT) mice received daily low-dose (2 mg/kg/day) isoproterenol injections for 10 days. Histopathologic assessment showed that isoproterenol treatment increased myocyte injury, elevated serum cardiac troponin I levels, and enhanced fibrosis in mdx mice. Ultrasound revealed reduced ventricular function, decreased wall thickness, increased volumes, and diminished cardiac reserve in mdx mice compared to wild-type. Our findings highlight the utility of low-dose isoproterenol in mdx mice as a valuable model for exploring therapies targeting DMD-associated cardiac complications. SUMMARY STATEMENTThis work introduces an improved method to model heart failure in mouse models of Duchenne muscular dystrophy and comprehensively describes underlying cellular and physiologic mechanisms using advanced imaging techniques.

physiology↗

Deep learning enables accurate soft tissue deformation estimation in vivo

Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue failure. These tools have been instrumental in biomechanics research where measuring in vitro and in vivo tissue deformations help evaluate tissue health and disease progression. However, accurately measuring tissue deformation in vivo is particularly challenging due to limited image signal-to-noise ratio. Therefore, we created a novel deep-learning approach for measuring deformation from a sequence of in vivo images called StrainNet. Utilizing a training dataset that incorporates image artifacts, StrainNet was designed to maximize performance in challenging in vivo settings. Artificially generated image sequences of human flexor tendons undergoing known deformations were used to compare StrainNet against two conventional image-based strain measurement techniques. StrainNet outperformed the traditional techniques by nearly 90%. High-frequency ultrasound imaging was then used to acquire images of the flexor tendons engaged during contraction. Only StrainNet was able to track tissue deformations under the in vivo test conditions. Findings revealed strong correlations between tendon deformation and contraction effort, highlighting the potential for StrainNet to be a valuable tool for assessing preventative care, rehabilitation strategies, or disease progression. Additionally, by using real-world data to train our model, StrainNet was able to generalize and reveal important relationships between the effort exerted by the participant and tendon mechanics. Overall, StrainNet demonstrated the effectiveness of using deep learning for image-based strain analysis in vivo.

bioengineering↗