Ghajar-Rahimi, E. | bioRxiv Science

bioRxiv2026

Adaptations of the pregnant murine heart to pre-existing hypertension

2-5% of pregnancies are complicated by chronic hypertension [1], increasing the risk of peripartum cardiomyopathy and maternal morbidity [2, 3]. Hypertension reduces stroke volume, whereas pregnancy increases stroke volume. The combined impact of these opposing loads on cardiac function and strain remains unclear. We used a mouse model of pregnancy with pre-existing hypertension to characterize cardiac remodeling. We hypothesized that pregnancy may counteract hypertension-related reductions in cardiac function. Nulliparous female C57Bl6/J mice (7-11 weeks old) were assigned into pregnant (PREG, n=11), angiotensin II-treated (ANGII, n=7), and ANGII+PREG (n=8) groups. Hypertension was induced with subcutaneous angII infusion (1000 ng/kg/min). Cardiac function was measured with high-frequency ultrasound. Ejection fraction (EF) was maintained in PREG animals at 65% but decreased significantly by day 36 in ANGII animals (44.85{+/-}3.27%). In ANGII+PREG mice, EF initially decreased but trended towards PREG values on postpartum day 1. Left ventricle strain was lower in ANGII and ANGII+PREG groups compared to PREG. ANGII and ANGII+PREG animals showed decreased early longitudinal diastolic strain rate at mid-pregnancy compared to baseline (0.82{+/-}0.09% ANGII; 0.73{+/-}0.09% ANGII+PREG), while increasing in PREG (1.76{+/-}0.14%). Minimal physiologically relevant fibrosis was noted across groups via immunohistochemistry. Heart mass-to-body mass was lowest in PREG (6.20{+/-}0.22 mg/g), greatest in ANGII (8.95{+/-}0.39 mg/g), and intermediate in ANGII+PREG (6.92{+/-}0.25 mg/g). Cardiomyocyte cross-sectional area was similar between groups. Overall, pregnancy altered the functional and hypertrophic response to elevated blood pressure. Findings from this work set the foundation for future efforts that may eventually improve cardiovascular care for pregnant patients.

bioengineering↗

bioRxiv2023

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↗