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I. Pipinos

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Open access Jul 2026

Differential Myopathic and Transcriptomic Changes in Soleus and Gastrocnemius Muscles in a Novel Chronic Hindlimb Ischemia Rat Model Induced by Endovascular Catheter Occlusion

Peripheral artery disease (PAD) is a progressive atherothrombotic disorder affecting more than 230 million people worldwide. Conventional animal models of chronic hindlimb ischemia (HLI) are highly invasive, technically challenging, fail to account for anatomical variation, and may not accurately recapitulate progressive PAD pathophysiology. To address these limitations, we developed a novel chronic HLI model using an ilio‐femoral endovascular catheter occlusion (IFCO) approach. We hypothesized that IFCO would chronically reduce hindlimb blood flow, induce PAD‐associated myopathy, and overcome limitations of conventional techniques. Laser Doppler perfusion imaging demonstrated that IFCO reduced resting hindlimb perfusion by 70%, with significant reductions persisting for up to 28 days. Histological analysis of the soleus revealed significantly increased fibrosis (4.99% in sham vs. 13.19% in IFCO) and reduced myofiber cross‐sectional area (3072.21 μm2 in sham vs. 1556.27 μm2 in IFCO), whereas no significant differences were observed in the gastrocnemius. In contrast, IFCO increased the proportion of centralized nuclei from near 0% in sham muscles to 21.55% in the gastrocnemius and 32.65% in the soleus. RNA sequencing corroborated these findings and demonstrated that the ischemic soleus transcriptome exhibited enhanced fibrotic, pro‐angiogenic, and myoblast fusion‐associated signaling consistent with histological evidence of myopathy. These findings demonstrate that IFCO produces sustained hindlimb ischemia and PAD‐associated skeletal muscle remodeling, with distinct responses between muscle types. This model may facilitate studies of PAD‐associated myopathy and support the development of targeted therapeutic interventions.

Oliver Kitzerow, Zhiqiu Xia, Samuel Gillman et al. · 0 citations
Open access Jul 2026

Prediction of Peripheral Artery Disease Treatment Decisions and Outcomes Using Machine Learning and Gait-Derived Digital Biomarkers

Background: Peripheral artery disease (PAD) is clinically heterogeneous, complicating identification, treatment selection, and prediction of therapeutic benefit. Gait biomechanics provide high-resolution, objective digital biomarkers that can be used in routine care. Methods: We built a computational framework for three tasks: (1) distinguish controls versus PAD; (2) predict clinician-selected treatment (nonoperative vs. revascularization); and (3) forecast post-treatment ground-reaction force (GRF) from baseline GRF features. The dataset included 55 unique patients with PAD and 42 controls. Standard classifiers and regressors were evaluated, and performance was measured by accuracy, balanced accuracy, Matthew’s correlation coefficient (MCC), and mean absolute error. Results: A single GRF feature (propulsive peak) achieved 90.9% accuracy, 86.7% balanced accuracy, and 0.868 MCC for distinguishing PAD from controls and predicting treatment, outperforming patient-reported outcomes and kinematics. For outcome forecasting, a Random Forest model predicted changes in the propulsive peak with 80% accuracy and low error. Conclusions: GRF-based models accurately identify PAD, anticipate this clinical team’s treatment selection, and forecast functional recovery within this single-center cohort; generalization to other practice patterns and settings remains untested. The approach relies on minimal features and modest computation and is readily generalized to other medical conditions that affect gait. Because it is compatible with wearable sensors, it is well suited to real-time, remote assessment and can advance personalized, data-driven decision support in vascular care.

Ali Al Ramini, Farahnaz Fallahtafti, M. A. Takallou et al. · 0 citations