Photoactivation is a promising theranostic tool to image and stabilize the atherosclerotic plaque by apoptosis induction in macrophages or other vascular cells; however, lack of effective drugs and mechanistic understanding hinder its clinical application for cardiovascular disease. Here, we developed the macrophage targeted photosensitizer delivery strategy and demonstrated that imaging assisted light activation reduced inflammation and burden of atherosclerotic plaques. Mechanistically, targeted photoactivation induced autophagy and increased MerTK expression in carotid atheroma as early as 1 day, and had 2-fold increase in macrophage-associated apoptotic cells, indicating efferocytosis enhancement. This multifunctional photoactivatable theranostic strategy could confer a promising tool for high-risk plaques.
Intravascular optical coherence tomography-fluorescence lifetime imaging (OCT-FLIm) provides co-registered structural and biochemical information of atherosclerotic plaques in a label-free manner. For intuitive image interpretation of OCT-FLIm, herein, we present a machine learning classifier where key biochemical components (lipids, lipids+macrophages, macrophages, fibrotic, and normal) related to plaque destabilization are characterized based on the combination of multispectral FLIm parameters and convolutional OCT features. Using dataset from in vivo atheromatous swine models, the classification accuracy was >92% for each plaque component according the five-fold cross validation. This highly translatable imaging strategy will open a new avenue for clinical intracoronary assessment of high-risk plaques.
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