Abstract Photodynamic therapy (PDT) with noninvasiveness and high safety has emerged as a promising therapeutic approach for the management of hypertrophic scars (HS). However, the low transdermal delivery and overexpressed levels of intracellular glutathione (GSH) severely hinder its therapeutic effectiveness. Herein, a multifunctional biomimetic nanoplatform (NDs@EV‐RGD) composed of arginine‐glycine‐aspartic acid (RGD)‐modified cucumber‐derived extracellular vesicles (EVs) and copper‐based metal‐organic framework nanodots (Cu‐MOF NDs) is designed for PDT‐mediated HS treatment. The EVs with low Young's modulus exhibit excellent deformability which endow NDs@EV‐RGD with the capacity to overcome the compact stratum corneum barrier, thereby significantly improving their transdermal delivery efficiency. Notably, the RGD targeting peptide displays specific binding to α1β1 integrin on the fibroblast membranes within HS, leading to the high accumulation efficiency of NDs@EV‐RGD at the HS site. Under near‐infrared laser irradiation, NDs@EV‐RGD efficiently generates abundant reactive oxygen species, inducing the apoptosis of excessively proliferated fibroblasts. Moreover, Cu‐MOF NDs interact with the local GSH, leading to GSH depletion and a significant enhancement in PDT efficacy. Furthermore, NDs@EV‐RGD demonstrates a remarkable therapeutic effect in improving the appearance of HS in a rabbit ear HS model, promoting the apoptosis and remodeling of collagen fibers. Therefore, this work provides a promising biomimetic platform for HS treatment.
Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the prevalent practice of employing additional CT scans for generating attenuation maps (u-map) for PET attenuation correction significantly elevates radiation doses. To address this concern and further mitigate radiation exposure in low-dose PET exams, we propose POUR-Net - an innovative population-prior-aided over-under-representation network that aims for high-quality attenuation map generation from low-dose PET. First, POUR-Net incorporates an over-under-representation network (OUR-Net) to facilitate efficient feature extraction, encompassing both low-resolution abstracted and fine-detail features, for assisting deep generation on the full-resolution level. Second, complementing OUR-Net, a population prior generation machine (PPGM) utilizing a comprehensive CT-derived u-map dataset, provides additional prior information to aid OUR-Net generation. The integration of OUR-Net and PPGM within a cascade framework enables iterative refinement of $\mu$-map generation, resulting in the production of high-quality $\mu$-maps. Experimental results underscore the effectiveness of POUR-Net, showing it as a promising solution for accurate CT-free low-count PET attenuation correction, which also surpasses the performance of previous baseline methods.
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