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In this study, a reactive oxygen species (ROS)-responsive nanoparticle system was designed for combining photodynamic therapy (PDT) and chemotherapy for oral tongue squamous cell carcinoma (OTSCC)-targeted treatment. A PEGlated prodrug (RPTD) of doxorubicin (DOX) via thioketal linkage and cRGD peptide modification was synthesized and then used to prepare nanoparticles for encapsulating photosensitizer hematoporphyrin (HP). Thus, the obtained HP-loaded RPTD (RPTD/HP) nanoparticles had a regular spherical shape and small size, approximately 180 nm. The RPTD/HP nanoparticles showed a remarkable PDT efficiency and successfully induced ROS generation upon laser irradiation both in vitro and in vivo. DOX exhibited significant ROS-responsive release property from RPTD/HP nanoparticles because of the rupture of the thioketal linker. In OTSCC cells, RPTD/HP nanoparticles were efficiently internalized and showed potent effects on cell growth inhibition and apoptosis induction after laser irradiation. In OTSCC tumor-bearing mice, RPTD/HP nanoparticles displayed excellent tumor-targeting ability and notably suppressed tumor growth through multiple mechanisms after local laser irradiation. Taken together, we supplied a novel therapeutic nanosystem for OTSCC treatment through combining PDT and chemotherapy.

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Infrared and visible image fusion can compensate for the incompleteness of single-modality imaging and provide a more comprehensive scene description based on cross-modal complementarity. Most works focus on learning the overall cross-modal features by high-and low-frequency constraints at the image level alone, ignoring the fact that cross-modal instance-level features often contain more valuable information. To fill this gap, we model cross-modal instance-level features by embedding instance information into a set of Mixture-of-Experts (MoEs) for the first time, prompting image fusion networks to specifically learn instance-level information. We propose a novel framework with instance embedded Mixture-of-Experts for infrared and visible image fusion, termed MoE-Fusion, which contains an instance embedded MoE group (IE-MoE), an MoE-Decoder, two encoders, and two auxiliary detection networks. By embedding the instance-level information learned in the auxiliary network, IE-MoE achieves specialized learning of crossmodal foreground and background features. MoE-Decoder can adaptively select suitable experts for cross-modal feature decoding and obtain fusion results dynamically. Extensive experiments show that our MoE-Fusion outperforms state-of-the-art methods in preserving contrast and texture details by learning instance-level information in cross-modal images. Our code will be available: https://github.com/SunYM2020/MoE-Fusion.

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