Conventional oxygen‐dependent photodynamic therapy (PDT) has faced severe challenges because of the non‐specificity of most available photosensitizers (PSs) and the hypoxic nature of tumor tissues. Here, an O2 self‐sufficient cell‐like biomimetic nanoplatform (CAT‐PS‐ZIF@Mem) consisting of the cancer cell membrane (Mem) and a cytoskeleton‐like porous zeolitic imidazolate framework (ZIF‐8) with the embedded catalase (CAT) protein molecules and Al(III) phthalocyanine chloride tetrasulfonic acid (AlPcS4, defined as PS) is developed. Because of the immunological response and homologous targeting abilities of the cancer cell membrane, CAT‐PS‐ZIF@Mem is selectively accumulated at the tumor site and taken up effectively by tumor cells after intravenous injection. After the intracellular H2O2 penetration into the framework, it is catalyzed by CAT to produce O2 at the hypoxic tumor site, facilitating the generation of toxic 1O2 for highly effective PDT in vivo under near‐infrared irradiation. By integrating the immune escape, cell homologous recognition, and O2 self‐sufficiency, this cell‐like biomimetic nanoplatform demonstrates highly specific and efficient PDT against hypoxic tumor cells with much reduced side‐effect on normal tissues.

Geometrical shape of nanoparticles plays an important role in cellular internalization. However, the applicability in tumor selective therapeutics is still scarcely reported. In this article, we designed a tumor extracellular acidity-responsive chimeric peptide with geometrical shape switch for enhanced tumor internalization and photodynamic therapy. This chimeric peptide could self-assemble into spherical nanoparticles at physiological condition. While at tumor extracellular acidic microenvironment, chimeric peptide underwent detachment of acidity-sensitive 2,3-dimethylmaleic anhydride groups. The subsequent recovery of ionic complementarity between chimeric peptides resulted in formation of rod-like nanoparticles. Both in vitro and in vivo studies demonstrated that this acidity-triggered geometrical shape switch endowed chimeric peptide with accelerated internalization in tumor cells, prolonged accumulation in tumor tissue, enhanced photodynamic therapy, and minimal side effects. Our results suggested that fusing tumor microenvironment with geometrical shape switch should be a promising strategy for targeted drug delivery.

Here, a protein farnesyltransferase (PFTase)-driven plasma membrane (PM)-targeted chimeric peptide, PpIX-C 6 -PEG 8 -KKKKKKSKTKC-OMe (PCPK), was designed for PM-targeted photodynamic therapy (PM-PDT) and enhanced immunotherapy via tumor cell PM damage and fast release of damageassociated molecular patterns (DAMPs). The PM targeting ability of PCPK originates from the cellular K-Ras signaling, which occurs exclusively to drive the corresponding proteins to PM by PFTase. With the conjugation of the photosensitizer protoporphyrin IX (PpIX), PCPK could generate cytotoxic reactive oxygen species to deactivate membrane-associated proteins, initiate lipid peroxidation, and destroy PM with an extremely low concentration (1 μM) under light irradiation. The specific PM damage further induced the fast release of DAMPs (high-mobility group box 1 and ATP), resulting in antitumor immune responses stronger than those of conventional cytoplasm-localized PDT. This immune-stimulating PM-PDT strategy also exhibited the inhibition effect for distant metastatic tumors when combined with programmed cell death receptor 1 blockade therapy.

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During the last decade, peptide‐based nanomaterials are recognized as upcoming biomedical materials for tumor imaging and therapy. The rapid expansion of peptides and peptide derivatives is almost owing to their excellent biocompatibility, diverse bioactivity, potential biodegradability, specific biological recognition ability, and easy chemical modification characteristic. The present review outlines the development up to now concerning the design and biomedical applications of peptide‐based multifunctional nanomaterials, with an emphasis on their variegated therapeutic methods.

The cell membrane is the most important protective barrier in living cells and cell membrane targeted therapy may be a high-performance therapeutic modality for tumor treatment. Here, a novel charge reversible self-delivery chimeric peptide C 16 -PRP-DMA is developed for long-term cell membrane targeted photodynamic therapy (PDT). The self-assembled C 16 -PRP-DMA nanoparticles can effectively target to tumor by enhanced permeability and retention effect without additional carriers. After undergoing charge reverse in acidic tumor microenvironment, C 16 -PRP-DMA inserts into the tumor cell membrane with a long retention time of more than 14 h, which is very helpful for in vivo applications. It is found that under light irradiation, the reactive oxygen species generated by the inserted C 16 -PRP-DMA would directly disrupt cell membrane and rapidly induce cell necrosis, which remarkably increases the PDT effect in vitro and in vivo. This novel self-delivery chimeric peptide with a long-term cell membrane targeting property provides a new prospect for effective PDT of cancer.

The study investigates land use/cover classification and change detection of urban areas from very high resolution (VHR) remote sensing images using deep learning-based methods. Firstly, we introduce a fully Atrous convolutional neural network (FACNN) to learn the land cover classification. In the FACNN an encoder, consisting of full Atrous convolution layers, is proposed for extracting scale robust features from VHR images. Then, a pixel-based change map is produced based on the classification map of current images and an outdated land cover geographical information system (GIS) map. Both polygon-based and object-based change detection accuracy is investigated, where a polygon is the unit of the GIS map and an object consists of those adjacent changed pixels on the pixel-based change map. The test data covers a rapidly developing city of Wuhan (8000 km2), China, consisting of 0.5 m ground resolution aerial images acquired in 2014, and 1 m ground resolution Beijing-2 satellite images in 2017, and their land cover GIS maps. Testing results showed that our FACNN greatly exceeded several recent convolutional neural networks in land cover classification. Second, the object-based change detection could achieve much better results than a pixel-based method, and provide accurate change maps to facilitate manual urban land cover updating.

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