Immunotherapy holds great promise for the treatment of aggressive and metastatic cancers; however, currently available immunotherapeutics, such as immune checkpoint blockade, benefit only a small subset of patients. A photoactivatable toll-like receptor 7/8 (TLR7/8) nanoagonist (PNA) system that imparts near-infrared (NIR) light-induced immunogenic cell death (ICD) in dying tumor cells in synchrony with the spontaneous release of a potent immunoadjuvant is developed here. The PNA consists of polymer-derived proimmunoadjuvants ligated via a reactive oxygen species (ROS)-cleavable linker and polymer-derived photosensitizers, which are further encapsulated in amphiphilic matrices for systemic injection. In particular, conjugation of the TLR7/8 agonist resiquimod to biodegradable macromolecular moieties with different molecular weights enabled pharmacokinetic tuning of small-molecule agonists and optimized delivery efficiency in mice. Upon NIR photoirradiation, PNA effectively generated ROS not only to ablate tumors and induce the ICD cascade but also to trigger the on-demand release of TLR agonists. In several preclinical cancer models, intravenous PNA administration followed by NIR tumor irradiation resulted in remarkable tumor regression and suppressed postsurgical tumor recurrence and metastasis. Furthermore, this treatment profoundly shifted the tumor immune landscape to a tumoricidal one, eliciting robust tumor-specific T cell priming in vivo. This work highlights a simple and cost-effective approach to generate in situ cancer vaccines for synergistic photodynamic immunotherapy of metastatic cancers.

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The frequent occurrence of extreme weather and the development of urbanization have led to the continuously worsening climate-related disaster losses. Socioeconomic exposure is crucial in disaster risk assessment. Social assets at risk mainly include the buildings, the machinery and the equipment, and the infrastructure. In this study, the wealth capital stock (WKS) was selected as an indicator for measuring social wealth. However, the existing WKS estimates have not been gridded accurately, thereby limiting further disaster assessment. Hence, the multisource remote sensing and the POI data were used to disaggregate the 2012 prefecture-level WKS data into 1000 m × 1000 m grids. Subsequently, ensemble models were built via the stacking method. The performance of the ensemble models was verified by evaluating and comparing the three base models with the stacking model. The stacking model attained more robust prediction results (RMSE = 0.34, R2 = 0.9025), and its prediction spatially presented a realistic asset distribution. The 1000 m × 1000 m WKS gridded data produced by this research offer a more reasonable and accurate socioeconomic exposure map compared with existing ones, thereby providing an important bibliography for disaster assessment. This study may also be adopted by the ensemble learning models in refining the spatialization of the socioeconomic data.