Photodynamic therapy (PDT) has been used for the treatment of non-malignant and malignant diseases from head to toe. Over the last decade its clinical application has gained increasing acceptance around the world. Pre-clinical studies demonstrate that, in addition to the direct local cytotoxicity and vascular effects, PDT can induce various host immune responses. Recent clinical data also show that improved clinical outcomes are obtained through the combination of PDT and immunomodulation. This review will summarize and discuss recent progress in developing innovative regimen of PDT combined with immunomodulation for the treatment of both nonmalignant and malignant diseases.

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The aim of image-based localization (IBL) is to localize the real location of query image by matching reference image in database with GNSS-tags. Popular methods related to IBL commonly use street-level images, which have high value in practical application. Using street-level image to tackle IBL task has the primary challenges: existing works have not made targeted optimization for urban IBL tasks. Besides, the matching result is over-reliant on the quality of image features. Methods should address their practicality and robustness in engineering application, under metropolitan-scale. In response to these, this paper made following contributions: firstly, given the critical of buildings in distinguishing urban scenes, we contribute a feature called Building-Aware Feature (BAF). Secondly, in view of negative influence of complex urban scenes in retrieval process, we propose a retrieval method called Patch-Region Retrieval (PRR). To prove the effectiveness of BAF and PRR, we established an image-based localization experimental framework. Experiments prove that BAF can retain the feature points that fall on the building, and selectively lessen the feature points that fall on other things. While this effectively compresses the storage amount of feature index, we can also improve recall of localization results; implemented in the stage of geometric verification, PRR compares matching results of regional features and selects the best ranking as final result. PRR can enhance effectiveness of patch-regional feature. In addition, we fully confirmed the superiority of our proposed methods through a metropolitan-scale street-level image dataset.