Multifunctional phototheranostics that integrate several diagnostic and therapeutic strategies into one platform hold great promise for precision medicine. However, it is really difficult for one molecule to possess multimodality optical imaging and therapy properties that all functions are in the optimized mode because the absorbed photoenergy is fixed. Herein, a smart one‐for‐all nanoagent that the photophysical energy transformation processes can be facilely tuned by external light stimuli is developed for precise multifunctional image‐guided therapy. A dithienylethene‐based molecule is designed and synthesized because it has two light‐switchable forms. In the ring‐closed form, most of the absorbed energy dissipates via nonradiative thermal deactivation for photoacoustic (PA) imaging. In the ring‐open form, the molecule possesses obvious aggregation‐induced emission features with excellent fluorescence and photodynamic therapy properties. In vivo experiments demonstrate that preoperative PA and fluorescence imaging help to delineate tumors in a high‐contrast manner, and intraoperative fluorescence imaging is able to sensitively detect tiny residual tumors. Furthermore, the nanoagent can induce immunogenic cell death to elicit antitumor immunity and significantly suppress solid tumors. This work develops a smart one‐for‐all agent that the photophysical energy transformation and related phototheranostic properties can be optimized by light‐driven structure switch, which is promising for multifunctional biomedical applications.
At present, people pay less attention to the diversity of the generated results about the generation of handwritten Chinese characters. The structure of Chinese characters is complex and the process of handwriting has strong freedom. So the image of handwritten Chinese characters has a certain diversity. In this paper, a handwritten Chinese character generation adversarial network is proposed. By adding the standard font information and feature vector into the network, the generation results of the network can not only achieve certain accuracy but also produce diversity. Improved loss function makes the network more inclined to generate a diversity of results. The method using the connected domain segmentation is used to better ensure the accuracy of the generated image. By training on the handwritten Chinese character dataset, it is verified that the network shows good diversity when the accuracy is similar to that of other methods.
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