RESUMO
Donor-acceptor (D-A) conjugated polymers can favor the nonradiative thermal dissipation process, due to the formation of an intramolecular charge transfer (ICT) state resulting from the electron cloud delocalization of the HOMO (highest occupied molecular orbital) and LUMO (lowest unoccupied molecular orbital). Thus, to realize a high extinction coefficient and excellent photothermal conversion ability for a single photothermal agent, donor-acceptor type conjugated polymers PBDT-QTz and PCDT-QTz, comprising a new electron-deficient unit 2-(2-decyltetradecyl)-6,7-dimethyl-2H-[1,2,3]triazolo [4,5-g] quinoxaline (QTz) as the acceptor and 4,8-di(thiophen-2-yl)benzo[1,2-b:4,5-b']dithiophene (BDT) or 4H-cyclopenta[2,1-b:3,4-b'] dithiophene (CDT) as the donor, are designed and synthesized by manipulating intramolecular motion. The high extinction coefficient of 28.5 L g-1 cm-1 at 850 nm and the optimal photothermal conversion efficiency of 64.3% under an 808 nm laser are achieved based on PBDT-QTz. Consequently, PBDT-QTz nanoparticles can be successfully used for both in vitro and in vivo experiments. After intravenous administration and 808 nm laser irradiation, HeLa tumor-bearing mice achieve complete tumor remission without recurrence. The results provide an efficient photothermal agent by manipulating molecular motion.
Assuntos
Nanopartículas , Terapia Fototérmica , Humanos , Animais , Camundongos , Polímeros , Quinoxalinas/farmacologia , Células HeLaRESUMO
Multifunctional theranostics play a critical role in improving the efficacy of photothermal therapy and tumor fluorescence imaging; however, they require the integration of complex components into a single theranostic system, and their response in the second near-infrared (NIR-II) region is constrained by wavelengths of a photosensitizer. To address this issue, we herein developed a novel multifunctional thiazole-fused quinoxalineimide semiconducting polymer (named PQIA-BDTT), which exhibits NIR-II fluorescence and photothermal properties. PQIA-BDTT nanoparticles achieved an impressively high photothermal conversion efficiency (72.6%) in laser (1064 nm)-induced photothermal therapy at a safe maximum permissible exposure, demonstrating their capability as an effective photothermal agent. Moreover, PQIA-BDTT nanoparticles can be used as a reference for NIR-II fluorescence imaging under a low laser fluence. The tumor size and location in 4T1 mice intravenously injected with the PQIA-BDTT nanoparticles could be precisely identified through NIR-II fluorescence imaging, which also exhibited remarkable photothermal antitumor efficacy by in vitro and in vivo therapy. Overall, this study demonstrates that introducing a thiazole-fused quinoxalineimide acceptor unit into a donor-acceptor conjugated polymer is an effective strategy for the synthesis of novel multifunctional theranostic systems, which provides a novel platform for designing theranostic agents for biomedical applications.
Assuntos
Nanopartículas , Neoplasias , Animais , Camundongos , Linhagem Celular Tumoral , Nanopartículas/uso terapêutico , Imagem Óptica , Fototerapia/métodos , Terapia Fototérmica , Polímeros , Nanomedicina Teranóstica/métodos , Espectroscopia de Luz Próxima ao InfravermelhoRESUMO
In transfer learning model, the source domain samples and target domain samples usually share the same class labels but have different distributions. In general, the existing transfer learning algorithms ignore the interclass differences and intraclass similarities across domains. To address these problems, this article proposes a transfer learning algorithm based on discriminative Fisher embedding and adaptive maximum mean discrepancy (AMMD) constraints, called discriminative Fisher embedding dictionary transfer learning (DFEDTL). First, combining the label information of source domain and part of target domain, we construct the discriminative Fisher embedding model to preserve the interclass differences and intraclass similarities of training samples in transfer learning. Second, an AMMD model is constructed using atoms and profiles, which can adaptively minimize the distribution differences between source domain and target domain. The proposed method has three advantages: 1) using the Fisher criterion, we construct the discriminative Fisher embedding model between source domain samples and target domain samples, which encourages the samples from the same class to have similar coding coefficients; 2) instead of using the training samples to design the maximum mean discrepancy (MMD), we construct the AMMD model based on the relationship between the dictionary atoms and profiles; thus, the source domain samples can be adaptive to the target domain samples; and 3) the dictionary learning is based on the combination of source and target samples which can avoid the classification error caused by the difference among samples and reduce the tedious and expensive data annotation. A large number of experiments on five public image classification datasets show that the proposed method obtains better classification performance than some state-of-the-art dictionary and transfer learning methods. The code has been available at https://github.com/shilinrui/DFEDTL.