Nanoscale hydroxyapatite (nHA) is considered as a promising drug carrier or therapeutic agent against malignant tumors. But the strong agglomeration tendency and lack of active groups seriously hamper their usage in vivo. To address these issues, we fabricated an organic−inorganic hybrid nanosystem composed of poly(acrylic acid) (PAA), nHA, and indocyanine green (ICG), and further modified with glucose to give a targeting nanosystem (GA@HAP/ICG-NPs). These hybrid nanoparticles (∼90 nm) showed excellent storage and physiological stability assisted by PAA and had a sustained drug release in an acidic tumor environment. In vitro cell experiments confirmed that glucose-attached particles significantly promoted cellular uptake and increased intracellular ICG and Ca 2+ concentrations by glucose transporter 1 (GLUT1)-mediated endocytosis. Subsequently, the excessive Ca 2+ induced cell or organelle damage and ICG triggered photothermal and photodynamic effects (PTT/PDT) under laser irradiation, resulting in enhanced cell toxicity and apoptosis. In vivo tests revealed that the hybrid nanosystem possessed good hemocompatibility and biosafety, facilitating in vivo circulation and usage. NIR imaging further showed that tumor tissues had more drug accumulation, resulting in the highest tumor growth inhibition (87.89%). Overall, the glucose-targeted hybrid nanosystem was an effective platform for collaborative therapy and expected to be further used in clinical trials.
With the explosive growth of network information, in order to obtain the information faster and more accurately, this paper proposes a text keyword extraction method based on Bert. Firstly, the key sentence set is extracted from the background material by Bert model as the information supplement to the text. Then, based on the extended text, TF-IDF, text rank and LDA are combined to extract keywords. The experimental results on real science and technology academic paper data sets show that the performance of the fusion multi type feature combination algorithm is better than that of the traditional single algorithm; and the F value of the algorithm is increased by 1.5% by extracting key sentences from background materials, which further improves the effect of key word extraction.
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