A polymeric nanocarrier that eradicates breast cancer stem cells and delivers chemotherapeutic drugs.

BACKGROUND: Drug nanocarriers can markedly reduce the toxicities and side effects of encapsulated chemotherapeutic drugs in the clinic. However, these drug nanocarriers have little effect on eradicating breast cancer stem cells (BCSCs). Although compounds that can inhibit BCSCs have been reported, these compounds are difficult to use as carriers for the widespread delivery of conventional chemotherapeutic drugs. METHODS: Herein, we synthesize a polymeric nanocarrier, hyaluronic acid-block-poly (curcumin-dithiodipropionic acid) (HA-b-PCDA), and explore the use of HA-b-PCDA to simultaneously deliver chemotherapeutic drugs and eradicate BCSCs. RESULTS: Based on molecular docking and molecular dynamics studies, HA-b-PCDA delivers 35 clinical chemotherapeutic drugs. To further verify the drug deliver ability of HA-b-PCDA, doxorubicin, paclitaxel, docetaxel, gemcitabine and camptothecin are employed as model drugs to prepare nanoparticles. These drug-loaded HA-b-PCDA nanoparticles significantly inhibit the proliferation and stemness of BCSC-enriched 4T1 mammospheres. Moreover, doxorubicin-loaded HA-b-PCDA nanoparticles efficiently inhibit tumor growth and eradicate approximately 95% of BCSCs fraction in vivo. Finally, HA-b-PCDA eradicates BCSCs by activating Hippo and inhibiting the JAK2/STAT3 pathway. CONCLUSION: HA-b-PCDA is a polymeric nanocarrier that eradicates BCSCs and potentially delivers numerous clinical chemotherapeutic drugs.

Adoption of a deep learning-based neural network model in the psychological behavior analysis of resident tourism consumption.

With the development of society and the continuous progress of science and technology, it has become the mainstream measure to promote the development of the social economy through science and technology. Therefore, to improve the current situation of tourism consumption, improve the consumer sentiment of tourists, and promote the development of the tourism economy, the convolutional neural network (CNN) technology model is used to analyze the tourist's consumer psychology and behavior. Based on this, the user's consumption situation is analyzed, thus providing support for the intelligent improvement of tourism consumption. First, the basic characteristics of tourism consumption mood and behavior are introduced, and the methods to improve the tourism consumption mood and behavior are briefly introduced. Then, the CNN algorithm is employed to identify consumers' travel consumption behaviors and emotions. To improve the recognition effect, the algorithm is combined with skeleton node behavior recognition and video image behavior recognition. Finally, the performance of the designed algorithm is tested. The accuracy of the human behavior recognition (HBR) algorithm is more than 0.88. Compared with the detection effect of the HBR algorithm, the combined algorithm adopted in this work can reduce the image processing time and improve the detection efficiency. The multithread method can effectively reduce the complexity of the model and improve the recognition accuracy. The test results on different data sets show that the proposed algorithm can better adapt to the changes in identification samples and obtain more accurate recognition results compared with similar algorithms. In summary, this study not only provides technical support for the rational analysis of consumer sentiment and consumer behavior but also contributes to the comprehensive development of the tourism market.