Introduction Chemotherapeutics are known to have undesirable side effects (i.e. nausea, weight loss, hair loss, weakened immune system, etc.) due to the non-specificity of the drugs. Encapsulation of these chemotherapeutics inside nanoparticles significantly improves the bioavailability and half-life of drugs, while increasing their tumor penetration and localization. However, most, if not all, nanoparticles in clinics or research are synthetic, with no long-term studies on the effect of these nanoparticles in vivo. Herein, we developed a synergistic resveratrol nanoparticle system by using lecithin encapsulation. Lecithin, being a fully natural phospholipid derived from soybean, possesses inherent anti-tumor activity. Methods Lec(RSV) was successfully prepared using the nanoprecipitation method, and characterized by particle size and zeta potential analysis, and transmission electron microscopy (TEM). The in vitro cellular uptake and cytotoxic effects of Lec(RSV) were investigated in human breast cancer cell line BT474. Finally, the in vivo tumoral uptake of Lec(RSV) was carried out in the BT474 orthotopic model. Results Lec(RSV) showed a uniform distribution of ~120 nm, with prolonged stability. Lec(RSV) showed high cellular uptake and anti-cancer properties in vitro. Time-dependent uptake in the BT474 xenograft model indicated an increased tumoral uptake and apoptosis rate at 4 hours after tail vein injection of Lec(RSV). Conclusion Taken together, we successfully developed a fully natural Lec(RSV) that possesses potent anti-cancer activity in vitro, with good tumoral uptake in vivo. We hypothesize that Lec(RSV) could be a safe anti-cancer therapeutic that could be easily translated into clinical application.

In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user interaction module obtains and displays the retrieval results through data parsing, calling the background, and computing engine. The data cleaning module fills, optimizes, and normalizes the data through business experience; the calculation engine module uses the algorithm and extracts the database information to get the similar time series and matching financial model. Finally, the data acquisition module fills the database with historical data at the initialization stage and updates the database every day. The data analysis platform for quantitative trading designed and implemented in this paper has carried out demand analysis, design, implementation, and test. From the perspective of function test and performance test, two functions of similar stock search and financial matching model are selected and tested, and the results are in line with the expected results.