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Polygonatum sibiricum polysaccharides (PSP) are a traditional herbal medicine component with potential therapeutic effects on several diseases. The present study aimed to assess the role of PSP in the treatment of human prostate cancer using a PC-3 cell line by Cell CK-8, transwell and wound healing assays, then elucidate the potential underlying mechanisms by western blot and quantitative Real-time RT-PCR. Different concentrations of PSP were applied to PC-3 cells, and the proliferation, invasion and migration of PC-3 cells were demonstrated to be significantly inhibited with increasing concentrations of PSP. Additionally, cell apoptosis rate and expression of caspase-3 increased with higher PSP concentrations, and the cell cycle was arrested in the S phase. Furthermore, it was demonstrated that the expression of the multidrug resistance-1 gene and its encoded protein P-glycoprotein in PC-3 cells decreased following PSP treatment, suggesting that PSP may have the potential to reverse multidrug resistance in PC-3 cells. The present study also evaluated the possible mechanism of PSP action on PC-3 cells. The results revealed that phosphorylated P65, PI3K and AKT decreased in a concentration-dependent manner. As key molecules in the NF-κB and PI3K/Akt signaling pathways, this finding suggests that the potential mechanism of the effect of PSP on prostate cancer cells may involve simultaneous mediation of the PI3K/Akt and NF-κB signaling pathways. The present study demonstrated that PSP inhibit the proliferation, invasion and migration of PC-3 cells in vitro, as well as reverse MDR in these cells. The underlying mechanism may involve the simultaneous regulation of the PI3K/Akt and NF-κB signaling pathways. [ABSTRACT FROM AUTHOR]

The global warming potential (GWP) is a relative measure of the capability of a molecule to trap the Earth's infrared radiation as heat. The measurement or prediction of the GWP of a molecule is based on two factors: the radiative efficiency and atmospheric lifetime of a molecule. While the calculation of the radiative efficiency of a molecule using the computational chemistry approach, such as density functional theory (DFT), is well-established and robust, the development of a computational approach to estimate the atmospheric lifetime remains challenging and limited to date. In this contribution, we developed a machine learning (ML) approach to estimate a molecule's atmospheric lifetime and GWP 100 based on electronic and geometrical features. We benchmarked the state-of-the-art computational workflow with the developed ML model in estimating the atmospheric lifetime and GWP 100 . The developed ML model outperforms the existing approach with the mean absolute error values of 0.234 (ML-predicted atmospheric lifetime) and 0.249 (direct ML model for GWP 100 ) compared with 0.535 (Atkinson's method) and 0.773 (Kazakov et al.) from previous works. The developed models were used to screen >7000 molecules in PubChem and bigQM7 data sets in a search for SF 6 replacement gas for the electric industry and identified 84 potential candidates.