The aim of the study was to investigate the effects of protosappanin B on the proliferation and apoptosis of bladder cancer cells. The effects of protosappanin B (12.5, 25, 50, 100, or 200 μg/mL, 48 h) on proliferation of SV-HUC-1, T24 and 5637 cells was assessed using the MTT assay. The effects of protosappanin B (100, 150, 200, 250, or 300 μg/mL, 48 h) on cell apoptosis and cell cycle were analyzed using flow cytometry. T24 and 5637 cells treated with 200 µg/mL protosappanin B showed morphological changes (shrinkage, rounding, membrane abnormalities, and reduced adhesion), but protosappanin B had no proliferation arrest effect on SV-HUC-1 cells. Protosappanin B caused concentration-dependent inhibition of cell growth, with IC50 of 82.78 µg/mL in T24 cells and 113.79 µg/mL in 5637 cells. Protosappanin B caused concentration-dependent increases in T24 and 5637 cell apoptosis (100–300 µg/mL). The effects of protosappanin B on the cell cycle in both cell types was G1 arrest with reductions in the proportion of S-phase cells and proliferation index. A proteomics analysis showed that protosappanin B modulated a number of genes involved in the cell cycle. In conclusion, protosappanin B inhibits the proliferation and promotes the apoptosis of T24 and 5637 human bladder cancer cells in a concentration-dependent manner, possibly via interference with cell cycle regulation, preventing G1-to-S transition.
A decision maker looks to take an active action (e.g., purchase some goods or make an investment). The payoff of this active action depends on his own private type as well as a random and unknown state of nature. To decide between this active action and another passive action, which always leads to a safe constant utility, the decision maker may purchase information from an information seller. The seller can access the realized state of nature, and this information is useful for the decision maker (i.e., the information buyer) to better estimate his payoff from the active action.We study the seller's problem of designing a revenue-optimal pricing scheme to sell her information to the buyer. Suppose the buyer's private type and the state of nature are drawn from two independent distributions, we fully characterize the optimal pricing mechanism for the seller in closed form. Specifically, under a natural linearity assumption of the buyer payoff function, we show that an optimal pricing mechanism is the threshold mechanism which charges each buyer type some upfront payment and then reveals whether the realized state is above some threshold or below it. The payment and the threshold are generally different for different buyer types, and are carefully tailored to accommodate the different amount of risks each buyer type can take. The proof of our results relies on novel techniques and concepts, such as upper/lower virtual values and their mixtures, which may be of independent interest.A full version of this paper can be accessed from the following link: https://arxiv.org/abs/2102.13289 CCS Concepts: • Theory of computation → Algorithmic mechanism design; Computational pricing and auctions; Algorithmic game theory; • Applied computing → Economics.
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