Introducing upfront losses as well as gains decreases impatience in intertemporal choices with rewards

People tend to prefer smaller and sooner (SS) rewards over larger and later (LL) ones even when the latter are much larger. Previous research have identified several ways to enhance people’s patience. Adding to this literature, the current paper demonstrates that introduction of upfront losses as well as gains to both SS and LL rewards can decrease people’s impatience. This effect is incompatible with both the normative exponential and descriptive hyperbolic discounting models, which agree on the additive assumption and the independence assumption. We also exculde the integration explanation which assumes subjects integrate upfront money with final rewards and make a decision with bottom line at the end. We consider several possible explanations, including the salience hypothesis, which states that introducing upfront money makes the money dimension more salient than not and thus increases the attractiveness of LL options

Curcumin-loaded graphene oxide quantum dots enhance otoprotective effects via blocking cuproptosis

Background: Cisplatin (CIS) is widely used to treat various cancers but can cause ototoxicity and sensory hair cell loss in the inner ear. Copper induces an excessive production of reactive oxygen species (ROS) in hair cells, leading to the development of various antioxidants.Methods and results: This study aimed to evaluate the potential antioxidant properties of curcumin (CUR) in the inner ear organ of corti-1 cells (OC1) and animal models (zebrafish and guinea pigs). Graphene oxide quantum dots (GOQDs) enabled CUR to penetrate the round window membrane (RWM) and maintain the concentration in the perilymph after inner ear administration. The results showed that CUR/GOQDs had favorable biocompatibility and strongly affected ROS generation induced by CIS in OC1 cells. DCFHDA Green staining demonstrated that CUR/GOQDs successfully reversed the decrease in mitochondrial membrane potential induced by CIS in vitro and rescued cells from early cuproptosis, which was confirmed by FDX1 staining. Additionally, the experiment found that CUR decreased the expression of cuproptosis proteins (FDX1, LIAS, and LIPT1) and increased the expression of the Bcl-2 protein.Conclusion: The results demonstrate that CUR/GOQDs is a promising therapeutic agent that can prevent CIS-induced ototoxicity by blocking the cuproptosis signal pathway

Prediction of Drug-Likeness Using Deep Autoencoder Neural Networks

Due to diverse reasons, most drug candidates cannot eventually become marketed drugs. Developing reliable computational methods for prediction of drug-likeness of candidate compounds is of vital importance to improve the success rate of drug discovery and development. In this study, we used a fully connected neural networks (FNN) to construct drug-likeness classification models with deep autoencoder to initialize model parameters. We collected datasets of drugs (represented by ZINC World Drug), bioactive molecules (represented by MDDR and WDI), and common molecules (represented by ZINC All Purchasable and ACD). Compounds were encoded with MOLD2 two-dimensional structure descriptors. The classification accuracies of drug-like/non-drug-like model are 91.04% on WDI/ACD databases, and 91.20% on MDDR/ZINC, respectively. The performance of the models outperforms previously reported models. In addition, we develop a drug/non-drug-like model (ZINC World Drug vs. ZINC All Purchasable), which distinguishes drugs and common compounds, with a classification accuracy of 96.99%. Our work shows that by using high-latitude molecular descriptors, we can apply deep learning technology to establish state-of-the-art drug-likeness prediction models