Protoilludane-Type and Related Nor-Sesquiterpenes from Phellinus hartigii and Their Anti-Hypertrophic Activities in Rat Cardiomyocytes.

Three nor-sesquiterpenes, phellinharts A-C (1-3), isolated from Phellinus hartigii, exhibited unprecedented protoilludane and cerapicane-type structures. The structures of compounds 1-3 were elucidated via spectroscopic analysis, quantum chemical calculations, and X-ray diffraction. Potential biogenic pathways involving demethylation, ring cleavage, and rearrangement were proposed. Compounds 1-3 displayed potent anti-hypertrophic activities with low cytotoxicity (CC50 > 50 µM) in rat cardiomyocytes, underscoring their therapeutic potential.

Integrating Computational and Experimental Methods to Identify Novel Sweet Peptides from Egg and Soy Proteins.

Sweetness in food delivers a delightful sensory experience, underscoring the crucial role of sweeteners in the food industry. However, the widespread use of sweeteners has sparked health concerns. This underscores the importance of developing and screening natural, health-conscious sweeteners. Our study represents a groundbreaking venture into the discovery of such sweeteners derived from egg and soy proteins. Employing virtual hydrolysis as a novel technique, our research entailed a comprehensive screening process that evaluated biological activity, solubility, and toxicity of the derived compounds. We harnessed cutting-edge machine learning methodologies, specifically the latest graph neural network models, for predicting the sweetness of molecules. Subsequent refinements were made through molecular docking screenings and molecular dynamics simulations. This meticulous research approach culminated in the identification of three promising sweet peptides: DCY(Asp-Cys-Tyr), GGR(Gly-Gly-Arg), and IGR(Ile-Gly-Arg). Their binding affinity with T1R2/T1R3 was lower than -15 kcal/mol. Using an electronic tongue, we verified the taste profiles of these peptides, with IGR emerging as the most favorable in terms of taste with a sweetness value of 19.29 and bitterness value of 1.71. This study not only reveals the potential of these natural peptides as healthier alternatives to traditional sweeteners in food applications but also demonstrates the successful synergy of computational predictions and experimental validations in the realm of flavor science.

Deep-learning-assisted theoretical insights into the compatibility of environment friendly insulation medium with metal surface of power equipment.

Exploring a new generation of eco-friendly gas insulation medium to replace greenhouse gas sulphur hexafluoride (SF6) in power industry is significant for reducing the greenhouse effect and building a low-carbon environment. The gas-solid compatibility of insulation gas with various electrical equipment is also of significance before practical applications. Herein, take a promising SF6 replacing gas trifluoromethyl sulfonyl fluoride (CF3SO2F) for example, one strategy to theoretically evaluate the gas-solid compatibility between insulation gas and the typical solid surfaces of common equipment was raised. Firstly, the active site where the CF3SO2F molecule is prone to interact with other compounds was identified. Secondly, the interaction strength and charge transfer between CF3SO2F and four typical solid surfaces of equipment were studied by first-principles calculations and further analysis was conducted, with SF6 as the control group. Then, the dynamic compatibility of CF3SO2F with solid surfaces was investigated by large-scale molecular dynamics simulations with the aid of deep learning. The results indicate that CF3SO2F has excellent compatibility similar to SF6, especially in the equipment whose contact surface is Cu, CuO, and Al2O3 due to their similar outermost orbital electronic structures. Besides, the dynamic compatibility with pure Al surfaces is poor. Finally, preliminary experimental verifications indicate the validity of the strategy.

Crocin Ameliorates Diabetic Nephropathy through Regulating Metabolism, CYP4A11/PPARγ, and TGF-ß/Smad Pathways in Mice.

INTRODUCTION: Crocin is one of the main components of Crocus sativus L. and can alleviate oxidative stress and inflammation in diabetic nephropathy (DN). However, the specific mechanism by which crocin treats DN still needs to be further elucidated. METHOD: In the present study, a mouse model of DN was first established to investigate the therapeutic effect of crocin on DN mice. Subsequently, non-targeted metabolomics techniques were used to analyze the mechanisms of action of crocin in the treatment of DN. The effects of crocin on CYP4A11/PPARγ and TGF-ß/Smad pathway were also investigated. RESULT: Results showed that crocin exhibited significant therapeutic and anti-inflammatory, and anti-oxidative effects on DN mice. In addition, the non-targeted metabolomics results indicated that crocin treatment affected several metabolites in kidney. These metabolites were mainly associated with biotin metabolism, riboflavin metabolism, and arachidonic acid metabolism. Furthermore, crocin treatment upregulated the decreased levels of CYP4A11 and phosphorylated PPARγ, and reduced the increased levels of TGF-ß1 and phosphorylated Smad2/3 in the kidneys of DN mice. CONCLUSION: In conclusion, our study validated the considerable therapeutic, anti-inflammatory, and antioxidative impacts of crocin on DN mice. The mechanism of crocin treatment may be related to the regulation of biotin riboflavin and arachidonic acid metabolism, the activation of CYP4A11/PPARγ pathway, and the inhibition of TGF-ß/Smad pathway in the kidney.

Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook.

Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic-level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks, can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross-validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of Artificial-intelligence-assisted material exploration.