In the present study, a new coamorphous phase (CAP) of bioactive herbal ingredient curcumin (CUR) with high solubilitythe was screened with pharmaceutically acceptable coformers. Besides, to provide basic information for the best practice of physiological and pharmaceutical preparations of CUR-based CAP, the interaction between CUR-based CAP and bovine serum albumin (BSA) was studied at the molecular level in this paper. CAP of CUR and piperazine with molar ratio of 1:2 was prepared by EtOH-assisted grinding. The as-prepared CAP was characterized by powder X-ray diffraction, modulated temperature differential scanning calorimetry, thermogravimetric analysis, Fourier-transform infrared, and solid-state C nuclear magnetic resonance. The 1:2 CAP stoichioimetry was sustained by C═O···H hydrogen bonds between the N-H group of the piperazine and the C═O group of CUR; piperazine stabilized the diketo structure of CUR in CAP. The dissolution rate of CUR-piperazine CAP in 30% ethanol-water was faster than that of CUR; the t values were 243.1 min for CUR and 4.378 min for CAP. Furthermore, interactions of CUR and CUR-piperazine CAP with BSA were investigated by fluorescence spectroscopy and density functional theory (DFT) calculation. The binding constants (K) of CUR and CUR-piperazine CAP with BSA were 10.0 and 9.1 × 10 L mol at 298 K, respectively. Moreover, DFT simulation indicated that the interaction energy values of hydrogen-bonded interaction in the tryptophan-CUR and tryptophan-CUR-piperazine complex were -26.1 and -17.9 kJ mol, respectively. In a conclusion, after formation of CUR-piperazine CAP, the interaction forces between CUR and BSA became weaker.
AbstractTo support various service requirements such as massive Machine Type Communications, Ultra-Reliable and Low-Latency Communications in 5G scenario, Network Function Virtualization (NFV) plays an important role in the 5G network architecture to manage and orchestrate network services. As the key network function responsible for mobility management, Access and Mobility Management Function (AMF) can be deployed flexibly at the edge of the radio access network to improve the performance of mobility management based on NFV. In this paper, the optimal placement of AMF is addressed based on Deep Reinforcement Learning (DRL) in a heterogeneous radio access network, which aims to minimize the network utility including the average delay of mobility management requests at AMF, the average wired hops to relay the requests and the cost of AMF instances. By considering time-varying features including user mobility and the arrival rate of user mobility management requests, an AMF optimal placement approach is proposed for the long term optimization. Simulation results show that the performance of the proposed DRL based AMF optimal placement approach outperforms that of the baselines.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.