Herein, we have developed a novel approach to investigate the mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model, experimental optimization of key parameters and experimental data validation of the predictive power of the model. The advantages of this study are that the impact of mechanical stimulation on bone regeneration in a porous biodegradable CaP scaffold is considered, experimental design is used to investigate the optimal combination of growth factors loaded on the porous biodegradable CaP scaffold to promote bone regeneration and the training, testing and analysis of the model are carried out by using experimental data, a data-mining algorithm and related sensitivity analysis. The results reveal that mechanical stimulation has a great impact on bone regeneration in a porous biodegradable CaP scaffold and the optimal combination of growth factors that are encapsulated in nanospheres and loaded into porous biodegradable CaP scaffolds layer-by-layer can effectively promote bone regeneration. Furthermore, the model is robust and able to predict the development of bone regeneration under specified conditions.

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The growth and survival of cancer cells are greatly related to their surrounding microenvironment. To understand the regulation under the impact of anti-cancer drugs and their synergistic effects, we have developed a multiscale agent-based model that can investigate the synergistic effects of drug combinations with three innovations. First, it explores the synergistic effects of drug combinations in a huge dose combinational space at the cell line level. Second, it can simulate the interaction between cells and their microenvironment. Third, it employs both local and global optimization algorithms to train the key parameters and validate the predictive power of the model by using experimental data. The research results indicate that our multicellular system can not only describe the interactions between the microenvironment and cells in detail, but also predict the synergistic effects of drug combinations.

A Cu-rich alloy nanocluster [Ag13Cu10(SAdm)12]X3 is reported, and its geometric and electronic structures are further determined via theoretical calculations.

The drug chirality is attracting increasing attention because of different biological activities, metabolic pathways, and toxicities of chiral enantiomers.The chiral separation has been a great challenge. Optimized high-performance liquid chromatography (HPLC) methods based on vancomycin chiral stationary phase (CSP) were developed for the enantioseparation of propranolol, atenolol, metoprolol, venlafaxine, fluoxetine, and amlodipine. The retention and enantioseparation properties of these analytes were investigated in the variety of mobile phase additives, flow rate, and column temperature. As a result, the optimal chromatographic condition was achieved using methanol as a main mobile phase with triethylamine (TEA) and glacial acetic acid (HOAc) added as modifiers in a volume ratio of 0.01% at a flow rate of 0.3 mL/minute and at a column temperature of 5°C. The thermodynamic parameters (eg, ΔH, ΔΔH, and ΔΔS) from linear van 't Hoff plots revealed that the retention of investigated pharmaceuticals on vancomycin CSP was an exothermic process. The nonlinear behavior of lnk′ against 1/T for propranolol, atenolol, and metoprolol suggested the presence of multiple binding mechanisms for these analytes on CSP with variation of temperature. The simulated interaction processes between vancomycin and pharmaceutical enantiomers using molecular docking technique and binding energy calculations indicated that the calculated magnitudes of steady combination energy (ΔG) coincided with experimental elution order for most of these enantiomers.