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Objectives: The purpose of this study was to create a new delivery system that can synergistically remineralize enamel white spot lesions (WSLs).Materials and methods: The delivery system (PAA-ACP@aMBG) was prepared by using aminated mesoporous bioactive glasses (aMBG) as the carrier loaded with polyacrylic-stabilized amorphous calcium phosphate (PAA-ACP). The materials were characterized by transmission electron microscopy (TEM), X-ray powder diffraction (XRD), inductively coupled plasma–optical emission spectrometry (ICP–OES), and so on. Forty-eight artificial WSLs enamel samples were randomized to four groups: artificial saliva (negative control, NC), casein phosphopeptide-amorphous calcium phosphate (CPP-ACP), PAA-ACP@aMBG, and MBG. The effects of demineralization and remineralization of the enamel surface were compared by means of surface microhardness (SMH) measurements, surface color change measurements, fluorescence microscopy (FM), X-ray diffraction (XRD) analysis and scanning electron microscopy (SEM).Results: There was no significant difference in the surface microhardness recovery rate (SMHRR) or color recovery rate (CRR) among the CPP-ACP group, PAA-ACP@aMBG group and MBG group (P>0.05), but these values were significantly higher than those in the NC group (p < 0.01). FM demonstrated that the remineralization depth in the PAA-ACP@aMBG group was significantly greater than that of the remaining three groups (p < 0.01). SEM analysis indicated that the enamel demineralization marks in the PAA-ACP@aMBG group, CPP-ACP group, and MBG group were obscured by mineral deposition.Conclusions: PAA-ACP@aMBG showed good mineralization properties, implying its great potential for clinical application.

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Summary Varying material particle sizes represent a common contributing factor to the batch‐to‐batch variation of extraction yields. To increase the batch‐to‐batch quality consistency of extracts, a method was proposed to adjust the extraction conditions for different material particle sizes, taking the hydrodistillation extraction process of Radix Curcumae and Fructus Gardeniae as a case study. Statistical models were built for five sesquiterpenes including curcumenone, curcumenol, curdione, curzerenone and furanodienone, to quantitatively describe the effects of particle size and process parameters on their extraction yields. An increase in sodium chloride concentration remarkably increased the yields of curcumenone and curcumenol, and a larger solvent‐to‐solid ratio increased the yields of five sesquiterpenes. Under the adjusted process parameters for two different particle sizes, the yield for each compound was controlled to fall in the 90–110% target range. The proposed method can be applied to various extraction processes of foods and herbal medicines.

As machine learning becomes increasingly important in engineering and science, it is inevitable that its techniques will be applied to the investigation of materials, and in particular the structural phase transitions common in ferroelectric materials. Here, we build and train an artificial neural network to accurately predict the energy change associated with atom displacements and use the trained artificial neural network in Monte-Carlo simulations on ferroelectric materials to understand their phase transitions. We apply this approach to two dimensional single-layer SnTe and shows that it can indeed be used to simulate the phase transitions and predict the transition temperature. The artificial neural network, when viewed as a universal mathematical structure, can be readily transferred to the investigation of other ferroelectric materials when training data generated with ab initio methods are available.