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A three-level hierarchical calcium phosphate/collagen/hydroxyapatite (CaP/Col/HAp) scaffold for bone tissue engineering was developed using biomimetic synthesis. Porous CaP ceramics were first prepared as substrate materials to mimic the porous bone structure. A second-level Col network was then composited into porous CaP ceramics by vacuum infusion. Finally, a third-level HAp layer was achieved by biomimetic mineralization. The three-level hierarchical biomimetic scaffold was characterized using scanning electron microscopy, energy-dispersive x-ray spectra, x-ray diffraction and Fourier transform infrared spectroscopy, and the mechanical properties of the scaffold were evaluated using dynamic mechanical analysis. The results show that this scaffold exhibits a similar structure and composition to natural bone tissues. Furthermore, this three-level hierarchical biomimetic scaffold showed enhanced mechanical strength compared with pure porous CaP scaffolds. The biocompatibility and osteoinductivity of the biomimetic scaffolds were evaluated using in vitro and in vivo tests. Cell culture results indicated the good biocompatibility of this biomimetic scaffold. Faster and increased bone formation was observed in these scaffolds following a six-month implantation in the dorsal muscles of rabbits, indicating that this biomimetic scaffold exhibits better osteoinductivity than common CaP scaffolds.

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Contact tracing APPs have been recently advocated by many countries (e.g., the United Kingdom, Australia, etc.) as part of control measures on COVID-19. Controversies have been raised about their effectiveness in practice as it still remains unclear how they can be fully utilized to fuel the fight against COVID-19. In this article, we show that an abundance of information can be extracted from contact tracing for COVID-19 prevention and control, providing the first data-driven evidence that supports the wide implementation of such APPs. Specifically, we construct a temporal contact graph that quantifies the daily contacts between infectious and susceptible individuals by exploiting a large volume of location related data contributed by 10,527,737 smartphone users in Wuhan, China. Five time-varying indicators we introduce can accurately capture actual contact trends at individual and population levels, demonstrating that travel restriction in Wuhan played an important role in containing COVID-19. We reveal a strong correlation (Pearson coefficient 0.929) between daily confirmed cases and daily total contacts, which can be utilized as a new and efficient way to evaluate and predict the evolving epidemic situation of COVID-19. Further, we find that there is a prominent distinction of contact behaviors between the infected and uninfected contacted individuals, and design an infection risk evaluation framework to identify infected ones. This can help narrow down the search of high risk contacted individuals for quarantine. Our results indicate that user involvement has an explicit impact on individual-level contact trend estimation while minor impact on situation evaluation, offering guidelines for governments to implement contact tracing APPs.