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A novel method was developed for calcium deficient hydroxyapatite (CDHA) scaffold 3D printing, through which a bilayer scaffold was fabricated by the integral molding of individual CDHA and poly(lactic-glycolic acid copolymer) (PLGA). The hydration reaction of α-tricalcium phosphate (TCP) was utilized to form CDHA, and a mixed solution of gelatin, glycerine and glutaraldehyde was applied as the dispersant and adhesive. The concentration of the glutaraldehyde (1‰(v/v)) and the mixing ratio of α-TCP (0.6, 0.8, 1.0 and 1.2 g ml−1) were studied with regard to the effect on the forming ability of the CDHA ink. The influence of α-TCP proportion (0.6, 0.8, 1.0 and 1.2 g ml−1) on the formation of CDHA was also researched in phase analysis, morphology and compressive strength measurements. The CDHA/PLGA bilayer scaffold was fabricated with a good combination of the two components by 3D printing. The in vitro degradation, cytotoxicity and cell proliferation behavior were studied. Meanwhile, the in-vivo performances in terms of surgical safety, biodegradation and osteogenic capacity were investigated with a cortical bone defect model in a rabbit femur. The results showed that the CDHA/PLGA bilayer scaffold had excellent biocompatibility and no cytotoxicity. The scaffolds were successfully implanted and presented remarkable osteogenic capacity within 6 months through analyses in radiography and histology. In conclusion, the method has a potential clinical application in diverse bone repair practices by varied 3D-printing fabrication.

Text steganography has become a hotspot in the field of information security in recent years. As an important steganographic carrier, text does not have the advantage that other multimedia could easily use more redundancy to hide information. Traditional methods were realized by changing the appearance attributes of text or by equivalent replacement based on grammar and semantics, but there were some shortcomings more or less. Based on Markov chain model, this paper proposed a coverless text steganography method. In order to enhance the effect of steganography, the method focused on the most important concepts in Markov model, the transition probability, and guided the generation of steganographic text based on it. The relevant experiments were designed to evaluate the model. The experimental results showed that the model had higher concealment and hiding capacity.

As a carrier of information hiding, text is difficult to hide secret because of its less redundancy. The robustness and security of some traditional methods based text are not high. Based on Markov chain model, a coverless text steganography algorithm was proposed in this paper. According to the characteristic and value of transition probability in the model, the algorithm used the maximum variable bit embedding instead of the usual fixed bit embedding. In order to verify the effectiveness of the algorithm, different datasets and algorithms were selected to test. By comparing with results, the proposed model had higher concealment and hidden capacity.