Effects of hydroxyapatite-coated porous titanium scaffolds functionalized by exosomes on the regeneration and repair of irregular bone.

As a traditional bone implant material, titanium (Ti) and its alloys have the disadvantages of lack of biological activity and susceptibility to stress shielding effect. Adipose stem cells (ADSCs) and exosomes were combined with the scaffold material in the current work to effectively create a hydroxyapatite (HA) coated porous titanium alloy scaffold that can load ADSCs and release exosomes over time. The composite made up for the drawbacks of traditional titanium alloy materials with higher mechanical characteristics and a quicker rate of osseointegration. Exosomes (Exos) are capable of promoting the development of ADSCs in porous titanium alloy scaffolds with HA coating, based on experimental findings from in vitro and in vivo research. Additionally, compared to pure Ti implants, the HA scaffolds loaded with adipose stem cell exosomes demonstrated improved bone regeneration capability and bone integration ability. It offers a theoretical foundation for the combined use of stem cell treatment and bone tissue engineering, as well as a design concept for the creation and use of novel clinical bone defect repair materials.

High-security photoacoustic identity recognition by capturing hierarchical vascular structure of finger.

Currently, most biometric methods mainly use single features, making them easily forged and cracked. In this study, a novel triple-layers biometric recognition method, based on photoacoustic microscopy, is proposed to improve the security of biometric identity recognition. Using the photoacoustic (PA) dermoscope, three-dimensional absorption-structure information of the fingers was obtained. Then, by combining U-Net, Gabor filtering, wavelet analysis and morphological transform, a lightweight algorithm called photoacoustic depth feature recognition algorithm (PADFR) was developed to automatically realize stratification (the fingerprint, blood vessel fingerprint and venous vascular), extracting feature points and identity recognition. The experimental results show that PADFR can automatically recognize the PA hierarchical features with an average accuracy equal to 92.99%. The proposed method is expected to be widely used in biometric identification system due to its high security.