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Stem cell transplantation is a promising therapeutic strategy that includes both cell therapy and tissue engineering for the treatment of many regenerative diseases; however, the efficacy and safety of stem cell therapy depend on the cell type used in therapeutic and translational applications. In this study, we validated the hypothesis that human nasal turbinate-derived mesenchymal stem cells (hTMSCs) are a potential therapeutic source of adult stem cells for clinical use in bone tissue engineering using three-dimensional (3D) cell-printing technology. hTMSCs were cultured and evaluated for clinical use according to their cell growth, cell size, and preclinical safety and were then incorporated into a multicompositional 3D bioprinting system and investigated for bone tissue regeneration in vitro and in vivo. Finally, hTMSCs were compared with human bone marrow-derived MSCs (hBMSCs), which are the most common stem cell type used in regenerative medicine. hTMSCs from three different donors showed greater and faster cell growth than hBMSCs from two different donors when cultured. The hTMSCs were smaller in size than the hBMSCs. Furthermore, the hTMSCs did not exhibit safety issues in immunodeficient mice. hTMSCs in 3D-printed constructs (3D-hTMSC) showed much greater viability, growth, and osteogenic differentiation potential in vitro than hBMSCs in 3D-printed constructs (3D-hBMSC). Likewise, 3D-hTMSC showed better cell survival and alkaline phosphatase activity and greater osteogenic protein expression than 3D-hBMSC upon subcutaneous implantation into the dorsal region of nude mice. Notably, in an orthotopic model involving implantation into a tibial defect in rats, implantation of 3D-hTMSC led to greater bone matrix formation and enhanced bone healing to a greater degree than implantation of 3D-hBMSC. The clinically reliable evidence provided by these results is underlined by the potential for rapid tissue regeneration and ambulation in bone fracture patients implanted with 3D-hTMSC.

Background Stem cell transplantation is a fascinating therapeutic approach for the treatment of many neurodegenerative disorders; however, clinical trials using stem cells have not been as effective as expected based on preclinical studies. The aim of this study is to validate the hypothesis that human neural crest-derived nasal turbinate stem cells (hNTSCs) are a clinically promising therapeutic source of adult stem cells for the treatment of Alzheimer’s disease (AD). Methods hNTSCs were evaluated in comparison with human bone marrow-derived mesenchymal stem cells (hBM-MSCs) according to the effect of transplantation on AD pathology, including PET/CT neuroimaging, immune status indicated by microglial numbers and autophagic capacity, neuronal survival, and cognition, in a 5 × FAD transgenic mouse model of AD. Results We demonstrated that hNTSCs showed a high proliferative capacity and great neurogenic properties in vitro. Compared with hBM-MSC transplantation, hNTSC transplantation markedly reduced Aβ42 levels and plaque formation in the brains of the 5 × FAD transgenic AD mice on neuroimaging, concomitant with increased survival of hippocampal and cortex neurons. Moreover, hNTSCs strongly modulated immune status by reducing the number of microglia and the expression of the inflammatory cytokine IL-6 and upregulating autophagic capacity at 7 weeks after transplantation in AD models. Notably, compared with transplantation of hBM-MSCs, transplantation of hNTSCs significantly enhanced performance on the Morris water maze, with an increased level of TIMP2, which is necessary for spatial memory in young mice and neurons; this difference could be explained by the high engraftment of hNTSCs after transplantation. Conclusion The reliable evidence provided by these findings reveals a promising therapeutic effect of hNTSCs and indicates a step forward the clinical application of hNTSCs in patients with AD.

In augmented reality (AR) or virtual reality (VR) systems, eye tracking is a key technology and requires significant accuracy as well as real-time operation. Many techniques for detecting pupil centers with error range of iris radius have been developed, but few techniques have precise performance with error range of pupil radius. In addition, the conventional methods rarely guarantee real-time pupil center detection in a general-purpose computer environment due to high complexity. Thus, we propose more accurate pupil center detection by improving the representation quality of the network in charge of pupil center detection. This is realized by representation learning based on mutual information. Also, the latency of the entire system is greatly reduced by using non-local block and self-attention block with large receptive field, which makes it accomplish real-time operation. The proposed system not only shows real-time performance of 52 FPS in a general-purpose computer environment but also provides state-of-the-art accuracy in terms of fine level index of 96.71%, 99.84% and 96.38% for BioID, GI4E and Talking Face Video datasets, respectively.