Accurate detection and early diagnosis of oral diseases such as dental caries and periodontitis, can be potentially achieved by detecting the secretion of volatile sulfur compounds (VSCs) in oral cavities. Current diagnostic approaches for VSCs can detect the existence and concentrations, yet are not capable of locating the dental lesion sites. Herein, the development of a unique approach for accurately locating dental lesion sites using a fluorescent mouthguard consisting of the zinc oxide–poly(dimethylsiloxane) (ZnO‐PDMS) nanocomposite to detect the local release of VSCs is reported. The ZnO‐PDMS mouthguard displays a highly sensitive and selective response to VSCs, and exhibits high fluorescent stability, good biocompatibility, and low biological toxicity in normal physiological environments. Then, the wearable ZnO‐PDMS mouthguard is demonstrated to be able to identify the precise locations of lesion sites in human subjects. Combined with image analysis, the mouthguards successfully uncover the precise locations of dental caries, allowing convenient screening of hidden dental lesion sites that are oftentimes omitted by dentists. Due to low cost, long‐term stability, and good patient compliance, the proposed wearable mouthguard is suitable for large‐scale production and enables widely applicable, preliminary yet accurate screening of dental lesions prior to dental clinics and routine physical examinations.

The 3D bioprinting technologies have attracted increasing attention due to their flexibility in producing architecturally relevant tissue constructs. Here, a vertical embedded extrusion bioprinting strategy using uniaxial or coaxial nozzles is presented, which allows formation of vertical structures of homogeneous or heterogeneous properties. By adjusting the bioprinting parameters, the characteristics of the bioprinted vertical patterns can be precisely controlled. Using this strategy, two proof‐of‐concept applications in tissue biofabrication are demonstrated. Specifically, intestinal villi and hair follicles, two liner‐shaped tissues in the human body, are successfully generated with the vertical embedded bioprinting method, reconstructing some of their key structures as well as restoring partial functions in vitro. Caco‐2 cells in the bioprinted intestinal villus constructs proliferated and aggregated properly, also showing functional biomarker expressions such as ZO‐1 and villin. Moreover, preliminary hair follicle structures featuring keratinized human keratinocytes and spheroid‐shaped human dermal papilla cells are formed after vertical bioprinting and culturing. In summary, this vertical embedded extrusion bioprinting technique harnessing a uniaxial or coaxial format will likely bring further improvements in the reconstruction of certain human tissues and organs, especially those with a linear structure, potentially leading to wide utilities in tissue engineering, tissue model engineering, and drug discovery.

In 3D (bio)printing, it is critical to optimize the printing conditions to obtain scaffolds with designed structures and good uniformities. Traditional approaches for optimizing the parameters oftentimes rely on the prior knowledge of the operators and tedious optimization experiments, which can be both time‐consuming and labor‐intensive. Moreover, with the rapid increase in the types of biomaterial inks and the geometrical complexities of the scaffolds to be fabricated, such a traditional strategy may prove less effective. To address the challenge, an artificial intelligence‐assisted high‐throughput printing‐condition‐screening system (AI‐HTPCSS) is proposed, which is composed of a programmable pneumatic extrusion (bio)printer and an AI‐assisted image‐analysis algorithm. Based on the AI‐HTPCSS, the printing conditions for obtaining uniformly structured hydrogel architectures are screened in a high‐throughput manner. The results show that the scaffolds printed under the optimized conditions demonstrate satisfying mechanical properties, in vitro biological performances, and efficacy in accelerating the diabetic wound healing in vivo. The unique AI‐HTPCSS is expected to offer an enabling platform technology on streamlining the manufacturing of tissue‐engineering scaffolds through 3D (bio)printing techniques in the future.