Three-dimensional (3D) printing technology, known as additive manufacturing, is an advanced technology that rapidly developed in the late 20^th century. In the medical field, 3D printing technology has achieved breakthrough progress in medical model manufacturing, implant placement, and prosthodontics and orthodontics. In this paper, the application of 3D printing technology from the perspective of orthodontics, such as dental models, clear aligners, orthodontic brackets, transfer trays, and removable appliances, is reviewed. The application of 3D printing technology is increasingly used in the orthodontic field due to its advantages of high accuracy, high efficiency, simple operation and personalization, and an increasing number of economic applications in the medical field are worth the wait.

As the world becomes older, sustainable healthy aging becomes an important goal of social development. Robotic technologies have been widely considered an effective solution to reduce the labor demand and cost in aged care, thus providing satisfiable services to the elderly while keeping the cost low. The global outbreak of corona virus disease 2019 (COVID-19) has strengthened this trend when it impacted the elderly most because (1) the elderly was generally the most vulnerable population group to pandemics, and (2) the resources available to the elderly significantly declined due to lockdown and quarantines. The observations and experience from COVID-19 inspired us to consider the impact of pandemics on sustainable healthy aging, which was largely missing in existing work, leading to the study of the use of robots in general and telepresence robots in specific to aid sustainable healthy aging. The methodology of systematic review is applied to retrieve and analyze the articles published in nine databases between 2010 and 2020. Based on the review, the paper classifies the applications of robots in pandemics into four main categories, including healthcare, social support, education, and manufacturing. Further analysis of these applications revealed the missing features and challenges in applying them to healthy aging. The discoveries and findings in this paper provide practical guidelines for the future design and development of (telepresence) robots.

Background and Purpose: Malignant melanoma is a high-grade skin cancer with high feasibility to metastasize to both regional and distant sites when detected late. Therefore, it is crucial to diagnose this type of cancer at an early stage to ensure effective treatment. The identification of melanocytic lesions is a difficult issue, even for experienced experts. The current development of information technology, particularly related to image analysis and machine learning, is an opportunity to support the work of specialists and detect malignant melanoma more effectively. The aim of this work is to present a melanocytic skin lesion ontology (MLSO) structure, which serves as a basis for a melanoma diagnosis system and includes the formalization of the experts' and literature knowledge. Subjects and Methods: MLSO describes the most commonly used melanoma assessing strategies: Argenziano's (also known as the 7-point checklist), Menzies', and Stolz's (based on the ABCD rule) strategies as well as Chaos and Clues. Results: In this work, a case study was conducted on the description of a dermatoscopic digital image of a melanocytic skin nevus. The nevus was evaluated according to all of the strategies included in the MLSO, and inferences were made based on these strategies. The analyzed lesion was classified as a benign nevus since no malignancy was indicated by any of the applied strategies. Conclusion: Initial results indicate the usefulness of MLSO in diagnosing skin cancer. A significant advantage of MLSO is that it provides results obtained using four strategies. Therefore, the results are more objective and the possible errors may be avoided. The MLSO structure is still developing and will be implemented into an automated skin cancer diagnosis system.