Background: Twitter, representing a big social media network, is broadly used for the communication of health-related information. In this work, we aimed to identify and analyze the scientific literature on Twitter use in context of health by utilizing a bibliometric approach, in order to obtain quantitative information on dominant research topics, trending themes, key publications, scientific institutions, and prolific researchers who contributed to this scientific area.Methods: Web of Science electronic database was searched to identify relevant papers on Twitter and health. Basic bibliographic data was obtained utilizing the “Analyze” function of the database. Full records and cited references were exported to VOSviewer, a dedicated bibliometric software, for further analysis. A term map and a keyword map were synthesized to visualize recurring words within titles, abstracts and keywords.Results: The analysis was based on the data from 2,582 papers. The first papers were published in 2009, and the publication count increased rapidly since 2015. Original articles and reviews were published in a ratio of 10.6:1. The Journal of Medical Internet Research was the top journal, and the United States had contributions to over half (52%) of these publications, being the home-country of eight of the top ten most productive institutions. Keyword analysis identified six topically defined clusters, with professional education in healthcare being the top theme cluster (consisting of 66 keywords). The identified papers often investigated Twitter together with other social media, such as YouTube and Facebook.Conclusions: A great diversity of themes was found in the identified papers, including: professional education in healthcare, big data and sentiment analysis, social marketing and substance use, physical and emotional well-being of young adults, and public health and health communication. Our quantitative analysis outlines Twitter as both, an increasingly popular data source, and a highly versatile tool for health-related research.

Personalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one’s biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial intelligence (AI) technologies with improved data analytic approaches are expected to integrate diverse data from the individual, setting, and system levels, which may facilitate a deeper understanding of the interaction of these multi level data and therefore bring us closer to more personalized, predictive, preventive, and participatory dentistry, also known as P4 dentistry. In the field of dentomaxillofacial imaging, a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists. Notably, the impact of these dental AI applications on treatment decision, clinical and patient-reported outcomes, and cost-effectiveness has so far been assessed sparsely. Such information should be further investigated in future studies to provide patients, providers, and healthcare organizers a clearer picture of the true usefulness of AI in daily dental practice.

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Objectives: To investigate the dose-area product (DAP) of cone-beam computed tomography (CBCT) examinations for different scan settings and imaging indications, and to establish institutional diagnostic reference levels (DRLs) for dose optimization. Methods: A retrospective analysis of the DAP values of 3568 CBCT examinations taken from two different devices at the Prince Philip Dental Hospital, Hong Kong between 2016 and 2021 was performed. Patient- (age, gender, and imaging indication) and imaging-related (CBCT device, field-of-view (FOV), and voxel size) were correlated with the DAPs. The indication-oriented third-quartile DAP values were compared with DRLs from the UK, Finland, and Switzerland. The obtained third-quartile DAPs lower than the national DRLs and those for which no national DRLs have been proposed were used to establish institutional DRLs. Results: In the investigated CBCTs, the DAP value for large FOV scans was significantly lower than medium/small FOVs. CBCTs with a small voxel size exhibited a significantly higher DAP than those with a medium/large voxel size. CBCTs for endodontic, periodontal, orthodontic, or orthognathic evaluation exhibited a significantly higher DAP than other indications. Twelve indication-oriented institutional DRLs were established and five of them were lower than the national DRLs: third molars (229 mGy×cm2), jaw cysts/tumors (410 mGy×cm2), maxillary sinus pathology (520 mGy×cm2), developing dentition (164 mGy×cm2), and periapical lesions (564 mGy×cm2). Conclusions: CBCT examinations for endodontic, periodontal, orthodontic, or orthognathic evaluation may deliver a higher radiation dose to the patient than other imaging tasks. A periodic review of the patient dose from CBCT imaging and establishment of institutional DRLs for specific clinical settings are needed for monitoring patient dose and to optimize indication-oriented scanning protocols.

ATP-binding cassette subfamily A member 1 (ABCA1) protein plays an essential role in a variety of events, such as cholesterol and phospholipid efflux, nascent high-density lipoprotein (HDL) biosynthesis, phospholipid translocation. Thus, there has been much research activity aimed at understanding the molecular mechanisms of regulating ABCA1 expression. In this review, we first discuss ABCA1 structure, tissue distribution, cellular localization and trafficking, as well as its function. Furthermore, current understanding of the molecular mechanisms involved in the regulation of ABCA1 expression are summarized. ABCA1 transcriptional regulation is mediated by a very complicated system, including nuclear receptor systems, factors binding to other sites in the ABCA1 promoter, cytokines, hormones, growth factors, lipid metabolites, enzymes, and other messengers/factors/pathways. In addition, ABCA1 post-transcriptional regulation is mediated by microRNA, long noncoding RNA, RNA-binding proteins, proteases, fatty acids, PDZ proteins, signaling proteins, and other factors. Compared to the transcriptional regulation of ABCA1 which is well established, the post-transcriptional regulation of ABCA1 expression is poorly understood.