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Oral mucositis (OM) is a common complication of chemotherapy and remains a significant unmet need. The aim of this study was to investigate the role of oral bacteriota and HSV-1 in OM. Forty-six patients admitted for autologous hematopoietic stem cell transplantation were longitudinally evaluated for OM, Candida, HSV-1, and leukocyte count, and buccal mucosal bacterial samples were obtained during their admission period. The bacterial communities collected at the baseline and post-chemotherapy, chosen from the time with the highest severity, were analyzed by sequencing the 16S rRNA gene. Twenty (43.5%) patients developed OM, the severity of which ranged from 1 to 5 according to the Oral Mucositis Assessment Scale (OMAS). Chemotherapy significantly increased the prevalence of HSV-1 detection but not that of Candida. The bacterial communities of patients after conditioning chemotherapy were characterized by aberrant enrichment of minor species and decreased evenness and Shannon diversity. After adjustment for age, gender, and neutropenia, the presence of HSV-1 was associated with the incidence of OM (odds ratio = 3.668, p = 0.004), while the decrease in Shannon diversity was associated with the severity of OM (β = 0.533 ± 0.220, p = 0.015). The control of HSV-1 and restoration of oral bacterial diversity may be a novel option to treat or prevent OM.

BackgroundAs studies analyzing the networks and relational structures of research topics in academic fields emerge, studies that apply methods of network and relationship analysis, such as social network analysis (SNA), are drawing more attention. The purpose of this study is to explore the interaction of medical education subjects in the framework of complex systems theory using SNA and to analyze the trends in medical education.MethodsThe authors extracted keywords using Medical Subject Headings terms from 9,379 research articles (162,866 keywords) published in 1963–2015 in PubMed. They generated an occurrence frequency matrix, calculated relatedness using Weighted Jaccard Similarity, and analyzed and visualized the networks with Gephi software.ResultsNewly emerging topics by period units were identified as historical trends, and 20 global-level topic clusters were obtained through network analysis. A time-series analysis led to the definition of five historical periods: the waking phase (1963–1975), the birth phase (1976–1990), the growth phase (1991–1996), the maturity phase (1997–2005), and the expansion phase (2006–2015).ConclusionsThe study analyzed the trends in medical education research using SNA and analyzed their meaning using complex systems theory. During the 53-year period studied, medical education research has been subdivided and has expanded, improved, and changed along with shifts in society’s needs. By analyzing the trends in medical education using the conceptual framework of complex systems theory, the research team determined that medical education is forming a sense of the voluntary order within the field of medicine by interacting with social studies, philosophy, etc., and establishing legitimacy and originality.Electronic supplementary materialThe online version of this article (10.1186/s12909-018-1323-y) contains supplementary material, which is available to authorized users.

The Death Domain (DD) superfamily, which is one of the largest classes of protein interaction modules, plays a pivotal role in apoptosis, inflammation, necrosis and immune cell signaling pathways. Because aberrant or inappropriate DD superfamily-mediated signaling events are associated with various human diseases, such as cancers, neurodegenerative diseases and immunological disorders, the studies in these fields are of great biological and clinical importance. To facilitate the understanding of the molecular mechanisms by which the DD superfamily is associated with biological and disease processes, we have developed the DD database (http://www.deathdomain.org), a manually curated database that aims to offer comprehensive information on protein–protein interactions (PPIs) of the DD superfamily. The DD database was created by manually curating 295 peer-reviewed studies that were published in the literature; the current version documents 175 PPI pairs among the 99 DD superfamily proteins. The DD database provides a detailed summary of the DD superfamily proteins and their PPI data. Users can find in-depth information that is specified in the literature on relevant analytical methods, experimental resources and domain structures. Our database provides a definitive and valuable tool that assists researchers in understanding the signaling network that is mediated by the DD superfamily.