＜Editors' Choice＞ Developing online lectures using text mining reduces health workers' anxiety in non-epicenter areas of COVID-19.

COVID-19 is indirectly associated with various mental disorders such as anxiety, insomnia, and depression, and healthcare professionals who treat COVID-19 patients are particularly prone to severe anxiety. However, neither the anxiety of healthcare workers in non-epicenter areas nor the effects of knowledge support have been examined thus far. Participants were 458 staff working at the Toyota Regional Medical Center who completed a preliminary questionnaire of their knowledge and anxiety regarding COVID-19. Based on text mining of the questionnaire responses, participants were offered an online lecture. The effect of the lecture was analyzed using a pre- and post-lecture rating of anxiety and knowledge confidence, and quantitative text mining. The response rates were 45.6% pre- and 62.9% post-lecture. Open-ended responses regarding anxiety and knowledge were classified into seven clusters using a co-occurrence network. Before the lecture, 28.2%, 27.2%, and 20.3% of participants were interested in and anxious about "infection prevention and our hospital's response," "infection and impact on myself, family, and neighbors," and "general knowledge of COVID-19," respectively. As a result of the lecture, Likert-scale ratings for anxiety of COVID-19 decreased significantly and knowledge confidence increased significantly. These changes were confirmed by analyses of open-ended responses about anxiety, lifestyle changes, and knowledge. Positive changes were strongly linked to the topics focused on in the lecture, especially infection prevention. The anxieties about COVID-19 of healthcare workers in non-epicenter areas can be effectively reduced through questionnaire surveys and online lectures using text mining.

Clinical characteristics of Corynebacterium simulans.

Corynebacterium simulans was first reported in 2000. Its characteristics such as isolation frequency, specimen types, and antimicrobial susceptibilities are poorly understood, because identification is difficult using conventional methods. We performed a retrospective observational study of 13 and 317 strains of C. simulans and C. striatum, respectively, isolated from consecutive patients at Nagoya University Hospital from January 2017 to December 2018. We analyzed patients' backgrounds, types of specimens, and antimicrobial susceptibilities. Antimicrobial susceptibilities were compared with those of C. striatum. The frequencies of isolation of C. simulans and C. striatum were 3.9% and 96%, respectively. C. simulans was not detected in specimens associated with mucous membranes, such as sputum and secretions from the craniocervical region, which were frequent for C. striatum. C. simulans was mainly detected in the skin (61.5%). All C. simulans isolates were susceptible to anti-MRSA drugs, as well as to numerous other antibiotics, including those that are orally administered. For example, C. simulans was significantly more susceptible to penicillin G, ceftriaxone, and ciprofloxacin than C. striatum (respective susceptibilities: 66.7% vs 5.4%, 50.0% vs 4.0%, 66.7% vs 5.9%). There was no significant difference between meropenem and erythromycin, although susceptibility to each was relatively high (100.0% vs 31.7%, 50.0% vs 11.9%). C. simulans was susceptible to numerous orally administered antibiotics and more susceptible to antimicrobial drugs than C. striatum. C. simulans was detected less frequently than C. striatum and was infrequently detected in specimens associated with mucous membranes. These characteristics will aid the selection of optimal antimicrobial therapies.