COVID-19 and Antimicrobial Resistance: Data from the Greek Electronic System for the Surveillance of Antimicrobial Resistance-WHONET-Greece (January 2018-March 2021).

Changes in hospitals' daily practice due to COVID-19 pandemic may have an impact on antimicrobial resistance (AMR). We aimed to assess this possible impact as captured by the Greek Electronic System for the Surveillance of Antimicrobial Resistance (WHONET-Greece). Routine susceptibility data of 17,837 Gram-negative and Gram-positive bacterial isolates from blood and respiratory specimens of hospitalized patients in nine COVID-19 tertiary hospitals were used in order to identify potential differences in AMR trends in the last three years, divided into two periods, January 2018-March 2020 and April 2020-March 2021. Interrupted time-series analysis was used to evaluate differences in the trends of non-susceptibility before and after the changes due to COVID-19. We found significant differences in the slope of non-susceptibility trends of Acinetobacter baumannii blood and respiratory isolates to amikacin, tigecycline and colistin; of Klebsiella pneumoniae blood and respiratory isolates to meropenem and tigecycline; and of Pseudomonas aeruginosa respiratory isolates to imipenem, meropenem and levofloxacin. Additionally, we found significant differences in the slope of non-susceptibility trends of Staphylococcus aureus isolates to oxacillin and of Enterococcus faecium isolates to glycopeptides. Assessing in this early stage, through surveillance of routine laboratory data, the way a new global threat like COVID-19 could affect an already ongoing pandemic like AMR provides useful information for prompt action.

A rare case of polymicrobial chronic maxillary sinusitis due to concurrent isolation of Parvimonas micra and Fusobacterium nucleatum.

Parvimonas micra and Fusobacterium nucleatum are commensal pathogens very rarely isolated simultaneously in clinical specimens. We report a case of chronic maxillary sinusitis caused by these two pathogens, presumably resulting from a co-existing dental infection.

RNA editing generates cellular subsets with diverse sequence within populations.

RNA editing is a mutational mechanism that specifically alters the nucleotide content in transcribed RNA. However, editing rates vary widely, and could result from equivalent editing amongst individual cells, or represent an average of variable editing within a population. Here we present a hierarchical Bayesian model that quantifies the variance of editing rates at specific sites using RNA-seq data from both single cells, and a cognate bulk sample to distinguish between these two possibilities. The model predicts high variance for specific edited sites in murine macrophages and dendritic cells, findings that we validated experimentally by using targeted amplification of specific editable transcripts from single cells. The model also predicts changes in variance in editing rates for specific sites in dendritic cells during the course of LPS stimulation. Our data demonstrate substantial variance in editing signatures amongst single cells, supporting the notion that RNA editing generates diversity within cellular populations.