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Periodontal diseases are prevalent worldwide and are linked to numerous other health conditions due to dysbiosis and chronic inflammatory state. Most periodontal diseases are caused by pathogenic bacteria that colonize dental tissues in the form of biofilm. Eradication of bacterial biofilms can be difficult to achieve due to the complex architecture of the teeth and gums which complicates the removal. Orthodontic wires and dental devices introduce additional hurdles to the adequate removal of biofilms by traditional methods since mechanical disruption via direct contact with toothbrush bristles, floss, and abrasive toothpaste is limited. Magnetically activated nanoparticles (NPs), specifically iron oxide nanoparticles (IONPs) that can be functionalized as antimicrobial particles and remotely controlled by magnetic fields, are of interest for oral biofilm eradication. We present data in multi-species bacterial cultures, established biofilms, human gingival keratinocytes, and human gingival fibroblast cells alone and in the presence of multispecies biofilm co-cultures to determine the safest, most efficacious IONP size ranges and treatment concentrations of active magnetic NPs for removal of dental biofilms. We report enhanced efficacy for IONPs coated with alginate vs. dextran, and small sizes (~8 nm vs. >20 nm in size) appear to exhibit enhanced antimicrobial efficacy. Human gingival keratinocyte (TIGK) cells in co-culture with treated and untreated multispecies biofilms in an in-vitro periodontitis model also exhibited a trend of reduced inflammatory markers in wells with IONP-treated biofilms. 

Research has explored the use of automatic text simplification (ATS), which consists of techniques to make text simpler to read, to provide reading assistance to Deaf and Hard-of-hearing (DHH) adults with various literacy levels. Prior work in this area has identified interest in and benefits from ATS-based reading assistance tools. However, no prior work on ATS has gathered judgements from DHH adults as to what constitutes complex text. Thus, following approaches in prior NLP work, this paper contributes new word-complexity judgements from 11 DHH adults on a dataset of 15,000 English words that had been previously annotated by L2 speakers, which we also augmented to include automatic annotations of linguistic characteristics of the words. Additionally, we conduct a supplementary analysis of the interaction effect between the linguistic characteristics of the words and the groups of annotators. This analysis highlights the importance of collecting judgements from DHH adults for training ATS systems, as it revealed statistically significant interaction effects for nearly all of the linguistic characteristics of the words.