An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights

How Far Can Conversational Agents Contribute to IBD Patient Health Care—A Review of the Literature

Modern societies are facing health and healthcare challenges as never seen before. The digital world in which we are living today considers digital health interventions such as “internet-delivered” therapy (e-Therapy) or mobile apps as an integrated part of healthcare systems. Digital transformation in health care requires the active involvement of patients as the central part of healthcare interventions. In the case of chronic health conditions, such as inflammatory bowel disease (IBD), it is believed that the adoption of new digital tools helps to maintain and extend the health and care of patients, optimizing the course of the treatment of the disease. The study goal was to undertake a literature review associating the use of chatbot technology with IBD patients' health care. This study intends to support digital product developments, mainly chatbot for IBD or other chronic diseases. The work was carried out through two literature review phases. The first one was based on a systematic approach and the second was a scoping review focused only on Frontiers Journals. This review followed a planned protocol for search and selection strategy that was created by a research team discussion. Chatbot technology for chronic disease self-management can have high acceptance and usability levels. The more interaction with a chatbot, the more patients are able to increase their self-care practice, but there is a challenge. The chatbot ontology to personalize the communication still needed to have strong guidelines helping other researchers to define which Electronic Medical Records (EMRs) should be used in the chatbots to improve the user satisfaction, engagement, and dialog quality. The literature review showed us both evidence and success of these tools in other health disorders. Some of them revealed a huge potential for conversational agents as a part of digital health interventions.

Filling the Void: An Optimized Polymicrobial Interkingdom Biofilm Model for Assessing Novel Antimicrobial Agents in Endodontic Infection

There is a growing realization that endodontic infections are often polymicrobial, and may contain Candida spp. Despite this understanding, the development of new endodontic irrigants and models of pathogenesis remains limited to mono-species biofilm models and is bacterially focused. The purpose of this study was to develop and optimize an interkingdom biofilm model of endodontic infection and use this to test suitable anti-biofilm actives. Biofilms containing Streptococcus gordonii, Fusobacterium nucleatum, Porphyromonas gingivalis, and Candida albicans were established from ontological analysis. Biofilms were optimized in different media and atmospheric conditions, prior to quantification and imaging, and subsequently treated with chlorhexidine, EDTA, and chitosan. These studies demonstrated that either media supplemented with serum were equally optimal for biofilm growth, which were dominated by S. gordonii, followed by C. albicans. Assessment of antimicrobial activity showed significant effectiveness of each antimicrobial, irrespective of serum. Chitosan was most effective (3 log reduction), and preferentially targeted C. albicans in both biofilm treatment and inhibition models. Chitosan was similarly effective at preventing biofilm growth on a dentine substrate. This study has shown that a reproducible and robust complex interkingdom model, which when tested with the antifungal chitosan, supports the notion of C. albicans as a key structural component.