Dataset Information

Interference with AI-2-mediated bacterial cell-cell communication.

ABSTRACT: Bacteria communicate by means of chemical signal molecules called autoinducers. This process, called quorum sensing, allows bacteria to count the members in the community and to alter gene expression synchronously across the population. Quorum-sensing-controlled processes are often crucial for successful bacterial--host relationships--both symbiotic and pathogenic. Most quorum-sensing autoinducers promote intraspecies communication, but one autoinducer, called AI-2, is produced and detected by a wide variety of bacteria and is proposed to allow interspecies communication. Here we show that some species of bacteria can manipulate AI-2 signalling and interfere with other species' ability to assess and respond correctly to changes in cell population density. AI-2 signalling, and the interference with it, could have important ramifications for eukaryotes in the maintenance of normal microflora and in protection from pathogenic bacteria.

SUBMITTER: Xavier KB

PROVIDER: S-EPMC1388276 | biostudies-literature | 2005 Sep

REPOSITORIES: biostudies-literature

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Interference with AI-2-mediated bacterial cell-cell communication.

Xavier Karina B KB Bassler Bonnie L BL

Nature 20050901 7059

Bacteria communicate by means of chemical signal molecules called autoinducers. This process, called quorum sensing, allows bacteria to count the members in the community and to alter gene expression synchronously across the population. Quorum-sensing-controlled processes are often crucial for successful bacterial--host relationships--both symbiotic and pathogenic. Most quorum-sensing autoinducers promote intraspecies communication, but one autoinducer, called AI-2, is produced and detected by a ...[more]

PMID: 16193054

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Project description:BackgroundSince the mid-2010s, use of conversational artificial intelligence (AI; chatbots) in health care has expanded significantly, especially in the context of increased burdens on health systems and restrictions on in-person consultations with health care providers during the COVID-19 pandemic. One emerging use for conversational AI is to capture evolving questions and communicate information about vaccines and vaccination.ObjectiveThe objective of this systematic review was to examine documented uses and evidence on the effectiveness of conversational AI for vaccine communication.MethodsThis systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, Web of Science, PsycINFO, MEDLINE, Scopus, CINAHL Complete, Cochrane Library, Embase, Epistemonikos, Global Health, Global Index Medicus, Academic Search Complete, and the University of London library database were searched for papers on the use of conversational AI for vaccine communication. The inclusion criteria were studies that included (1) documented instances of conversational AI being used for the purpose of vaccine communication and (2) evaluation data on the impact and effectiveness of the intervention.ResultsAfter duplicates were removed, the review identified 496 unique records, which were then screened by title and abstract, of which 38 were identified for full-text review. Seven fit the inclusion criteria and were assessed and summarized in the findings of this review. Overall, vaccine chatbots deployed to date have been relatively simple in their design and have mainly been used to provide factual information to users in response to their questions about vaccines. Additionally, chatbots have been used for vaccination scheduling, appointment reminders, debunking misinformation, and, in some cases, for vaccine counseling and persuasion. Available evidence suggests that chatbots can have a positive effect on vaccine attitudes; however, studies were typically exploratory in nature, and some lacked a control group or had very small sample sizes.ConclusionsThe review found evidence of potential benefits from conversational AI for vaccine communication. Factors that may contribute to the effectiveness of vaccine chatbots include their ability to provide credible and personalized information in real time, the familiarity and accessibility of the chatbot platform, and the extent to which interactions with the chatbot feel "natural" to users. However, evaluations have focused on the short-term, direct effects of chatbots on their users. The potential longer-term and societal impacts of conversational AI have yet to be analyzed. In addition, existing studies do not adequately address how ethics apply in the field of conversational AI around vaccines. In a context where further digitalization of vaccine communication can be anticipated, additional high-quality research will be required across all these areas.

Project description:ImportanceTimely tests are warranted to assess the association between generative artificial intelligence (GenAI) use and physicians' work efforts.ObjectiveTo investigate the association between GenAI-drafted replies for patient messages and physician time spent on answering messages and the length of replies.Design, setting, and participantsRandomized waiting list quality improvement (QI) study from June to August 2023 in an academic health system. Primary care physicians were randomized to an immediate activation group and a delayed activation group. Data were analyzed from August to November 2023.ExposureAccess to GenAI-drafted replies for patient messages.Main outcomes and measuresTime spent (1) reading messages, (2) replying to messages, (3) length of replies, and (4) physician likelihood to recommend GenAI drafts. The a priori hypothesis was that GenAI drafts would be associated with less physician time spent reading and replying to messages. A mixed-effects model was used.ResultsFifty-two physicians participated in this QI study, with 25 randomized to the immediate activation group and 27 randomized to the delayed activation group. A contemporary control group included 70 physicians. There were 18 female participants (72.0%) in the immediate group and 17 female participants (63.0%) in the delayed group; the median age range was 35-44 years in the immediate group and 45-54 years in the delayed group. The median (IQR) time spent reading messages in the immediate group was 26 (11-69) seconds at baseline, 31 (15-70) seconds 3 weeks after entry to the intervention, and 31 (14-70) seconds 6 weeks after entry. The delayed group's median (IQR) read time was 25 (10-67) seconds at baseline, 29 (11-77) seconds during the 3-week waiting period, and 32 (15-72) seconds 3 weeks after entry to the intervention. The contemporary control group's median (IQR) read times were 21 (9-54), 22 (9-63), and 23 (9-60) seconds in corresponding periods. The estimated association of GenAI was a 21.8% increase in read time (95% CI, 5.2% to 41.0%; P = .008), a -5.9% change in reply time (95% CI, -16.6% to 6.2%; P = .33), and a 17.9% increase in reply length (95% CI, 10.1% to 26.2%; P < .001). Participants recognized GenAI's value and suggested areas for improvement.Conclusions and relevanceIn this QI study, GenAI-drafted replies were associated with significantly increased read time, no change in reply time, significantly increased reply length, and some perceived benefits. Rigorous empirical tests are necessary to further examine GenAI's performance. Future studies should examine patient experience and compare multiple GenAIs, including those with medical training.

Dataset Information

Interference with AI-2-mediated bacterial cell-cell communication.

Publications

Interference with AI-2-mediated bacterial cell-cell communication.

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