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Published on: March 19, 2019
Oral Fungal Infections: Past, Present, and Future
1Department of Oral Sciences, Sir John Walsh Research Institute, University of Otago, Dunedin, New Zealand.
Abstract:
Oral fungal infections have afflicted humans for millennia. Hippocrates (ca. 460-370 BCE) described two cases of oral aphthae associated with severe underlying diseases that could well have been oral candidiasis. While oral infections caused by other fungi such as cryptococcosis, aspergillosis, mucormycosis, histoplasmosis, blastomycosis, and coccidioidomycosis occur infrequently, oral candidiasis came to the fore during the AIDS epidemic as a sentinel opportunistic infection signaling the transition from HIV infection to AIDS. The incidence of candidiasis in immunocompromised AIDS patients highlighted the importance of host defenses in preventing oral fungal infections. A greater understanding of the nuances of human immune systems has revealed that mucosal immunity in the mouth delivers a unique response to fungal pathogens. Oral fungal infection does not depend solely on the fungus and the host, however, and attention has now focussed on interactions with other members of the oral microbiome. It is evident that there is inter-kingdom signaling that affects microbial pathogenicity. The last decade has seen significant advances in the rapid qualitative and quantitative analysis of oral microbiomes and in the simultaneous quantification of immune cells and cytokines. The time is ripe for the application of machine learning and artificial intelligence to integrate more refined analyses of oral microbiome composition (including fungi, bacteria, archaea, protozoa and viruses-including SARS-CoV-2 that causes COVID-19). This analysis should incorporate the quantification of immune cells, cytokines, and microbial cell signaling molecules with signs of oral fungal infections in order to better diagnose and predict susceptibility to oral fungal disease.
Insights
Oral fungal infections are influenced by the oral microbiome and host immunity. Advanced AI analysis of the oral microbiome, immune cells, and signaling molecules can improve diagnosis and prediction of oral fungal diseases.
Area of Science:
- Oral mycology
- Immunology
- Microbiome research
- Computational biology
Background:
- Oral fungal infections, particularly candidiasis, are opportunistic infections linked to host immune status.
- Understanding oral fungal infections requires considering host defenses and interactions within the oral microbiome.
- Advances in microbiome analysis and immune cell quantification provide new tools for studying oral health.
Purpose of the Study:
- To explore the complex interplay between oral fungi, host immunity, and the oral microbiome.
- To highlight the potential of integrating multi-omics data with machine learning for diagnosing and predicting oral fungal diseases.
- To emphasize the need for a holistic approach to understanding oral fungal infections.
Main Methods:
- Review of historical and recent literature on oral fungal infections and host-microbiome interactions.
- Discussion of advancements in oral microbiome analysis (fungi, bacteria, viruses) and immune profiling.
- Proposal for the application of machine learning and artificial intelligence (AI) for integrated data analysis.
Main Results:
- Oral fungal infections are multifactorial, involving the pathogen, host immunity, and microbial community dynamics.
- Inter-kingdom signaling within the oral microbiome influences fungal pathogenicity.
- Simultaneous analysis of microbiome composition, immune responses, and signaling molecules is crucial.
Conclusions:
- Machine learning and AI can integrate complex oral microbiome and host immune data for improved diagnosis and prediction of oral fungal diseases.
- A comprehensive understanding of the oral ecosystem is essential for managing oral fungal infections.
- Future research should focus on applying AI to clinical data for personalized risk assessment and treatment strategies.
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