Clinical Microbiology Dissertation Topics

Info: 949 words (1 pages) Clinical Microbiology Dissertation Topics
Published: 21th May 2025 in Clinical Microbiology Dissertation Topics

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1. Project title:

“Predictive Surveillance and Control of Emerging Fungal Pathogens in a Changing Climate: A One Health Approach”

Description & Research gap:

Emerging fungal diseases represent an increasing global health concern as the result of many global changes including urbanization, agricultural intensification, climate change, and others. Surveillance systems currently available are generally reactive and disease-pathogen systems continue to be conceptualized in traditional ways, preventing the full consideration of transmission dynamics across human, animal and environmental systems.

Objectives:

• Identify the ecological and climatic drivers of emerging fungal disease.

• Develop predictive models based on One Health principles, that include data from people, animals, and the environment, and integrate health events and climate events.

• Propose intervention strategies and One Health based bio surveillance approaches that can be used to reduce the impacts of fungal disease.

Methods:

• Use spatiotemporal GIS, ecological niche modeling, and epidemiological data to assess distribution and disease dynamics.

• Use novel machine learning models to predict areas at greatest risk for fungal outbreaks.

• Conduct field-based studies in agricultural, urban, and wildlife contexts to validate models.

Expected Results:

• A predictive instrument for the early detection of emerging fungal disease.

• One Health based recommendations for timely bio surveillance of fungal disease.

• Recommendations on how to reduce the likelihood of zoonotic fungal transmission events from livestock and pets.

Key References:

• Hyde, K.D., Baldrian, P., Chen, Y. et al. Current trends, limitations and future research in the fungi?. Fungal Diversity 125, 1–71 (2024). https://doi.org/10.1007/s13225-023-00532-5

• de Hoog et al. (2020), Piret and Boivin (2021), Kimutai et al. (2022), Větrovský et al. (2019)

2. Project title:

“Combating Antifungal Resistance Through AI-Powered Drug Repurposing and Novel Agent Discovery”

Description and Research Gap:

With increasing antifungal resistance to most current antifungal agents and the continued lack of new drug classes, it is urgent that we discover and develop new ways to identify new antifungal agents. While the data and other structural information about known compounds are plentiful, the promise of using AI to find compounds may be under-explored. We could therefore tap into these resources to identify existing drugs that can be repurposed and predict the efficacy of new compounds.

Objectives:

• Use AI models to identify and repurpose existing compounds for use against resistant fungi.

• Identify and rank new candidate antifungal metabolites from fungi for antifungal activity.

• Assess AI-identified compounds using laboratory (in vitro) and live model (in vivo) experiments.

Methods:

• Deep learning programs to screen large compound library of existing drugs and to process the existing fungal metabolome datasets.

• Target identification using docking simulations and prediction of ADMET based on bioinformatic data.

• Experimental validation via performing antifungal susceptibility testing and resistance.

Expected Outcomes:

• Identification of drug candidates with treatment efficacy against major multi-drug-resistant fungi.

• Developed/Validated AI screening pipeline for antifungal drug discovery or development.

• Short list of natural products for potential clinical development.

Key References:

• Hyde, K.D., Baldrian, P., Chen, Y. et al. Current trends, limitations and future research in the fungi? Fungal Diversity 125, 1–71 (2024). https://doi.org/10.1007/s13225-023-00532-5

• Saldivar-Gonzalez et al. (2022), Berman et al. (2020), Fisher et al. (2022), Lin et al. (2023)

3. Project Title:

“Synthetic Biology for the Production of Fungal Natural Products by Uncovering the ‘Silent Majority’ of BGCs”

Overview and Research Gap:

Many biosynthetic gene clusters (BGCs) in fungi are silent or unexpressed under laboratory conditions. Synthetic biology provides a pathway for activating these silent genes and producing new compounds.

Goals:

• Identify and rank silent BGCs located in little-studied fungal species.

• Develop synthetic biology platforms for the heterologous expression of BGCs.

• Isolate, evaluate, and discover bioactive novel secondary metabolites.

Approach:

• Genomic mining of rare fungi (i.e., Hypoxylaceae).

• Transfer the biosynthetic gene clusters into model organisms, for instance Aspergillus nidulans, to facilitate expression and product analysis.

• Characterize the metabolites with LC- MS/NMR and bioactive assay.

Desired Outcomes:

• Novel compounds with antimicrobial, antifungal and anticancer activity.

• A platform for the heterologous activation and expression of BGCs.

• Information on regulation and function of the silent BGCs present in fungi.

Key IEEE References:

• Hyde, K.D., Baldrian, P., Chen, Y. et al. Current trends, limitations and future research in the fungi?. Fungal Diversity 125, 1–71 (2024). https://doi.org/10.1007/s13225-023-00532-5

• Robey et al. (2021), Kuhnert et al. (2021), Alberti et al. (2017), Fricke et al. (2017)

4. Project Title:

“Diagnostics of Emerging Mycoses: From AI-Assisted Microscopy to Lab-on-a-Chip”

Description & Research Gap:

Diagnostic capabilities of fungal infections are slow, outdated, and poorly adapted for field or low-resource environments. AI and microfluidics have alternative resources and methods to provide scalable and reliable diagnostics.

Objectives:

• Develop rapid, point-of-care diagnostic tools for high-priority fungal pathogens.

• Utilize AI to automate fungal identification with imaging data.

• Validate microfluidic systems for simultaneous detection of fungal biomarkers.

Method:

• Train models on fungal imagery for image recognition of fungal morphology (e.g. hyphae).

• Design and prototype lab-on-a-chip devices with immunoassay / molecular modules.

• Evaluate performance with simulated clinical and environmental samples.

Expected Outcomes:

• Accessible and affordable, accurate fungal diagnostic platform appropriate for outbreak conditions.

• AI-based fungal identification demonstrated with high sensitivity / specificity.

• Improved diagnostics for resistant fungi such as Candida auris.

Key IEEE References:

• Hyde, K.D., Baldrian, P., Chen, Y. et al. Current trends, limitations and future research in the fungi?. Fungal Diversity 125, 1–71 (2024). https://doi.org/10.1007/s13225-023-00532-5

• Koo et al. (2021), Richter et al. (2022), Osaigbovo & Bongomin (2021), Song et al. (2023)

5. Project Title:

“Multi-Omics Integration for Understanding Fungal Pathogenicity and Host-Pathogen Interaction”.

Description & Research Gap:

While genomics and metabolomics have come along way, the field of multi-omics data integration to understand fungal pathogenesis is still young. Understanding these relationships is important for precision antifungal strategies.

Objectives:

• To combine genomic, transcriptomic, proteomic, and metabolomic data to analyze fungal pathogens comprehensively.

• To characterize the relevant pathways and virulence factors across host-fungal interactions.

• To model the systems responses of fungi to antifungal therapies.

Method:

• Generate multi-omics data sets from fungal pathogens using controlled infection models.

• Employ network analysis and machine learning techniques to infer host-pathogen interaction networks.

• Confirm findings through gene knockouts and drug perturbation studies.

Expected Outcomes:

• A systems approach to modelling mechanisms of fungal infections.

• Newly identified targets for antifungal therapies.

• New insights into the mechanisms of resistance and immune evasion by the host.

Key IEEE References:

• Hautbergue et al. (2018), Rinschen et al. (2019), Song et al. (2020), Oliveira et al. (2021)

• Hyde, K.D., Baldrian, P., Chen, Y. et al. Current trends, limitations and future research in the fungi? Fungal Diversity 125, 1–71 (2024). https://doi.org/10.1007/s13225-023-00532-5

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Clinical Microbiology Topics