Bacterial‐fungal interactions and their impact on microbial pathogenesis
Bibliographic record
Abstract
Microbial communities of the human microbiota exhibit diverse effects on human health and disease. Microbial homeostasis is important for normal physiological functions and changes to the microbiota are associated with many human diseases including diabetes, cancer, and colitis. In addition, there are many microorganisms that are either commensal or acquired from environmental reservoirs that can cause diverse pathologies. Importantly, the balance between health and disease is intricately connected to how members of the microbiota interact and affect one another's growth and pathogenicity. However, the mechanisms that govern these interactions are only beginning to be understood. In this review, we outline bacterial-fungal interactions in the human body, including examining the mechanisms by which bacteria govern fungal growth and virulence, as well as how fungi regulate bacterial pathogenesis. We summarize advances in the understanding of chemical, physical, and protein-based interactions, and their role in exacerbating or impeding human disease. We focus on the three fungal species responsible for the majority of systemic fungal infections in humans: Candida albicans, Cryptococcus neoformans, and Aspergillus fumigatus. We conclude by summarizing recent studies that have mined microbes for novel antimicrobials and antivirulence factors, highlighting the potential of the human microbiota as a rich resource for small molecule discovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".