Linking Cellular Morphogenesis with Antifungal Treatment and Susceptibility in Candida Pathogens
Bibliographic record
Abstract
Fungal infections are a growing public health concern, and an increasingly important cause of human mortality, with Candida species being amongst the most frequently encountered of these opportunistic fungal pathogens. Several Candida species are polymorphic, and able to transition between distinct morphological states, including yeast, hyphal, and pseudohyphal forms. While not all Candida pathogens are polymorphic, the ability to undergo morphogenesis is linked with the virulence of many of these pathogens. There are also many connections between Candida morphogenesis and antifungal drug treatment and susceptibility. Here, we review how Candida morphogenesis—a key virulence trait—is linked with antifungal drugs and antifungal drug resistance. We highlight how antifungal therapeutics are able to modulate morphogenesis in both sensitive and drug-resistant Candida strains, the shared signaling pathways that mediate both morphogenesis and the cellular response to antifungal drugs and drug resistance, and the connection between Candida morphology, drug resistance, and biofilm growth. We further review the development of anti-virulence drugs, and targeting Candida morphogenesis as a novel therapeutic strategy to target fungal pathogens. Together, this review highlights important connections between fungal morphogenesis, virulence, and susceptibility to antifungals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".