Adhesion and biofilm formation by the opportunistic pathogen <i>Candida tropicalis</i>: what do we know?
Bibliographic record
Abstract
Candida tropicalis is among the most important Candida species in terms of epidemiology, virulence and resistance. Considering the increase in C. tropicalis incidence and high rates of mortality associated with this species, knowledge of its adhesion and biofilm formation abilities is needed. These traits determine the persistence and survival of yeast on different indwelling medical devices and host sites. C. tropicalis is among the most adherent Candida species, and it has been described as a strong biofilm producer. Environmental factors, phenotypic switching and quorum sensing molecules can affect adhesion and biofilm growth. C. tropicalis can form sexual biofilms, which are promoted by mating pheromones. C. tropicalis biofilms are regulated by a wide and complex network of genes and signaling pathways that are currently poorly understood. Morphological studies showed improved biofilm architecture, which was related to the expression of several hypha-specific genes. Based on recent updates, research is still needed to increase our knowledge on the genetic network of adhesion and biofilm formation by C. tropicalis, as well as the protein diversity that mediates interactions with inert materials and biological surfaces. Here, we have reviewed the main aspects related to adhesion and biofilm formation in C. tropicalis and summarized current knowledge on the significance of these virulence factors in this opportunistic species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".