Where the infection is isolated rather than the specific species correlates with adherence strength, whereas biofilm density remains static in clinically isolated <i>Candida</i> and arthroconidial yeasts
Bibliographic record
Abstract
To colonize and infect the host, arthroconidial yeasts must avoid being killed by the host’s defenses. The formation of biofilms on implanted devices allows fungi to avoid host responses and to disseminate into the host. To better study the mechanisms of infection by arthroconidial yeasts, adherence and biofilm formation were assayed using patient samples collected over 10 years. In clinical samples, adherence varies within species, but the relative adherence is constant for those samples isolated from the same infection site. Herein we document, for the first time, in-vitro biofilm formation by Trichosporon dohaense, T. ovoides, T. japonicum, T. coremiiforme, Cutaneotrichosporon mucoides, Cutaneotrichosporon cutaneum, Galactomyces candidus, and Magnusiomyces capitatus on clinically relevant catheter material. Analysis of biofilm biomass assays indicated that biofilm mass changes less than 2-fold, regardless of the species. Our results support the hypothesis that most pathogenic fungi can form biofilms, and that biofilm formation is a source of systemic infections.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".