Effect of trace iron levels and iron withdrawal by chelation on the growth of Candida albicans and Candida vini
Bibliographic record
Abstract
The iron requirements of the opportunistic pathogenic yeast, Candida albicans, and the related nonpathogenic spoilage yeast Candida vini were investigated along with their responses to various exogenous iron chelators. The influence of iron as well as the exogenous chelating agents lactoferrin, EDTA, deferiprone, desferrioxamine, bathophenanthroline sulphonate and a novel carried chelator with a hydroxypyridinone-like Fe-ligand functionality, DIBI, on fungal growth was studied in a chemically defined medium deferrated to trace iron levels (<1.2 microg L(-1) or 0.02 microM of Fe). Candida albicans competed better at low iron levels compared with C. vini, which was also more susceptible to most added chelators. Candida albicans was resistant to lactoferrin at physiologically relevant concentrations, but was inhibited by low concentrations of DIBI. Candida vini was sensitive to lactoferrin as well as to DIBI, whose inhibitory activity was shown to be Fe reversible. The pathogenic potential of C. albicans and the nonpathogenic nature of C. vini were consistent with their differing abilities to grow under iron-limiting conditions and in the presence of exogenous iron chelators. Both yeasts could be controlled by appropriately strong chelators. This work provides the first evidence of the iron requirements of the spoilage organism C. vini and its response to exogenous chelators. Efficient iron withdrawal has the potential to provide the basis for new fungal growth control strategies.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".