The herbicide paraquat alters growth and melanin production on the <i>Cryptococcus neoformans/Cryptococcus gattii</i> species complex
Bibliographic record
Abstract
Paraquat (1,10-dimethyl-4,4-bipyridinium dichloride; PQ) is a free-radical producing herbicide that affects cell membranes and can upset the environmental balance of microorganisms present in soil, such as Cryptococcus spp. This study aimed to evaluate the in vitro activity of PQ against Cryptococcus spp. in planktonic and biofilm forms, as well as the protective effect of antioxidant agents against the antifungal effect of PQ and the kinetics of melanin production in response to PQ. Susceptibility to PQ was evaluated by microdilution. Cryptococcus sp. strains exposed to PQ were grown in media with ascorbic acid (AA) and glutathione (GSH). Melanin production was assessed in the presence of l-3,4-dihydroxyphenylalanine (l-DOPA) + PQ. The minimum inhibitory concentration of PQ against Cryptococcus spp. ranged from 8 to 256 µg/mL. Furthermore, PQ reduced biofilm formation. AA and GSH restored the fungal growth of Cryptococcus spp. exposed to PQ. In addition, l-DOPA + PQ delayed melanin production by 24 and 48 h for C. deuterogattii and C. neoformans sensu lato, respectively, suggesting that PQ induces a fitness trade-off in melanin production. Taken together, our data suggest that the antifungal effect of PQ against Cryptococcus spp. possibly exerts selective pressures interfering with biofilm formation and melanin production by these yeasts.
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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 | 0.000 |
| 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".