Inhibitory activity of isoniazid and ethionamide against <i>Cryptococcus</i> biofilms
Bibliographic record
Abstract
In recent years, the search for drugs to treat systemic and opportunistic mycoses has attracted great interest from the scientific community. This study evaluated the in vitro inhibitory effect of the antituberculosis drugs isoniazid and ethionamide alone and combined with itraconazole and fluconazole against biofilms of Cryptococcus neoformans and Cryptococcus gattii. Antimicrobials were tested at defined concentrations after susceptibility assays with Cryptococcus planktonic cells. In addition, we investigated the synergistic interaction of antituberculosis drugs and azole derivatives against Cryptococcus planktonic cells, as well as the influence of isoniazid and ethionamide on ergosterol content and cell membrane permeability. Isoniazid and ethionamide inhibited both biofilm formation and viability of mature biofilms. Combinations formed by antituberculosis drugs and azoles proved synergic against both planktonic and sessile cells, showing an ability to reduce Cryptococcus biofilms by approximately 50%. Furthermore, isoniazid and ethionamide reduced the content of ergosterol in Cryptococcus spp. planktonic cells and destabilized or permeabilized the fungal cell membrane, leading to leakage of macromolecules. Owing to the paucity of drugs able to inhibit Cryptococcus biofilms, we believe that the results presented here might be of interest in the designing of new antifungal compounds.
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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".