Antifungal activity of secondary plant metabolites from potatoes (<i>Solanum tuberosum</i> L.): Glycoalkaloids and phenolic acids show synergistic effects
Bibliographic record
Abstract
AIMS: To study the antifungal effects of the potato secondary metabolites α-solanine, α-chaconine, solanidine and caffeic acid, alone or combined. METHODS AND RESULTS: Resistance to glycoalkaloids varied among the fungal species tested, as derived from minimum inhibitory concentrations assays. Synergistic antifungal activity between glycoalkaloids and phenolic compounds was found. Changes in the fluidity of fungal membranes caused by potato secondary plant metabolites were determined by calculation of the generalized polarization values. The results partially explained the synergistic effect between caffeic acid and α-chaconine and supported findings on membrane disruption mechanisms from previous studies on artificial membranes. LC/MS analysis was used to determine variability and relative amounts of sterols in the different fungal species. Results suggested that the sterol pattern of fungi is related to their resistance to potato glycoalkaloids and to their taxonomy. CONCLUSION: Fungal resistance to α-chaconine and possibly other glycoalkaloids is species dependent. α-Chaconine and caffeic acid show synergistic antifungal activity. The taxonomic classification and the sterol pattern play a role in fungal resistance to glycoalkaloids. SIGNIFICANCE AND IMPACT OF THE STUDY: Results improve the understanding of the antifungal mode of action of potato secondary metabolites, which is essential for their potential utilization as antifungal agents in nonfood systems.
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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".