Antifungal Activity and Mechanistic Insights of 1-<i>O</i>-Alkylglycerols against <i>Monilinia fructigena</i>
Bibliographic record
Abstract
Peach brown rot, caused by Monilinia fructigena ( M. fructigena ), is a destructive disease that affects peaches during pre- and postharvest stages. In this study, two 1- O -alkylglycerols (AKGs), namely, AKG-2 (1- O -dodecylglycerol) and AKG-4 (3-(2-ethylhexyloxy)propane-1,2-diol), demonstrated significant in vitro antifungal activity against M. fructigena, with EC 50 values of 102 and 103 μg/mL, respectively. At a concentration of 200 μg/mL, both compounds effectively suppressed brown rot symptoms in inoculated peaches, achieving a protective efficacy of 93.2%. Mechanistic investigations revealed that AKG-2 and AKG-4 disrupted the surface morphology and internal ultrastructure of fungal hyphae, compromised the integrity of the cell membrane and nucleus, and reduced mitochondrial membrane potential. Moreover, treatment with these compounds induced the accumulation of reactive oxygen species, leading to elevated malondialdehyde levels and decreased activities of key antioxidant enzymes including superoxide dismutase and catalase. These results suggest that AKG-2 and AKG-4 exhibit antifungal effects through multiple synergistic mechanisms including membrane and nuclear damage, mitochondrial dysfunction, and oxidative stress induction. This study, for the first time, provides valuable mechanistic insights into the antifungal effects of AKGs and supports their potential as naturally derived fungicidal agents for the protection of peach fruits.
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) | 0.000 | 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".