Effects of the<i>Fusarium</i>spp. mycotoxins fusaric acid and deoxynivalenol on the growth of<i>Ruminococcus albus</i>and<i>Methanobrevibacter ruminantium</i>
Bibliographic record
Abstract
The Fusarium spp. mycotoxins fusaric acid and deoxynivalenol (DON) were tested for antimicrobial activity against Ruminococcus albus and Methanobrevibacter ruminantium. The growth of both organisms was inhibited by fusaric acid as low as 15 micrograms/mL (84 microM) but not by DON, at levels as high as 100 micrograms/mL (338 microM). No synergistic inhibitory effect was observed with DON plus fusaric acid. Neither organism was able to adapt to the fusaric acid and responses of each organism to the compound were different. The optical density (OD) maximum for R. albus, but not for M. ruminantium, was diminished after 28 days incubation at concentrations of fusaric acid below 240 micrograms/mL. Inhibition of R. albus started before significant growth had occurred, while M. ruminantium doubled twice before the onset of inhibition. Responses to picolinic acid, an analog of fusaric acid, were also dramatically different between the two microorganisms with M. ruminantium exhibiting a severe lag followed by a complete recovery of growth, while R. albus was only slightly inhibited with no lag. These results suggest that the mechanism of fusaric acid inhibition is specific to each microorganism. This is the first demonstration of the common mycotoxin fusaric acid inhibiting the growth of rumen bacteria.
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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".