Addressing the mycotoxin deoxynivalenol contamination with soil-derived bacterial and enzymatic transformations targeting the C3 carbon
Bibliographic record
Abstract
The search for feasible biological means of detoxifying mycotoxins has attained successful accomplishments in the past twenty years due to the involvement of many teams coming from diverse backgrounds and research expertise. The recently witnessed breakthroughs in the field of bacterial genomics (including next-generation sequencing), proteomics, and computational biology helped all in shaping the current understanding of how microorganisms/mycotoxins/environmental factors intertwined and interact together, hence paving the road for some substantial discoveries. This perspective review summarises the advances that were observed in the past two decades within the deoxynivalenol (DON) bio-detoxification field. It highlights the research efforts and progresses that were made in the arena of the aerobic oxidation and epimerization of this mycotoxin at the C3 carbon carried out by multiple Devosia species. Moreover, it sets practical examples and discusses how the recent standing-knowledge of bacterial detoxifications of this mycotoxin has evolved into a fascinating potential of empirical bacterial and enzymatic solutions aiming at addressing DON contamination. The obtained results argue for determining the involved enzyme’s co-factors and defining the chemistry behind the established catalytic activity at an early stage of investigation to maximise the chances of isolating the responsible enzymes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".