Addressing the mycotoxin deoxynivalenol contamination with soil-derived bacterial and enzymatic transformations targeting the C3 carbon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it