Intracellular Enzymes Contribution to the Biocatalytic Removal of Pharmaceuticals by <i>Trametes hirsuta</i>
Bibliographic record
Abstract
The use of white rot fungi (WRF) for bioremediation of recalcitrant trace organic contaminants (TrOCs) is becoming greatly popular. Biosorption and lignin modifying enzymes (LMEs) are the most often reported mechanisms of action. Intracellular enzymes, such as cytochrome P450 (CYP450), have also been suggested to contribute. However, direct evidence of TrOCs uptake and intracellular transformation is lacking. The aim of this study was to evaluate the relative contribution of biosorption, extracellular LMEs activity, TrOCs uptake, and intracellular CYP450 on the removal of six nonsteroidal anti-inflammatories (NSAIs) by Trametes hirsuta. Results show that for most tested NSAIs, LMEs activity and biosorption failed to explain the observed removal. Most tested TrOCs are quickly taken up and intracellularly transformed. Fine characterization of intracellular transformation using ketoprofen showed that CYP450 is not the sole intracellular enzyme responsible for intracellular transformation. The contribution of CYP450 in further transformation of ketoprofen byproducts is also reported. These results illustrate that TrOCs transformation by WRF is a more complex process than previously reported. Rapid uptake of TrOCs and intracellular transformation through diverse enzymatic systems appears to be important components of WRF efficiency toward TrOCs.
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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".