Recent developments in the use of tyrosinase and laccase in environmental applications
Bibliographic record
Abstract
Our current global environmental challenges include the reduction of harmful chemicals and their derivatives. Bioremediation has been a key strategy to control the massive presence of chemicals in the environment. Enzymes including the phenoloxidases, laccases and tyrosinases, are increasingly being investigated as "green products" in the removal of many chemical contaminants in waters and soils. Both phenoloxidases are widespread in nature and attractive biocatalysts due to their ability to use readily available molecular oxygen as sole cofactor for their catalytic elimination of a large number of chemicals. Taking advantage of their catalytic potentials, remarkable advances have been made in the engineering of laccases to produce suitable biocatalysts in environmental applications. Studies about novel strategies of laccase immobilization and insolubilization for the treatment of chemical contaminants were provided. Likewise, tyrosinases are gaining increasing interest in environmental applications due to their catalytic similarities with laccases although they remain far less investigated to date. This disparity was addressed in this review along with the molecular features and catalytic mechanism of tyrosinases relevant in environmental applications. A perspective on the future use of laccases and tyrosinases in bioremediation was discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".