Horseradish peroxidase‐catalysed oxidation of aqueous natural and synthetic oestrogens
Bibliographic record
Abstract
Abstract The horseradish peroxidase (HRP)‐catalysed oxidation of selected potent oestrogens, including the natural oestrogens 17β‐oestradiol and oestriol and the synthetic oestrogen ethinyloestradiol, was studied and compared with that of phenol. HRP catalysed the oxidation of these oestrogens and phenol over a wide range of pH values, with optimal performance around neutral pH, which is in the range of typical urban wastewaters. In comparison with that of phenol, the oxidation of oestrogens consistently required less hydrogen peroxide. In addition, the rate of oxidation of oestradiol was equivalent, twofold faster and fivefold faster compared with that of ethinyloestradiol, oestriol and phenol respectively. For all substrates studied, similar kinetics of removal were observed provided that sufficient enzyme was added to reactions to compensate for differences in substrate affinity. In contrast with earlier studies with phenols in which HRP was observed to be susceptible to significant inactivation due to interactions with hydrogen peroxide and reaction products, minimal inactivation of HRP was observed during the present study, probably owing to the very low concentration of target substrates used here (i.e. 10 µmol dm−3) relative to earlier studies with other phenolic substrates. These observations suggest that this enzymatic approach has strong potential to be used to target the treatment of oestrogenic compounds. Copyright © 2007 Society of Chemical Industry
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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 | 0.000 |
| 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".