Elimination of Bisphenol A and Triclosan Using the Enzymatic System of Autochthonous Colombian Forest Fungi
Bibliographic record
Abstract
Bisphenol A (BPA) and triclosan (TCS) are known or suspected potential endocrine disrupting chemicals (EDCs) which may pose a risk to human health and have an environmental impact. Enzyme preparations containing mainly laccases, obtained from Ganoderma stipitatum and Lentinus swartzii, two autochthonous Colombian forest white rot fungi (WRF), previously identified as high enzyme producers, were used to remove BPA and TCS from aqueous solutions. A Box-Behnken factorial design showed that pH, temperature, and duration of treatment were significant model terms for the elimination of BPA and TCS. Our results demonstrated that these EDCs were extensively removed from 5 mg L(-1) solutions after a contact time of 6 hours. Ninety-four percent of TCS and 97.8% of BPA were removed with the enzyme solution from G. stipitatum; 83.2% of TCS and 88.2% of BPA were removed with the L. swartzii enzyme solution. After a 6-hour treatment with enzymes from G. stipitatum and L. swartzii, up to 90% of the estrogenic activity of BPA was lost, as shown by the yeast estrogen screen assay. 2,2-Azino-bis-(3-ethylthiazoline-6-sulfonate) (ABTS) was used as a mediator (laccase/mediator system) and significantly improved the laccase catalyzed elimination of BPA and TCS. The elimination of BPA in the absence of a mediator resulted in production of oligomers of molecular weights of 454, 680, and 906 amu as determined by mass spectra analysis. The elimination of TCS in the same conditions produced dimers, trimers, and tetramers of molecular weights of 574, 859, and 1146 amu. Ecotoxicological studies using Daphnia pulex to determine lethal concentration (LC50) showed an important reduction of the toxicity of BPA and TCS solutions after enzymatic treatments. Use of laccases emerges thus as a key alternative in the development of innovative wastewater treatment technologies. Moreover, the exploitation of local biodiversity appears as a potentially promising approach for identifying new efficient strains for biotechnological applications.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".