Bjerkandera adusta TM11 for the bioremediation of fluoroquinolone antibiotics spiked in wastewater: A sustainable approach to pharmaceutical contaminant biotransformation
Bibliographic record
Abstract
Global antibiotic consumption is increasing dramatically. Antibiotic release into the environment, primarily through wastewater discharge, has serious impacts for human and animal health and microbial ecosystems. To address this issue, white-rot fungi present a promising solution, as they possess oxidative enzymes that can degrade these pollutants. Here we investigated the effectiveness of the white-rot fungus Bjerkandera adusta TM11 for removing three persistent fluoroquinolone antibiotics, i.e. levofloxacin, ciprofloxacin, and enrofloxacin, in real wastewater. The three antibiotics were added to the wastewater separately at a concentration of 30 mg/L and together in a cocktail at 10 mg/L, then incubated for 9 days. LC-MS/MS analysis and anti-microbial assay (against Escherichia coli ) demonstrated complete removal of levofloxacin by day 7. However, ciprofloxacin and enrofloxacin biotransformed into degradation products that still had antimicrobial activity, with degradation efficiencies reaching 82 % and 99 %, respectively, by day 7. Proteomic analysis identified 21 fungal heme peroxidases. Versatile peroxidase was the most strongly-produced enzyme potentially involved in antibiotic biotransformation. Degradation products were characterized by LC-MS/MS analysis, and a degradation pathway was proposed based on these findings. • Bjerkandera adusta was able to completely biotransform levofloxacin in real wastewater within 7 days. • Ciprofloxacin and enrofloxacin were transformed into products with residual antimicrobial activity. • Proteomic analysis revealed versatile peroxidase as a putative key enzyme involved in the degradation process. • The degradation products of fluoroquinolone antibiotics were identified using LC-MS/MS analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".