Beyond Amoxicillin Removal: Investigating the Antimicrobial and Toxicological Effects of Its Byproducts
Bibliographic record
Abstract
Amoxicillin (AMX) is one of the most widely consumed antibiotics worldwide and has become a growing concern as an emerging pollutant. While many studies have reported the efficiency of AMX degradation treatments, few have assessed the safety of its byproducts. This study investigates the degradation of AMX using H2O2/UV treatment, evaluating its byproducts, antimicrobial potential, and toxicity over a 90-min treatment period. Solutions containing 128 mg.L-1 AMX and 3 mmol.L-1 H2O2 were irradiated under a UV-C lamp, and residual AMX concentration and metabolite formation were monitored using High-Performance Liquid Chromatography coupled with Mass Spectrometry. Antimicrobial activity was assessed using Escherichia coli, while toxicity was evaluated with Artemia salina as a model organism. Results showed that 99.0% of AMX was removed within 25 min. The detected byproducts suggested hydroxylation and cleavage of the β-lactam ring. Notably, a significant reduction in antimicrobial activity, reflected by increased minimum inhibitory concentration values, was observed only after 60 min, corresponding with more frequent cleavage of the β-lactam ring. Toxicity assays indicated that no toxic byproducts were generated. These findings underscore the importance of understanding the environmental behavior, toxicity, and developments in remediation technologies and methods to minimize ecological risks.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".