Evaluation of residual antibacterial potency in antibiotic production wastewater using a real-time quantitative method
Bibliographic record
Abstract
While antibiotic pollution has attracted considerable attention due to its potential in promoting the dissemination of antibiotic resistance genes in the environment, the antibiotic activity of their related substances has been neglected, which may underestimate the environmental impacts of antibiotic wastewater discharge. In this study, a real-time quantitative approach was established to evaluate the residual antibacterial potency of antibiotics and related substances in antibiotic production wastewater (APW) by comparing the growth of a standard bacterial strain (Staphylococcus aureus) in tested water samples with a standard reference substance (e.g. oxytetracycline). Antibiotic equivalent quantity (EQ) was used to express antibacterial potency, which made it possible to assess the contribution of each compound to the antibiotic activity in APW. The real-time quantitative method showed better repeatability (Relative Standard Deviation, RSD 1.08%) compared with the conventional fixed growth time method (RSD 5.62-11.29%). And its quantification limits ranged from 0.20 to 24.00 μg L(-1), depending on the antibiotic. We applied the developed method to analyze the residual potency of water samples from four APW treatment systems, and confirmed a significant contribution from antibiotic transformation products to potent antibacterial activity. Specifically, neospiramycin, a major transformation product of spiramycin, was found to contribute 13.15-22.89% of residual potency in spiramycin production wastewater. In addition, some unknown related substances with antimicrobial activity were indicated in the effluent. This developed approach will be effective for the management of antibacterial potency discharge from antibiotic wastewater and other waste streams.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".