Occurrence of Imipenem in natural water: Effect of dissolved organic matter and metals
Bibliographic record
Abstract
The occurrence of trace antibiotic residues in the environment poses a threat by promoting antibiotic resistance and spreading resistant genes. Recent studies show that these residues interact with metals, forming toxic and persistent antibiotic-metal complexes (AMCs). Investigating the photodegradation of these contaminants in environmental waters is essential to understand their fate and ecotoxicological risk assessment in environmental waters. In this sense, the present work delineates the fate of IMP, a carbapenem antibiotic, in the environmental matrix and studies its interactions with humic acid and metals. The study established that the drug was labile and underwent degradation under light and ambient temperatures. Further, analytical studies with dissolved organic matter (DOM), such as humic acids, established an accelerating effect on antibiotic degradation via indirect photochemical pathways. For instance, for a concentration of 100 mg/L IMP mixed with 20 mg/L HA in volumetric ratios of IMP: HA 1:2, 1:1, and 2:1, the final concentrations of IMP after 24 h were 26.11 mg/L (-73.89 %), 34.44 mg/L (-65.56 %), and 44.22 mg/L (55.78 %), respectively. The higher the humic acid, the faster the degradation of IMP, thereby supporting the photochemical generation of reactive oxygen species (OH•) and subsequent oxidative degeneration of the drug. The interactions with metals, specifically copper, accelerated the degradation kinetics of the drug. The promotion effect was owed to the action of the OH• as the oxidizing agent. Based on the degradation products identified by LC-MS/MS, a scheme of the synergistic action of copper-redox coupling and imipenem, resulting in the oxidative degradation of the drug, was proposed. Understanding the photochemical behavior of antibiotics, and their behavior in the presence of DOM and metal is vital for unravelling their fate and complexity in wastewater.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".