Targeting antibiotic pollution: an investigation of azithromycin in wastewater by electrochemical surface-enhanced Raman spectroscopy
Bibliographic record
Abstract
Azithromycin is a macrolide antibiotic that is commonly prescribed to treat infections of the skin, soft tissues, and the upper and lower respiratory tracts. Many antibiotics, including azithromycin, are overprescribed, leading to elevated concentrations of these drugs in bodies of water; this phenomenon is referred to as antibiotic pollution. Antibiotic pollution is increasingly concerning, due to its implications for the development of antibiotic resistance and its potential ecotoxicity. Rapid detection of azithromycin in wastewater could be important for addressing antibiotic pollution. This research highlights the development of a direct electrochemical surface-enhanced Raman spectroscopy method for the detection of azithromycin in synthetic wastewater. The coupling of electrochemistry and surface-enhanced Raman spectroscopy allowed enhanced detection of azithromycin and its derivatives in wastewater. To the best of our knowledge, this is the first report of direct detection of azithromycin using electrochemical surface-enhanced Raman spectroscopy. This novel detection method has promising application in future research concerning antibiotic pollution.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".