Triclosan\nis the Predominant Antibacterial Compound\nin Ontario Sewage Sludge
Bibliographic record
Abstract
Sewage treatment plants (STPs) accumulate\nboth antibiotic and nonantibiotic\nantimicrobial compounds that can select for antibiotic resistant bacteria.\nHerein, we aimed to identify the predominant antibacterial compounds\nimpacting E. coli from Ontario sewage\nsludge consisting of thousands of unknown compounds. Among the 10\nextracted sludge samples, 6 extracts exerted significant growth inhibition\neffects in E. coli. A total of 103\ncompounds were tentatively detected across the 10 sludge samples by\nsuspect screening, among which the bacterial enoyl-ACP reductase (FabI)\ninhibitor triclocarban was detected at the highest abundance. A hypomorphic\nFabI knockdown E. coli strain was highly\nsusceptible to the sludge extracts, confirming FabI inhibitors as\nthe primary antibacterial compounds in the sludge. Protein affinity\npulldown identified triclosan as the major ligand binding to a His-tagged\nFabI protein from the sludge, despite the higher abundance of triclocarban\nin the same samples. Effect-directed analysis was used to determine\nthe contributions of triclosan to the observed antibacterial potencies.\nAntibacterial effects were only detected in F<sub>17</sub> and F<sub>18</sub> across 20 fractions, which was consistent with the elution\nof triclosan and triclocarban in the same two fractions. Further,\npotency mass balance analysis confirmed that triclosan explained the\nmajority (58–113%) of inhibition effects from sludge extracts.\nThis study highlighted triclosan as the predominant antibacterial\ncompound in sewage sludge impacting E. coli despite the co-occurrence of numerous other antibiotics and nonantibiotics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.787 | 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; both teacher heads agree on what is shown here.
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".