Cross-Coupling of Sulfonamide Antimicrobial Agents with Model Humic Constituents
Bibliographic record
Abstract
The oxidative cross-coupling of sulfonamide antimicrobials to constituents of natural organic matter was investigated. Sulfonamide antimicrobials were incubated with surrogate humic constituents in the absence and presence of phenoloxidases (viz., peroxidase, laccase, and tyrosinase) or acid birnessite. Substituted phenols were chosen as simple model constituents to determine the structures in humic substances important for cross-coupling reactions. The extent of sulfonamide transformation was evaluated by the disappearance of the parent compound from solution. Incubation with phenoloxidases in the absence of substituted phenols resulted in little or no sulfonamide transformation. In contrast to this, direct oxidation of sulfonamides by acid birnessite was significant. Inclusion of o-diphenols and 2,6-dimethoxyphenols in reaction mixtures resulted in significant phenoloxidase-mediated transformation of sulfonamides and enhanced antimicrobial transformation in the presence of acid birnessite. Phenolic compounds with other substitution patterns were less effective in promoting sulfonamide transformation. Nuclear magnetic resonance spectroscopy experiments provided direct evidence of peroxidase-mediated covalent cross-coupling of sulfamethazine with syringic and protocatechuic acids. Our results indicate that sulfonamide antimicrobials may be chemically incorporated into humic substances. This may result in their diminished mobility, bioavailability, and biological activity.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".