X-Ray Absorption Near-Edge Structure (XANES) Spectroscopy Study of the Interaction of Silver Ions with Staphylococcus aureus, Listeria monocytogenes, and Escherichia coli
Bibliographic record
Abstract
Silver ions are widely used as antibacterial agents, but the basic molecular mechanism of this effect is still poorly understood. X-ray absorption near-edge structure (XANES) spectroscopy at the Ag LIII, S K, and P K edges reveals the chemical forms of silver in Staphylococcus aureus and Escherichia coli (Ag(+) treated). The Ag LIII-edge XANES spectra of the bacteria are all slightly different and very different from the spectra of silver ions (silver nitrate and silver acetate), which confirms that a reaction occurs. Death or inactivation of bacteria was observed by plate counting and light microscopy. Silver bonding to sulfhydryl groups (Ag-S) in cysteine and Ag-N or Ag-O bonding in histidine, alanine, and DL-aspartic acid was detected by using synthesized silver-amino acids. Significantly lower silver-cysteine content, coupled with higher silver-histidine content, in Gram-positive S. aureus and Listeria monocytogenes cells indicates that the peptidoglycan multilayer could be buffering the biocidal effect of silver on Gram-positive bacteria, at least in part. Bonding of silver to phosphate groups was not detected. Interaction with DNA or proteins can occur through Ag-N bonding. The formation of silver-cysteine can be confirmed for both bacterial cell types, which supports the hypothesis that enzyme-catalyzed reactions and the electron transport chain within the cell are disrupted.
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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 | 0.000 | 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".