Mechanisms Underlying the Antimicrobial Capacity of Metals
Bibliographic record
Abstract
The antimicrobial activity of metals – how metals poison the microbial cell – is based on the metal ions' particular chemistries. As such, metals target specified biological functional groups within the bacterial cell. Moreover, metal toxicity can lead to the expression of select genetic determinants within the microbial cell that alter cellular physiology. For centuries, we've known that certain metals, such as silver (Ag) and copper (Cu), have antimicrobial capacities. A modern environmental concern is how anthropogenic metal contaminants might toxify indigenous microbial communities (microbiomes) or affect bioremediation efforts. Furthermore, the emergence of antibiotic-resistant pathogens, combined with a slow pipeline of novel antibiotic therapies, has reinvigorated research into exploring the antimicrobial potential of metal and metal nanoparticles. Currently, the potential for Ag, Cu, and gallium (Ga) to complement the current armamentarium of antimicrobials is being rigorously explored in the lab and clinic. Still, many other metals have antimicrobial activities that require exploration. Here, we will describe what has been established regarding the antimicrobial activity of metals, as well as the current gaps in our knowledge. To that end, this chapter will aim to provide a guide for future research surrounding the antimicrobial capacity of metals.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".