Salicylic acids and pathogenic bacteria: new perspectives on an old compound
Bibliographic record
Abstract
Salicylic acids have been used in human and veterinary medicine for their anti-pyretic, anti-inflammatory, and analgesic properties for centuries. A key role of salicylic acid—immune modulation in response to microbial infection—was first recognized during studies of their botanical origin. The effects of salicylic acid on bacterial physiology are diverse. In many cases, they impose selective pressures leading to development of cross-resistance to antimicrobial compounds. Initial characterization of these interactions was in Escherichia coli, where salicylic acid activates the multiple antibiotic resistance ( mar) operon, resulting in decreased antibiotic susceptibility. Studies suggest that stimulation of the mar phenotype presents similarly in closely related Enterobacteriaceae. Salicylic acids also affect virulence in many opportunistic pathogens by decreasing their ability to form biofilms and increasing persister cell populations. It is imperative to understand the effects of salicylic acid on bacteria of various origins to illuminate potential links between environmental microbes and their clinically relevant antimicrobial-resistant counterparts. This review provides an update on known effects of salicylic acid and key derivatives on a variety of bacterial pathogens, offers insights to possible potentiation of current treatment options, and highlights cellular regulatory networks that have been established during the study of this important class of medicines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".