Salicylic acids and pathogenic bacteria: new perspectives on an old compound
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it