Fluoroquinolone resistance of<i>Serratia marcescens</i>: sucrose, salicylate, temperature, and pH induction of phenotypic resistance
Bibliographic record
Abstract
Serratia marcescens is a nosocomial agent with a natural resistance to a broad spectrum of antibiotics, making the treatment of its infections very challenging. This study examines the influence of salicylate, sucrose, temperature, and pH variability on membrane permeability and susceptibility of S. marcescens to norfloxacin (hydrophilic fluoroquinolone) and nalidixic acid (hydrophobic quinolone). Resistance of wild-type S. marcescens UOC-67 (ATCC 13880) to norfloxacin and nalidixic acid was assessed by minimal inhibitory concentration (MIC) assays after growth in the presence of various concentrations of sucrose and salicylate and different temperatures and pH values. Norfloxacin and nalidixic acid accumulation was determined in the absence and presence of (i) carbonyl cyanide m-chlorophenylhydrazone (CCCP), a proton-motive-force collapser, and (ii) Phe-Arg beta-naphthylamide (PAbetaN), an efflux pump inhibitor. Accumulation of norfloxacin decreased when S. marcescens was grown in high concentrations of salicylate (8 mmol/L) and sucrose (10% m/v), at high temperature (42 degrees C), and at pH 6, and it was restored in the presence of CCCP because of the collapse of proton-gradient-dependent efflux in S. marcescens. Although nalidixic acid accumulation was observed, it was not affected by salicylate, sucrose, pH, or temperature changes. In the absence of PAbetaN, and either in the presence or absence of CCCP, a plateau was reached in the nalidixic acid accumulation for all environmental conditions. With the addition of 20 mg/L PAbetaN nalidixic acid accumulation is restored for all environmental conditions, suggesting that this quinolone is recognized by a yet to be identified S. marcescens pump that does not use proton motive force as its energy source.
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.
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".