Synergistic activity of rhamnolipid combined with linezolid against linezolid-resistant <i>Enterococcus faecium</i>
Bibliographic record
Abstract
Abstract Enterococci resistance is increasing sharply, which poses a serious threat to public health. Rhamnolipids are a kind of amphiphilic compound used for its bioactivities, while the combination of nontraditional drugs to restore linezolid activity is an attractive strategy to treat infections caused by these pathogens. This study aimed to investigate the activity of linezolid in combination with the rhamnolipids against Enterococcus faecium . Here, we determined that the rhamnolipids could enhance the efficacy of linezolid against enterococci infections by a checkerboard MIC assay, a time-kill assay, a combined disk test, anti-biofilm assay, molecular simulation dynamics, and mouse infection models. We identified that the combination of rhamnolipids and linezolid restored the linezolid sensitivity. Anti-biofilm experiments show that our new scheme can effectively inhibit biofilm generation. The mouse infection model demonstrated that the combination therapy significantly reduced the bacterial load in the feces, colons and kidneys following subcutaneous administration. This study showed that rhamnolipids could play a synergistic role with linezolid against Enterococcus. Our combined agents could be appealing candidates for developing new combinatorial agents to restore antibiotic efficacy in the treatment of linezolid-resistant Enterococcus infections. Highlight The identification of a potential inhibitor of LRE, RLS could restore the antibacterial activity of LNZ. A rational mechanism of synergism between LNZ and RLS. ;;L;;RLS in combination with LNZ promote the egress of pathogenic E. faecium from the intestinal tract of mice to reduce its colonization, which has obvious synergistic effects.
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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".