Effect of Matricaria aurea Essential Oils on Biofilm Development, Virulence Factors and Quorum Sensing-Dependent Genes of Pseudomonas aeruginosa
Bibliographic record
Abstract
The emergence of drug-resistant microorganisms presents a substantial global public health threat. The increase in pathogens resistant to commonly prescribed antibiotics underscores the urgent requirement to explore alternative treatment strategies. This study adopts a novel approach by harnessing natural resources, specifically essential oils (EO), to combat bacterial pathogenicity. The primary aim of this research was to analyze the chemical composition of the aerial part of the Matricaria aurea (M. aureas) EO and evaluate its potential for inhibiting quorum sensing (QS) and disrupting biofilm formation in Pseudomonas aeruginosa (P. aeruginosa). The gas chromatography-mass spectrometry (GCMS) analysis unveiled that α-bisabolol oxide A constituted the predominant portion, comprising 64.8% of the total, with β-bisabolene at 6.3% and α-farnesene at 4.8% following closely behind. The antibiofilm efficacy was observed at concentrations of 0.3, 0.15, and 0.08 mg/mL, demonstrating negligible effects on cell viability. Furthermore, the EO from M. aurea effectively inhibited the formation of P. aeruginosa biofilms by diminishing aggregation, hydrophobicity, and swarming motility. Significantly, the EO treatment resulted in a conspicuous decrease in the production of pyocyanin, rhamnolipid, and extracellular polymeric substances (EPS), along with a reduction in the enzymatic activity of protease and chitinase. The EO effectively hindered QS by disrupting QS mechanisms, resulting in a marked decline in the secretion of N-Acyl homoserine lactone (AHL) molecules and the expression of phazA1 and aprA genes. This investigation offers compelling evidence supporting the potential of M. aurea EO as a promising therapeutic candidate for addressing infectious diseases induced by biofilm formation.
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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".