Anti-Biofilm Activity of Marrubium vulgare L. (Lamiaceae) Extract on MRSA
Bibliographic record
Abstract
Many plants possess potent antimicrobial agents and provide effective remedies for skin conditions. Infusions of the aerial parts of Marrubium vulgare (white horehound) are used in the south Italian pharmacopoeia as a rinse for skin rashes and wounds [1]. Staphylococcus aureus , a common cause of skin infections, has generated increasing concern among health care professionals due to the prevalence of drug resistant strains. Identification of novel antibiotics and anti-biofilm agents for methicillin-resistant S. aureus (MRSA) is important to healthcare on a global scale. The aim of this study was to evaluate extracts from Marrubium vulgare for in vitro inhibition of planktonic growth, biofilm formation and adherence in MRSA. A broth microtiter dilution method was employed to determine the MIC after 18 hours growth using an optical density (OD 600 nm ) reading using a MRSA isolate (ATCC 33593). The impact of extracts on biofilm formation and adherence was tested by growing biofilms for 40 hours, then fixing and staining with crystal violet. After washing, 10% Tween 80 was added and OD 570 nm readings were taken. A crude ethanolic extract of the roots was the most effective at inhibiting both biofilm formation (IC 50 = 32 µg/ml) and adherence (IC 50 = 8 µg/ml). A significant dose-dependent response for the inhibition of both biofilm formation and adherence was evident. Acknowledgements: This work was funded by NIH/NCCAM F32AT005040 (PI: C.L. Quave). References: [1] Quave, C.L. et al. (2008) J. Ethnobiol. Ethnomed. Vol. 4: 5.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".