Bactericidal and Anti-Biofilm Activity of Ethanol Extracts Derived from Selected Medicinal Plants against Streptococcus pyogenes
Bibliographic record
Abstract
Background: There is a growing interest in medicinal plants which have been traditionally used for the treatment of human infections. This study assessed 14 ethanol extracts (EEs) on bacterial growth and biofilm formation of Streptococcus pyogenes. Methods: Constituent major phytochemicals in the extracts were identified using ultra performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS). Micro-broth dilution and time-kill assays were used to determine antibacterial activities. Anti-biofilm activities were studied using MTT assay, and morphology of biofilms was observed by scanning electron microscopy (SEM). Transmission electron microscopy (TEM) was employed to visualize the ultra-cross section structure of bacteria treated with efficacious extracts. Results: Licorice root, purple coneflower flower, purple coneflower stem, sage leaves and slippery elm inner bark EEs were the most effective, with minimum inhibitory concentrations (MIC) and minimum bactericidal concentrations (MBC) of 62.5 μg/mL and 125 μg/mL, respectively. The minimum biofilm inhibitory concentration (MBIC) of extracts ranged from 31.5–250 μg/mL. Morphological changes were observed in treated biofilms compared to the untreated. The four most effective extracts exhibited the ability to induce degradation of bacterial cell wall and disintegration of the plasma membrane. Conclusion: We suggest that EEs of sage leaf and purple coneflower flower are promising candidates to be further investigated for developing alternative natural therapies for the management of streptococcal pharyngitis.
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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 | 0.001 | 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".