The Aqueous Extract of Quercus robur L. (Fagaceae) Shows Promising Antibacterial Activity against Klebsiella pneumoniae.
Bibliographic record
Abstract
Introduction: Quercus robur L. shows anti-inflammatory, antimicrobial, antioxidative and astringent activity, and contains the following chemical compounds: flavonoids, tannins, phytoncide, phlobaphenes and phloroglucinol. Objective: The primary objective of this study was to investigate cytotoxic and antimicrobial activity of Quercus robur L. extract. Method: This study was designed as an experimental in vitro study on microorganisms. Chopped plant material was extracted using solvent (aqua) at room temperature and then left to dry in vacuum evaporators. We examined cytotoxic effects of different concentrations (10-1000g/ml) of Quercus robur L. on Klebsiella pneumoniae. Examination of antimicrobial activity of the extract was performed using disc diffusion method on Mueller-Hinton outpoured agar in a Petri dish. As the control, we used discs with Gentamicin. Results were obtained by measuring the inhibition zone diameter.Results: Our results revealed that the decrease of the extract concentration (from 1000g/ml to 10g/ml) of Quercus robur L. showed significantly lower cytotoxicity (p<0.05) on brine shrimp larvae. We demonstrated a strong positive correlation (r=0.959, p<0.05) between aqueous extract concentrations of Quercus robur L. and the number of dead larvae (Artemia salina L.). Additionally, aqueous extract of Quercus robur L. has shown effective antimicrobial activity on tested K. pneumoniae. Antibacterial activity of Quercus robur L. aqueous extract against Klebsiella pneumoniae was found at concentration of 1mg/mL. Maximum inhibition zone diameter was 15.40mm. Conclusion: Our study provides evidence that Q. robur cortex extract showed promising antibacterial activity against K. pneumoniae.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".