Antibacterial Activity of Methanolic Extracts, Decoction and Isolated Triterpene Products from Different Parts of Birch, Betula pendula, Roth
Bibliographic record
Abstract
Five methanolic extracts obtained from different parts of birch, Betula pendula, Roth. (external and internal bark, flowers, leaves and buds), were evaluated for their antibacterial activity in this study. Triterpene compounds, betulin, betulinic acid, oleanolic acid and lupeol, were isolated from the external parts of birch bark using the method of dry column chromatography (DCC) as well as preparative thin layer chromatography (TLC). These compounds were also investigated for their antibacterial activity. Taking into account that decoction is the most commonly used pharmaceutical form of herbal drug, decoctions made from external bark, leaf, flower and bud were investigated for their antibacterial activity. Antibacterial screening, against selected Gram-positive bacteria, Bacillus subtilis ATCC 6633, Staphylococcus aureus ATCC 6538P and Gram-negative bacteria, Escherichia coli ATCC 8739 and Pseudomonas aeruginosa ATCC 9027 was carried out. The methods of diffusion and dilution were used for this investigation according to European Pharmacopoea, 1996. The most prominent antibacterial activity showed oleanolic acid against bacterial species Staphylococcus aureus, expressed as minimal inhibitory concentration (MIC): 1.25% and Bacillus subtilis MIC: 0.625%. Escherichia coli showed resistance on all investigated samples.
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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) | 0.001 | 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".