Antimicrobial activity and chemical composition of white birch (<i>Betula papyrifera</i> Marshall) bark extracts
Bibliographic record
Abstract
Extracts from white birch have been reported to possess antimicrobial properties, but no study has linked the chemical composition of bark extract with antimicrobial activity. This study aimed to identify white birch (Betula papyrifera Marshall) bark extracts with antimicrobial activity and elucidate its composition. In order to obtain the highest extraction yield, bark residues >3 mm were retained for extraction. A total of 10 extraction solvents were used to determine the extraction yield of each of them. Methanol and ethanol solvents extracted a greater proportion of molecules. When tested on eight microorganism species, the water extract proved to have the best antimicrobial potential followed by the methanol extract. The water extract inhibited all microorganisms at low concentration with minimal inhibitory concentration between 0.83 and 1.67 mg/ml. Using ultraperformance liquid chromatography coupled to a time-of-flight quadrupole mass spectrometer, several molecules that have already been studied for their antimicrobial properties were identified in water and methanol extracts. Catechol was identified as one of the dominant components in white birch bark water extract, and its antimicrobial activity has already been demonstrated, suggesting that catechol could be one of the main components contributing to the antimicrobial activity of this extract. Thus, extractives from forestry wastes have potential for new applications to valorize these residues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".