Inhibitory effects on bacterial growth and b‐ketoacyl‐ACP reductase by different species of maple leaf extracts and tannic acid
Bibliographic record
Abstract
It is important to develop new antibiotics aimed at novel targets. The investigation found that the leaf extracts from five maples (Acer platanoides, Acer campestre, Acer rubrum, Acer saccharum and Acer truncatum Bunge collected in Denmark, Canada and China) and their component tannic acid displayed antibacterial ability against 24 standard bacteria strains with the minimum inhibitory concentration of 0.3-8.0 mg/mL. Unlike the standard antibiotic levofloxacin (LFX), these samples inhibited Gram-positive bacteria more effectively than they inhibited Gram-negative bacteria. These samples effectively inhibited two antidrug bacterial strains. The results show that these samples inhibit bacteria by a different mechanism from LFX. These samples potently inhibited b-ketoacyl-ACP reductase (FabG), which is an important enzyme in bacterial fatty acid synthesis. Tannic acid showed the strongest inhibition on FabG with a half inhibition concentration of 0.78 microM (0.81 microg/mL). Furthermore, tannic acid and two maple leaf extracts showed time-dependent irreversible inhibition of FabG. These three samples also exhibited better inhibition on bacteria. It is suggested that FabG is the antibacteria target of maple leaf extracts and tannic acid, and both reversible and irreversible inhibitions of FabG are important for the antibacterial effect.
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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 | 0.000 | 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 | 0.000 |
| 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".