Antibacterial, Antibiofilm, and Antioxidant Activity of 15 Different Plant-Based Natural Compounds in Comparison with Ciprofloxacin and Gentamicin
Bibliographic record
Abstract
Plant-based natural compounds (PBCs) are comparatively explored in this study to identify the most effective and safe antibacterial agent/s against six World Health Organization concern pathogens. Based on a contained systematic review, 11 of the most potent PBCs as antibacterial agents are included in this study. The antibacterial and antibiofilm efficacy of the included PBCs are compared with each other as well as common antibiotics (ciprofloxacin and gentamicin). The whole plants of two different strains of Cannabis sativa are extracted to compare the results with sourced ultrapure components. Out of 15 PBCs, tetrahydrocannabinol, cannabidiol, cinnamaldehyde, and carvacrol show promising antibacterial and antibiofilm efficacy. The most common antibacterial mechanisms are explored, and all of our selected PBCs utilize the same pathway for their antibacterial effects. They mostly target the bacterial cell membrane in the initial step rather than the other mechanisms. Reactive oxygen species production and targeting [Fe-S] centres in the respiratory enzymes are not found to be significant, which could be part of the explanation as to why they are not toxic to eukaryotic cells. Toxicity and antioxidant tests show that they are not only nontoxic but also have antioxidant properties in Caenorhabditis elegans as an animal model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".