Mini Review of Phytochemicals and Plant Taxa with Activity as Microbial Biofilm and Quorum Sensing Inhibitors
Bibliographic record
Abstract
Microbial biofilms readily form on many surfaces in nature including plant surfaces. In order to coordinate the formation of these biofilms, microorganisms use a cell-to-cell communication system called quorum sensing (QS). As formation of biofilms on vascular plants may not be advantageous to the hosts, plants have developed inhibitors to interfere with these processes. In this mini review, research papers published on plant-derived molecules that have microbial biofilm or quorum sensing inhibition are reviewed with the objectives of determining the biosynthetic classes of active compounds, their biological activity in assays, and their families of occurrence and range. The main findings are the identification of plant phenolics, including benzoates, phenyl propanoids, stilbenes, flavonoids, gallotannins, proanthocyanidins and coumarins as important inhibitors with both activities. Some terpenes including monoterpenes, sesquiterpenes, diterpenes and triterpenes also have anti-QS and anti-biofilm activities. Relatively few alkaloids were reported. Quinones and organosulfur compounds, especially from garlic, were also active. A common feature is the polar nature of these compounds. Phytochemicals with these activities are widespread in Angiosperms in temperate and tropical regions, but gymnosperms, bryophytes and pteridophytes were not represented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".