Phenolic Compounds in Bacterial Inactivation: A Perspective from Brazil
Bibliographic record
Abstract
Phenolic compounds are natural substances that are produced through the secondary metabolism of plants, fungi, and bacteria, in addition to being produced by chemical synthesis. These compounds have anti-inflammatory, antioxidant, and antimicrobial properties, among others. In this way, Brazil represents one of the most promising countries regarding phenolic compounds since it has a heterogeneous flora, with the presence of six distinct biomes (Cerrado, Amazon, Atlantic Forest, Caatinga, Pantanal, and Pampa). Recently, several studies have pointed to an era of antimicrobial resistance due to the unrestricted and large-scale use of antibiotics, which led to the emergence of some survival mechanisms of bacteria to these compounds. Therefore, the use of natural substances with antimicrobial action can help combat these resistant pathogens and represent a natural alternative that may be useful in animal nutrition for direct application in food and can be used in human nutrition to promote health. Therefore, this study aimed to (i) evaluate the phenolic compounds with antimicrobial properties isolated from plants present in Brazil, (ii) discuss the compounds across different classes (flavonoids, xanthones, coumarins, phenolic acids, and others), and (iii) address the structure-activity relationship of phenolic compounds that lead to antimicrobial action.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".