Antibacterial and antioxidant activity of catechin, gallic acid, and epigallocatechin-3-gallate: focus on nanoformulations
Bibliographic record
Abstract
Up to one million secondary metabolites are found in plant species, some of which may have desirable therapeutic activities. Among these secondary metabolites, catechin, gallic acid, and epigallocatechin-3-gallate are natural phenolic compounds with promising antioxidant and antibacterial activity. However, these compounds have disadvantages of poor solubility, low bioavailability in physiological conditions, and side effects in patients. Therefore new strategies could rely on formulations with other synthetic and natural materials. Nanoformulations of secondary metabolites could be new efficient strategies to treat many chronic bacterial infections. Combinations and conjugates of catechin, gallic acid, and epigallocatechin-3-gallate with various antibiotics could reduce the dose of these compounds, increase their antibacterial activity, and decrease cytotoxicity against healthy cells. For instance, a smart combination of two or more secondary metabolites may improve therapeutic applications in physiological conditions. In this regard, the growth of antibiotic-resistant bacteria, specifically multidrug-resistant bacteria with overexpression of efflux pumps and expression of the penicillinase enzyme, has been inhibited significantly. According to recent investigations, this review will discuss the advances and challenges of new micro and nanoformulations of these natural products.
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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) | 0.000 | 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".