Inhibitory Effect of Epigallocatechin Gallate on the Virulence of <i>Clostridium difficile</i> PCR Ribotype 027
Bibliographic record
Abstract
Clostridium difficile infection (CDI) is the most prevalent cause of health-care-associated infections. CDI-related health-care costs and deaths are both increasing annually on a global scale. C. difficile have been reported in food products in Canada, Europe, and the United States; however, the systematic transmission of C. difficile between humans and animals is yet to be understood. Because of the limitations of current therapeutic options, there is a need for the development of new patient treatments. Epigallocatechin gallate (EGCG) is a major catechin compound found in green tea extracts and exhibits antioxidant and antimicrobial activities. This study was conducted to investigate the inhibitory effects of EGCG on the expression of virulence genes in C. difficile and in C. difficile-associated diseases by inhibition of quorum sensing. The protein expression of autoinducer-2 (AI-2) was evaluated by AI-2 activity. EGCG at various concentrations had an inhibitory effect on AI-2 production, especially at 10 μg/mL. EGCG also significantly repressed the transcription of virulence genes, including luxS and tcdA, and prolonged the survival of Caenorhabditis elegans infected with C. difficile. Furthermore, treatment with EGCG effectively protected C. difficile-infected mice from C. difficile-induced death. Histological analysis of the colon and cecum of these mice revealed that EGCG protected tissues of the lower intestinal tract from damage. EGCG exerted growth-inhibitory and bactericidal activities on C. difficile in C. difficile-infected mice. Our results suggest that EGCG has significant antipathogenic effects on C. difficile and can be used to prevent or treat C. difficile-associated diseases or C. difficile infections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".