The effects of tea polyphenols on<i>Candida albicans</i>: inhibition of biofilm formation and proteasome inactivation
Bibliographic record
Abstract
The adherence of Candida albicans to one another and to various host and biomaterial surfaces is an important prerequisite for the colonization and pathogenesis of this organism. Cells in established biofilms exhibit different phenotypic traits and are inordinately resistant to antimicrobial agents. Recent studies have shown that black and green tea polyphenols exhibit both antimicrobial and strong cancer-preventive properties. Experiments were conducted to determine the effects of these polyphenols on C. albicans. Standard growth curves demonstrated a 40% reduction in the growth rate constant (K) with a 2 mg/mL concentration of Polyphenon 60, a green tea extract containing a mixture of polyphenolic compounds. Cultures treated with 1.0 micromol/L -(-)epigallocatechin-3-gallate (EGCG), the most abundant polyphenol, displayed a 75% reduction of viable cells during biofilm formation. Established biofilms treated with EGCG were also reduced, by 80%, as determined through XTT (2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide) colorimetric assays. Identical concentrations of epigallocatechin and epicatechin-3-gallate demonstrated similar biofilm inhibition. Further investigations regarding the possible mechanism of polyphenol action indicate that in vivo proteasome activity was significantly decreased when catechin-treated yeast cells were incubated with a fluorogenic peptide substrate that measured proteasomal chymotrypsin-like and peptidyl-glutamyl peptide-hydrolyzing activities. Impairment of proteasomal activity by tea polyphenols contributes to cellular metabolic and structural disruptions that expedite the inhibition of biofilm formation and maintenance by C. albicans.
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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) | 0.001 | 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) | 0.001 | 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".