New Curcumin Analogue (PAC) Inhibits Candida albicans Virulence, Restricts Its Adhesion Potential, and Relieves Oral Epithelial Cell Inflammation and Defense Mechanisms
Bibliographic record
Abstract
Objectives: The oral cavity hosts one of the most complex microbial communities in the body. A disruption of the balance favors the growth of pathogenic species, contributing to oral diseases. The rise in microbial resistance has limited the effectiveness of conventional treatments, shifting the interest to natural product-based alternatives. Given its superior bioavailability and bioactivity in other models, this study investigates the antifungal potential of a novel curcumin derivative, PAC (3,5-bis(4-hydroxy-3-methoxybenzylidene)-N-methyl-4-piperidone), and studies its impact on host–pathogen dynamics and host defense mechanisms. Methods: Candida albicans was used as the model organism. Viability, growth kinetics, and colony formation were evaluated using optical density, agar culture, and MTT assay. Biofilm formation was assessed through electron microscopy and total sugar quantification. The morphological transition from hyphae to the less virulent blastospore was monitored using an optical microscope. The gene expression of adhesion factors and host defense markers was analyzed using RT-PCR. Results: PAC impairs C. albicans viability and reduces virulence by compromising biofilm formation and ensuring phenotypic transition to a blastospore form. Also, PAC controls C. albicans growth via necrosis/ROS pathways. As a result, PAC appears to repress host–pathogen interaction by downregulating SAPs, EAP1, and HWP1 adhesion genes, thus relieving the need to activate gingival epithelial cell defense mechanisms. This is highlighted by recording baseline levels of IL-6, IL-8, and IL-1β cytokines and antimicrobial β-defensin peptides in the presence of less virulent candida forms. Conclusions: PAC effectively reduces C. albicans virulence by limiting biofilm formation and adhesion while minimizing inflammatory responses. These findings support its potential as a promising therapeutic agent for infectious disease control.
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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) | 0.002 | 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".