Impact of Citral and Phloretin, Alone and in Combination, on Major Virulence Traits of Streptococcus pyogenes
Bibliographic record
Abstract
Streptococcus pyogenes is well documented as a multi-virulent and exclusively human pathogen. The LuxS-based signaling in these bacteria has a crucial role in causing several infections through pathways that are pathogenic. This study evaluated the individual and synergistic effects of citral and phloretin against S. pyogenes in relation to major virulence traits. The in vitro synergy of citral and phloretin was evaluated by the checkerboard method. The fractional inhibitory concentration (FIC) values were calculated to determine the interactions between the inhibitors. The bacteria’s virulence properties were tested in the presence of the molecules, individually as well as in combination. Molecules’ cytotoxicity was tested using human tonsil epithelial cells. The synergistic effects of the molecules on the expression of biofilm and quorum sensing genes were tested using quantitative real-time polymerase chain reaction (qRT-PCR). The molecules were also tested for their impact on LuxS protein by molecular docking, modeling, and free-energy calculations. When the two molecules were assessed in combination (synergistic effect, FIC Index of 0.5), a stronger growth inhibitory activity was exhibited than the individual molecules. The cell surface hydrophobicity, as well as genes involved in quorum sensing and biofilm formation, showed greater suppression when the molecules were tested in combination. The in silico findings also suggest the inhibitory potential of the two molecules against LuxS protein. The binding orientation and the binding affinity of citral and phloretin well support the notion that there is a synergistic effect of citral and phloretin. The data reveal the combination of citral and phloretin as a potent antibacterial agent to combat the virulence of S. pyogenes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".