Cranberry and Sumac Extracts Exhibit Antibacterial and Anti-Adhesive Effects Against <i>Streptococcus pyogenes</i>
Bibliographic record
Abstract
Group A Streptococci (GAS) or Streptococcus pyogenes is responsible for acute bacterial pharyngitis in children as well as adults. Streptococcal pharyngitis is initiated by successful attachment and colonization of the bacteria, followed by the establishment of the biofilm in various environments. In this study, we examined the antibacterial activities of in-house prepared aqueous and ethanolic extracts of 10 Atlantic Canada fruits in the context of minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), time–kill kinetics, and adhesion inhibition properties against S. pyogenes . Per our findings, MIC and MBC for all the tested extracts ranged from 0.25 to 8 mg/mL and from 4 to 64 mg/mL, respectively. Accordingly, at 1⁄2 × MBC, cranberry and sumac extracts also lowered the attachment of GAS to the uncoated and fibronectin-coated substratum. Particularly, cranberry and sumac aqueous extracts were more effective against the adhesion of S. pyogenes ATCC 19615 to the fibronectin-coated surface than a clinical strain. In conclusion, ethanolic and aqueous extracts of cranberry and sumac could potentially be incorporated into natural health products designed for the amelioration of strep throat, yet a detailed understanding of its mode of action ( e.g. , biofilm inhibition and eradication) could pave its path to the field of antibacterial natural health product discovery, design, and development.
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) | 0.000 | 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".