Carvacrol Suppresses Inflammatory Biomarkers Production by Lipoteichoic Acid- and Peptidoglycan-Stimulated Human Tonsil Epithelial Cells
Bibliographic record
Abstract
Pharyngitis is an inflammation of the pharynx caused by viral, bacterial, or non-infectious factors. In the present study, the anti-inflammatory efficacy of carvacrol was assessed using an in vitro model of streptococcal pharyngitis using human tonsil epithelial cells (HTonEpiCs) induced with Streptococcus pyogenes cell wall antigens. HTonEpiCs were stimulated by a mixture of lipoteichoic acid (LTA) and peptidoglycan (PGN) for 4 h followed by exposure to carvacrol for 20 h. Following exposure, interleukin (IL)-6, IL-8, human beta defensin-2 (HBD-2), epithelial-derived neutrophil-activating protein-78 (ENA-78), granulocyte chemotactic protein-2 (GCP-2), cyclooxygenase-2 (COX-2), tumor necrosis factor-alpha (TNF-α), and prostaglandin (PGE2) were measured by enzyme-linked immunosorbent assays (ELISA). The levels of pro-inflammatory cytokines, IL-6, IL-8, ENA-78, and GCP-2 were decreased in a carvacrol dose-dependent manner. The production of HBD-2 was significantly suppressed over 24 h carvacrol treatments. PGE2 and COX-2 levels in the cell suspensions were affected by carvacrol treatment. TNF-α was not detected. The cell viability of all the tested carvacrol concentrations was greater than 80%, with no morphological changes. The results suggest that carvacrol has anti-inflammatory properties, and carvacrol needs to be further assessed for potential clinical or healthcare applications to manage the pain associated with streptococcal pharyngitis.
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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) | 0.001 | 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".