Phytochemical-rich medicinal plant extracts suppress bacterial antigens-induced inflammation in human tonsil epithelial cells
Bibliographic record
Abstract
Background Pharyngitis is an inflammatory condition of the pharynx and associated structures commonly caused by the Group A streptococci (GAS). There is a growing interest in discovering plant-based anti-inflammatory compounds as potential alternatives to conventional drugs. This study evaluated anti-inflammatory activity of phytochemical-rich extracts prepared from 12 herbal plants using human tonsil epithelial cells (HTonEpiC) in vitro . Methods The HTonEpiC were induced by a mixture of lipoteichoic acid (LTA) and peptidoglycan (PGN) (10 µg/mL; bacterial antigens) for 4 h and then exposed to ethanol extracts (EE) or aqueous extracts (AE) for 20 h. The secretion of four pro-inflammatory cytokines was measured using enzyme-linked immunosorbent assays (ELISA). Total phenolic and total flavonoid contents of the extracts were determined using spectrophotometric methods. Results The herbal plant extracts (≤5 µg/mL) were not cytotoxic to HTonEpiC. The extracts exhibited a broad range of reduction (1.2%–92.6%) of secretion of interleukin-8 (IL-8), human beta defensin-2 (hBD-2), epithelial-derived neutrophil activating protein-78 (ENA-78), and granulocyte chemotactic protein-2 (GCP-2). Both EE and AE of clove, ginger, and echinacea flower and EE from danshen root significantly inhibited the pro-inflammatory cytokine production as induced by LTA and PGN in HTonEpiCs at the concentrations of 1 and 5 µg/mL. Discussion Our observations indicate that danshen root, clove, ginger, and echinacea flower extracts exhibit an anti-inflammatory effect in HTonEpiCs. The most efficacious extracts from danshen root, clove, ginger and echinacea flowers have potential to be used as natural sources for developing phytotherapeutic products in the management of painful inflammation due to streptococcal pharyngitis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".