Active principles of <i>Grindelia robusta</i> exert antiinflammatory properties in a macrophage model
Bibliographic record
Abstract
Plant extracts and/or secondary metabolites are receiving considerable attention as therapeutic agents for treating inflammatory diseases such as periodontitis, which affects the tooth supporting tissues. The aim of this study was to investigate the effect of a Grindelia robusta extract enriched in saponins and polyphenols on Aggregatibacter actinomycetemcomitans lipopolysaccharide (LPS)-induced inflammatory mediator (IL-6, TNF-a, RANTES, MCP-1, PGE(2) ) and matrix metalloproteinase (MMP-1, -3, -7, -8, -9, -13) secretion by macrophages. LPS induced a marked increase in the secretion of all inflammatory mediators and MMPs tested by macrophages, as determined by enzyme-linked immunosorbent assays. At non-cytotoxic concentrations, the G. robusta extract inhibited dose-dependently the secretion of IL-6, RANTES, MCP-1 and, to a lesser extent, PGE(2) and TNF-a. Such inhibition was also observed for MMP-1, -3, -7, -8, -9 and -13 secretion. This ability of G. robusta extract to reduce the LPS-induced secretion of inflammatory mediators and MMPs was associated with a reduction of nuclear factor-kappa B (NF-kB) p65 activation. The results suggest that G. robusta extract possesses an antiinflammatory therapeutic potential through its capacity to reduce the accumulation of inflammatory mediators and MMPs.
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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) | 0.001 | 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 | 0.001 |
| 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".