Anti-Fine Dust Effect of Fucoidan Extracted from Ecklonia maxima Leaves in Macrophages via Inhibiting Inflammatory Signaling Pathways
Bibliographic record
Abstract
Brown seaweeds contain fucoidan, which has numerous biological activities. Here, the anti-fine-dust activity of fucoidan extracted from Ecklonia maxima, an abundant brown seaweed from South Africa, was explored. Fourier transmittance infrared spectroscopy, high-performance anion-exchange chromatography with pulsed amperometric detection analysis of the monosaccharide content, and nuclear magnetic resonance were used for the structural characterization of the polysaccharides. The toll-like receptor (TLR)-mediated nuclear factor kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) signaling pathways were evaluated. The results revealed that E. maxima purified leaf fucoidan fraction 7 (EMLF7), which contained the highest sulfate content, showed the best anti-inflammatory activity by attenuating the TLR-mediated NF-κB/MAPK protein expressions in the particulate matter-stimulated cells. This was solidified by the successful reduction of Prostaglandin E2, NO, and pro-inflammatory cytokines, such as TNF-α, IL-6, and IL-1β. The current findings confirm the anti-inflammatory activity of EMLF7, as well as the potential use of E. maxima as a low-cost fucoidan source due to its abundance. This suggests its further application as a functional ingredient in consumer products.
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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".