Fucoidan Isolated from Saccharina japonica Inhibits LPS-Induced Inflammation in Macrophages via Blocking NF-κB, MAPK and JAK-STAT Pathways
Bibliographic record
Abstract
Fucoidan has been reported to have a variety of biological activities. However, different algae species, extraction methods, harvesting seasons, and growth regions lead to the structural variation of fucoidan, which would affect the bioactivities of fucoidan. To date, the anti-inflammatory properties and the underlying mechanism of fucoidan from brown alga Saccharina japonica (S. japonica) remain limited. The aims of the present study were to investigate the structure, the anti-inflammatory properties, and the potential molecular mechanisms of fucoidan isolated from S. japonica (SF6) against lipopolysaccharide (LPS)-activated RAW 264.7 macrophages. SF6 was characterized using high performance liquid gel permeation chromatography (HPGPC), Fourier transform infrared spectroscopy (FTIR), and nuclear magnetic resonance spectroscopy (NMR), and observed to be rich in fucose, galactose, and sulfate. Additionally, results showed that SF6 remarkably inhibited LPS-induced production of various inflammatory mediators and pro-inflammation cytokines, including nitric oxide (NO), NO synthase (iNOS), cyclooxygenase-2 (COX-2), tumor necrosis factor-α (TNF-α), interleukin-β (IL-β), and interleukin-6 (IL-6). A mechanism study showed that SF6 could effectively inhibit inflammatory responses through blocking LPS-induced inflammation pathways, including nuclear factor-κB (NF-κB), mitogen-activated protein kinase (MAPK), and Janus kinase (JAK)-2 and signal transducer and activator of transcription (STAT)-1/3 pathways. These results suggested that SF6 has the potential to be developed as an anti-inflammatory agent applied in functional food.
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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 | 0.000 |
| 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".