The Anti-Inflammatory Effect of Low Molecular Weight Fucoidan from Sargassum siliquastrum in Lipopolysaccharide-Stimulated RAW 264.7 Macrophages via Inhibiting NF-κB/MAPK Signaling Pathways
Bibliographic record
Abstract
Brown seaweed is a rich source of fucoidan, which exhibits a variety of biological activities. The present study discloses the protective effect of low molecular weight fucoidan (FSSQ) isolated from an edible brown alga, Sargassum siliquastrum, on lipopolysaccharide (LPS)-stimulated inflammatory responses in RAW 264.7 macrophages. The findings of the study revealed that FSSQ increases cell viability while decreasing intracellular reactive oxygen species production in LPS-stimulated RAW 264.7 macrophages dose-dependently. FSSQ reduced the iNOS and COX-2 expression, inhibiting the NO and prostaglandin E2 production. Furthermore, mRNA expression of IL-1β, IL-6, and TNF-α was downregulated by FSSQ via modulating MAPK and NF-κB signaling. The NLRP3 inflammasome protein complex, including NLRP3, ASC, and caspase-1, as well as the subsequent release of pro-inflammatory cytokines, such as IL-1β and IL-18, release in LPS-stimulated RAW 264.7 macrophages was inhibited by FSSQ. The cytoprotective effect of FSSQ is indicated via Nrf2/HO-1 signaling activation, which is considerably reduced upon suppression of HO-1 activity by ZnPP. Collectively, the study revealed the therapeutic potential of FSSQ against inflammatory responses in LPS-stimulated RAW 264.7 macrophages. Moreover, the study suggests further investigations on commercially viable methods for fucoidan isolation.
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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) | 0.000 | 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 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".