Glutamic-Alanine Rich Glycoprotein from Undaria pinnatifida: A Promising Natural Anti-Inflammatory Agent
Bibliographic record
Abstract
This study aimed to assess the anti-inflammatory properties of a bioactive glutamic-alanine rich glycoprotein (GP) derived from Undaria pinnatifida on both LPS-stimulated RAW264.7 cells, peritoneal macrophages, and mouse models of carrageenan- and xylene-induced inflammation, investigating the underlying molecular mechanisms. In both in-vitro and in-vivo settings, GP was found to reduce the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) while also inhibiting the production of nitric oxide (NO) and prostaglandin E2 (PGE2) in response to lipopolysaccharide (LPS) stimulation. GP treatment significantly impeded the nuclear translocation of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway by blocking the phosphorylation of IKKα and IκBα, leading to a reduction in proinflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6). Additionally, GP effectively inhibited the activation of mitogen-activated protein kinases (MAPKs), with specific inhibitors of p38 and extra-cellular signal regulated kinase (ERK) enhancing GP’s anti-inflammatory efficacy. Notably, GP administration at 10 mg/kg/day (p.o.) markedly reduced carrageenan-induced paw inflammation and xylene-induced ear edema by preventing the infiltration of inflammatory cells into targeted tissues. GP treatment also downregulated key inflammatory markers, including iNOS, COX-2, IκBα, and NF-κB, by suppressing the phosphorylation of p38 and ERK, thereby improving the inflammatory index in both carrageenan- and xylene-induced mouse models. These findings suggest that marine resources, particularly seaweeds like U. pinnatifida, could serve as valuable sources of natural anti-inflammatory proteins for the effective treatment of inflammation and related conditions.
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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".