Eckmaxol Isolated from Ecklonia maxima Attenuates Particulate-Matter-Induced Inflammation in MH-S Lung Macrophage
Bibliographic record
Abstract
Airborne particulate matter (PM) originating from industrial processes is a major threat to the environment and health in East Asia. PM can cause asthma, collateral lung tissue damage, oxidative stress, allergic reactions, and inflammation. The present study was conducted to evaluate the protective effect of eckmaxol, a phlorotannin isolated from Ecklonia maxima, against PM-induced inflammation in MH-S macrophage cells. It was found that PM induced inflammation in MH-S lung macrophages, which was inhibited by eckmaxol treatment in a dose-dependent manner (21.0−84.12 µM). Eckmaxol attenuated the expression of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) in PM-induced lung macrophages. Subsequently, nitric oxide (NO), prostaglandin E-2 (PGE-2), and pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α) were downregulated. PM stimulated inflammation in MH-S lung macrophages by activating Toll-like receptors (TLRs), nuclear factor-kappa B (NF-κB), and mitogen-activated protein kinase (MAPK) pathways. Eckmaxol exhibited anti-inflammatory properties by suppressing the activation of TLRs, downstream signaling of NF-κB (p50 and p65), and MAPK pathways, including c-Jun N-terminal kinase (JNK) and p38. These findings suggest that eckmaxol may offer substantial therapeutic potential in the treatment of inflammatory diseases.
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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".