Fine-Dust-Induced Skin Inflammation: Low-Molecular-Weight Fucoidan Protects Keratinocytes and Underlying Fibroblasts in an Integrated Culture Model
Bibliographic record
Abstract
Prolonged exposure to fine dust (FD) increases the risk of skin inflammation. Stimulated epidermal cells release growth factors into their extracellular environment, which can induce inflammation in dermal cells. Algae are considered rich sources of bioactive materials. The present study emphasized the effect of low-molecular-weight fucoidan isolated from Sargassum confusum (LMF) against FD-induced inflammation in HaCaT keratinocytes and underneath fibroblasts (HDFs) in an integrated culture model. HDFs were treated with media from FD-stimulated HaCaT with LMF treatments (preconditioned media). The results suggested that FD increased the oxidative stress in HaCaT, thereby increasing the sub-G1 phase of the cell cycle up to 587%, as revealed via flow cytometric analysis. With preconditioned media, HDFs also displayed oxidative stress; however, the increase in the sub-G1 phase was insignificant compared with HaCaT. LMF dose-dependently regulated the NF-κB/MAPK signaling in HaCaT. Furthermore, significant downregulation in NF-κB/MAPK signaling, as well as inflammatory cytokines, tissue inhibitors of metalloproteinases, matrix metalloproteinases, and reduction in relative elastase and collagenase activities related to the extracellular matrix degeneration were observed in HDFs with a preconditioned media treatment. Therefore, we concluded that HDFs were protected from inflammation by preconditioned media. Continued research on tissue culture and in vivo studies may reveal the therapeutic potential of LMF.
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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 | 0.001 |
| 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".