The Effect of Heparin and Other Exogenous Glycosaminoglycans (GAGs) in Reducing IL-1β-Induced Pro-Inflammatory Cytokine IL-8 and IL-6 mRNA Expression and the Potential Role for Reducing Inflammation
Bibliographic record
Abstract
Glycosaminoglycans (GAGs) are long linear polysaccharides found in every mammalian tissue. Previously thought only to be involved in cellular structure or hydration, GAGs are now known to be involved in cell signaling and protein modulation in cellular adhesion, growth, proliferation, and anti-coagulation. In this study, we showed that GAGs have an inhibitory effect on the IL-1β-stimulated mRNA expression of IL-6 and IL-8. Exogenous heparin (p < 0.0001), heparan (p < 0.0001), chondroitin (p < 0.049), dermatan (p < 0.0027), and hyaluronan (p < 0.0005) significantly reduced the IL-1β-induced IL-8 mRNA expression in HeLa cells. Exogenous heparin (p < 0.0001), heparan (p < 0.0001), and dermatan (p < 0.0027) also significantly reduced IL-1β-induced IL-6 mRNA expression in HeLa cells, but exogenous chondroitin and hyaluronan had no significant effect. The exogenous GAGs may reduce the transcription of these inflammatory cytokines through binding to TILRR, a co-receptor of IL-1R1, and block/reduce the interactions of TILRR with IL-1R1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 teacher head, 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".