High molecular weight hyaluronan reduces lipopolysaccharide mediated microglial activation
Bibliographic record
Abstract
Toll-like receptor-4 (TLR4) signaling has been implicated in microglial activation and propagation of inflammation following spinal cord injury (SCI). As such, modulating microglial activation through TLR4 represents an attractive therapeutic approach to treat SCI. High molecular weight hyaluronan (HMW-HA), a polymer with multiple therapeutic uses, has been previously shown to modulate TLR4 activation in macrophages and has shown early promise as a therapeutic agent in SCI. However, the mechanism associated with HMW-HA has not been fully elucidated or tested in microglia, a similar cell type. In the current study, we sought to determine the effects of HMW-HA on TLR4 activation in microglia and to gain insights into the mechanism of action. Rat primary microglial cultures were exposed to lipopolysaccharides (LPS) and HMW-HA, and the extent and mechanisms of inflammation were studied. HMW-HA decreased LPS mediated IL-1β, IL-6, and Tumor necrosis factor-α gene expression and IL-6 and nitric oxide production. This decrease was associated with a reduction in ERK 1/2 and p38 phosphorylation, was dependent on the continued presence of HMW-HA, and activation of Akt and A20 protein expression was reduced by HMW-HA. Together, our results show that HMW-HA can reduce LPS-mediated inflammatory signaling in microglia. We suggest that HA possibly mediates its effects by blocking the induction of inflammatory signaling through an extracellular mechanism.
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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".