Chemically Modified N-Acylated Hyaluronan Fragments Modulate Proinflammatory Cytokine Production by Stimulated Human Macrophages
Bibliographic record
Abstract
Low molecular mass hyaluronans are known to induce inflammation. To determine the role of the acetyl groups of low molecular mass hyaluronan in stimulating the production of proinflammatory cytokines, partial N-deacetylation was carried out by hydrazinolysis. This resulted in 19.7 ± 3.5% free NH2 functional groups, which were then acylated by reacting with an acyl anhydride, including acetic anhydride. Hydrazinolysis resulted in bond cleavage of the hyaluronan chain causing a reduction of the molecular mass to 30-214 kDa. The total NH2 and N-acetyl moieties in the reacetylated hyaluronan were 0% and 98.7 ± 1.5% respectively, whereas for butyrylated hyaluronan, the total NH2, N-acetyl, and N-butyryl moieties were 0, 82.2 ± 4.6, and 22.7 ± 3.8%, respectively, based on (1)H NMR. We studied the effect of these polymers on cytokine production by cultured human macrophages (THP-1 cells). The reacetylated hyaluronan stimulated proinflammatory cytokine production to levels similar to LPS, whereas partially deacetylated hyaluronan had no stimulatory effect, indicating the critical role of the N-acetyl groups in the stimulation of proinflammatory cytokine production. Butyrylated hyaluronan significantly reduced the stimulatory effect on cytokine production by the reacetylated hyaluronan or LPS but had no stimulatory effect of its own. The other partially N-acylated hyaluronan derivatives tested showed smaller stimulatory effects than reacetylated hyaluronan. Antibody and antagonist experiments suggest that the acetylated and partially butyrylated lower molecular mass hyaluronans exert their effects through the TLR-4 receptor system. Selectively N-butyrylated lower molecular mass hyaluronan shows promise as an example of a novel semisynthetic anti-inflammatory molecule.
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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".