N-Acetylglucosamine Reduces Inflammatory Response during Acute Peritonitis in Uremic Rats
Bibliographic record
Abstract
BACKGROUND: Peritoneal dialysis (PD) induces intraperitoneal inflammation and that process may be uremia. The goal of this study is to evaluate the effect of uremia on the kinetics of peritonitis and furthermore test the anti-inflammatory potential of N-acetylglucosamine (NAG) in a uremic environment. METHODS: Experiments were performed on healthy Wistar rats and on animals with impaired renal function. Acute PD was performed in all animals with dialysis fluid containing either glucose (GLU) or NAG as osmotic solutes. Peritonitis was induced by addition of lipopolysaccharide from Escherichia coli (LPS) to the dialysis fluid. Transperitoneal transport of water and solutes as well as intraperitoneal and systemic inflammation were evaluated. RESULTS: Uremia reduces peritoneal permeability to total protein during peritonitis (-33% vs. control, p < 0.001) and increases net ultrafiltration (+2.5 +/- 2.2 vs. -2.7 +/- 3.2 ml in control, p < 0.001). In uremic rats with peritonitis, reduced dialysate levels of the following inflammatory mediators were detected as compared to healthy animals: MCP-1 (-15%, p < 0.01); IL-1beta (-53%, p < 0.001), and elastase (-28%, p < 0.02). In the serum of uremic rats, the increase in TNFalpha and MCP-1 concentrations was smaller than in control rats: -44% (p < 0.02) and -39% (p < 0.001), respectively. NAG used as an osmotic solute in rats with preserved renal function decreases intraperitoneal and systemic inflammation during acute peritonitis. Drained dialysate volume was increased in the NAG group by 32% (p < 0.001) and transperitoneal loss of protein was reduced by 21% (p < 0.002). When NAG was used as the osmotic solute instead of GLU, intraperitoneal inflammation in uremic animals was further reduced: TNFalpha (-40%, p < 0.05); IL-1beta (-49%, p < 0.005); MCP-1 (-21%, p < 0.005). The presence of NAG also reduced the increased blood level of IL-1beta (-47%,p < 0.02) and MCP-1 (-36%, p < 0.02). CONCLUSIONS: Intensity of acute peritonitis is reduced during uremia. NAG exerts a systemic and peritoneal anti-inflammatory action under conditions of uremia that confirms the potential use of this compound as an osmotic agent in the PD fluids that also decreases inflammation.
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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) | 0.001 | 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".