Anti-Inflammatory Effect of Pre-Elafin in Lipopolysaccharide-Induced Acute Lung Inflammation
Bibliographic record
Abstract
The aim of the present study was to evaluate the anti-inflammatory activity of pre-elafin, an elastase-specific inhibitor, in lipopolysaccharide (LPS)-induced acute lung inflammation. C57BL/6 mice were pre-treated intranasally with recombinant human pre-elafin or vehicle only. One hour later, they were instilled intranasally with LPS (2 microg/mouse). Animals were sacrificed 6 hours after LPS instillation and bronchoalveolar lavage (BAL) was performed with three 1-ml aliquots of saline. LPS induced a lung inflammation characterised by a 100-fold increase in BAL neutrophils compared to control animals (265.8 +/- 54.5 x 10(3) and 2.4 +/- 1.3 x 10(3) neutrophils/ml, respectively). Pre-elafin dose-dependently reduced the neutrophil influx in the lung alveolar spaces by up to 84%. No elastase activity was detectable in all BAL fluids tested. Pre-elafin also reduced significantly LPS-induced gelatinase activity, as shown by zymography, and BAL macrophage inflammatory protein-2 (MIP-2) and KC levels, two potent neutrophil attractants and activators. Moreover, pre-elafin also significantly reduced mRNA levels of the three members of the IL-1 ligand family, namely IL-1alpha, IL-1beta and IL-1 receptor antagonist (IL-1Ra), type II IL-1 receptor, and TNFalpha as assessed in whole lung tissue by RNase protection assay. Thus, pre-elafin may be considered as a potent anti-inflammatory mediator.
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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".