Effect of inhaled hydrosoluble curcumin on inflammatory markers in broncho-alveolar lavage fluid of horses with LPS-induced lung neutrophilia
Bibliographic record
Abstract
BACKGROUND: Horses commonly suffer from chronic respiratory disease and are also used in large animal models of spontaneous or induced airway inflammation. The anti-inflammatory properties of curcumin are largely described but its low bioavailability precludes its clinical use. NDS27, a lysin salt of curcumin incorporated in beta-cyclodextrine, has high bioavailability and can be administered by inhalation. The aim of this study was to investigate the effects of inhaled NDS27 on inflammatory cytokines and proteins in the broncho-alveolar lavage fluid using a model of neutrophilic airway inflammation. METHODS: Airway neutrophilia was induced in eight horses by inhalation of lipopolysaccharides (LPS). Horses were treated with either inhalation of NDS27 or with placebo in a randomized cross-over design. Broncho-alveolar lavages were performed 6 hours after stimulation with LPS. Percentage of neutrophils, concentrations of IL-1β, TNF-α, IL-6, Club cell secretory protein, myeloperoxidase (MPO) and elastase (ELT) concentrations were determined. RESULTS: LPS stimulation induced significant increases in neutrophil counts and concentrations of IL-6 (70.2 ± 66.0 pg/ml), TNF-α (43.9 ± 31.2 pg/ml), MPO (580.9 ± 327.0 ng/ml) and ELT (27.6 ± 16.7 ng/ml). Treatment with NDS27 significantly prevented the increase in active and total MPO (216.4 ± 118.1 ng/ml) and ELT (5.9 ± 3.2 ng/ml) while there was a trend towards reduced IL-6 concentration. CONCLUSIONS: Results show that, although not reducing neutrophil recruitment, NDS27 largely abolishes LPS-induced neutrophil degranulation. Reduced levels of ELT and MPO, as well as reduced MPO activity may have beneficial effects via a reduced production of reactive oxygen species implicated in chronic inflammation and airway remodeling.
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 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) | 0.001 | 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".