Proresolving Action of Docosahexaenoic Acid Monoglyceride in Lung Inflammatory Models Related to Cystic Fibrosis
Bibliographic record
Abstract
Cystic fibrosis (CF) is a hereditary, chronic disease of the exocrine glands, characterized by the production of viscid mucus that obstructs the pancreatic ducts and bronchi, leading to infection and fibrosis. ω3 fatty acid supplementations are known to improve the essential fatty acid deficiency as well as reduce inflammation in CF. The objective of this study was to determine the effects of docosahexaenoic acid monoacylglyceride (MAG-DHA) on mucin overproduction and resolution of airway inflammation in two in vitro models related to CF. Isolated human bronchi reverse permeabilized with CF transmembrane conductance regulator (CFTR) silencing (si) RNA and stable Calu3 cells expressing a short hairpin (sh) RNA directed against CFTR (shCFTR) were used. Lipid analyses revealed that MAG-DHA increased DHA/arachidonic acid (AA) ratio in shCFTR Calu-3 cells. MAG-DHA treatments, moreover, resulted in a decreased activation of Pseudomonas aeruginosa LPS-induced NF-κB in CF and non-CF Calu-3 cells. Data also revealed a reduction in MUC5AC, IL-6, and IL-8 expression levels in MAG-DHA-treated shCFTR cells stimulated, or not, with LPS. Antiinflammatory properties of MAG-DHA were also investigated in a reverse-permeabilized human bronchi model with CFTR siRNA. After MAG-DHA treatments, messenger RNA transcript levels for MUC5AC, IL-6, and IL-8 were markedly reduced in LPS-treated CFTR siRNA bronchi. MAG-DHA displays antiinflammatory properties and reduces mucin overexpression in Calu-3 cells and human bronchi untreated or treated with P. aeruginosa LPS, a finding consistent with the effects of resolvinD1, a known antiinflammatory 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".