Glucocorticoids and indacaterol regulated gene expressions profiling in human bronchial epithelial cells
Bibliographic record
Abstract
Introduction: Inhaled corticosteroid (ICS)/long-acting β2-adrenoceptor agonist (LABA) combination therapy proved to be effective in asthma management, but it was not rationally designed. In the present study, we describe using, the relationship between the intrinsic activities of GR ligands and the ability of the ultra-LABA, indacaterol, to enhance glucocorticoid (GC) dependent gene transcription. Methods: BEAS-2B airway epithelial cells were used as an experimental model. Expressions of glucocorticoid induced anti-inflammatory genes such as glucocorticoid-induced leucine zipper (GILZ), kinase inhibitor protein 2 of 57 kDa (p57kip2), cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) and a side effect gene pyruvate dehydrogenase kinase 4 (PDK4) were examined by qPCR. Results: Concentration-response curves were constructed to six GR ligands (Fluticasone furoate [FF], Dexamethasone [dex], desisobutyryl-ciclesonide [DC], GW870086X [GW], mifepristone [Mif] and ORG34517). Relative to Dex, a reference full agonist (intrinsic activity (α) = 1), DC, GW and Mif were partial agonists (α = 0.55, 0.36 and 0.02 respectively). Indacaterol significantly augmented expression of p57kip2, PDK4 and luciferase genes depending on the intrinsic activity of the GC. However, augmentation of expression of GILZ, CRISPLD2 in presence of indacaterol was independent of the intrinsic activity of the GC. Conclusions: We submit that the generation of glucocorticoid-inducible, gene “fingerprints” will provide important information that could assist the rational design of optimised ICS/LABA therapy for asthma and allied inflammatory diseases that hitherto, has not been possible.
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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 | 0.001 |
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".