Inhaled budesonide induces corticosteroid-dependent gene expression in asthmatics: Validation in primary epithelial and airways smooth muscle cells
Bibliographic record
Abstract
Rationale: Inhaled corticosteroids (ICS) reduce inflammatory gene expression. This is usually attributed to direct inhibition of inflammatory gene transcription by the glucocorticoid receptor. However, while corticosteroids induce anti-inflammatory gene expression in vitro , this has not been examined in asthmatic subjects taking ICS. Methods: Bronchial biopsies from atopic asthmatics taking inhaled budesonide (2×200 μg, twice daily for 11 days) or placebo were subjected to gene expression analysis using real-time reverse transcriptase-polymerase chain reaction. mRNA expression for the corticosteroid-inducible genes; TSC22D3 (GILZ), DUSP1 (MKP-1), both anti-inflammatory effectors, and FKBP5 (FKBP51), a regulator of glucocorticoid receptor function, was assessed. Cultured pulmonary epithelial and smooth muscle cells were also treated with corticosteroids before gene expression analysis. Results: Expression of GILZ and FKBP51 were significantly elevated in budesonide-treated subjects compared to placebo. Budesonide also increased GILZ expression in cultured epithelial and smooth muscle cells and immunostaining showed GILZ expression in the airways epithelium and smooth muscle of asthmatic subjects. Conclusions: Expression of corticosteroid-induced genes, including the anti-inflammatory gene, GILZ, is upregulated in the airways of asthmatic subjects taking medium daily doses of inhaled budesonide. The biological effects of such genes need to be considered when assessing ICS action. Funded by AstraZeneca.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".