The STAT6 inhibitor AS1517499 reduces the risk of asthma in mice with 2,4-dinitrochlorobenzene-induced atopic dermatitis by blocking the STAT6 signaling pathway
Bibliographic record
Abstract
BACKGROUND: Epidemiological studies have revealed a link between atopic dermatitis (AD) and asthma. AS1517499, a selective signal transducer and activation of transcription 6 (STAT6) inhibitor, has been shown to effectively block this connection. In this study, we further explored the underlying mechanism by constructing an AD mouse model. METHODS: Female BALB/c mice were randomly divided into four groups (n = 10/group). The AD mouse model was established by 2,4-dinitrochlorobenzene induction with repeated ovalbumin challenge. AS1517499 and corn oil were used as treatment interventions. The features of airway inflammation, remodeling, and hyperactivity were analyzed. RESULTS: Active use of AS1517499 in AD mice effectively reduced Th2-related cytokine levels, alleviated airway eosinophil and lymphocyte infiltration, and regulated GATA3/Foxp3 levels and subepithelial collagen deposition. These changes might be due to specific blockade of the STAT6 signaling pathway. CONCLUSION: AS1517499 could partially block the association between AD and asthma by specifically inhibiting the STAT6 signaling pathway.
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) | 0.001 | 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) | 0.002 | 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".