Evaluation of peroxisome proliferator‐activated receptor agonists on interleukin‐5‐induced eosinophil differentiation
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor (PPAR) agonists have been suggested as novel therapeutics for the treatment of inflammatory lung disease, such as allergic asthma. Treatment with PPAR agonists has been shown to inhibit airway eosinophilia in murine models of allergic asthma, which can occur through several mechanisms including attenuated generation of chemoattractants (e.g. eotaxin) and decreased eosinophil migrational responses. In addition, studies report that PPAR agonists can inhibit the differentiation of several cell types. To date, no studies have examined the effects of PPAR agonists on interleukin-5 (IL-5) -induced eosinophil differentiation from haemopoietic progenitor cells. Non-adherent mononuclear cells or CD34(+) cells isolated from the peripheral blood of allergic subjects were grown for 2 weeks in Methocult(®) cultures with IL-5 (10 ng/ml) and IL-3 (25 ng/ml) in the presence of 1-1000 nm PPARα agonist (GW9578), PPARβ/δ agonist (GW501516), PPARγ agonist (rosiglitazone) or diluent. The number of eosinophil/basophil colony-forming units (Eo/B CFU) was quantified by light microscopy. The signalling mechanism involved was assessed by phosphoflow. Blood-extracted CD34(+) cells cultured with IL-5 or IL-5 + IL-3 formed Eo/B CFU, which were significantly inhibited by rosiglitazone (100 nm, P < 0·01) but not GW9578 or GW501516. In addition, rosglitazone significantly inhibited IL-5-induced phosphorylation of extracellular signal-regulated kinase 1/2. We observed an inhibitory effect of rosiglitazone on eosinophil differentiation in vitro, mediated by attenuation of the extracellular signal-regulated kinase 1/2 signalling pathway. These findings indicate that the PPARγ agonist can attenuate tissue eosinophilia by interfering with local differentiative responses.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".