Comparative efficacy of glucocorticoid receptor agonists on Th2 cell function and attenuation by progesterone
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Corticosteroids (CS)s suppress cytokine production and induce apoptosis of inflammatory cells. Prednisone and dexamethasone are oral CSs prescribed for treating asthma exacerbations. While prednisone is more commonly prescribed, dexamethasone is long acting and a more potent glucocorticoid receptor (GR) agonist. It can be administered as a one or two dose regime, unlike the five to seven days required for prednisone, a feature that increases compliance. We compared the relative ability of these two oral CSs to suppress type 2 inflammation. Since progesterone has affinity for the GR and women are more likely to relapse following an asthma exacerbation, we assessed its influence on CS action. RESULTS: Dexamethasone suppressed the level of IL-5 and IL-13 mRNA within Th2 cells with ~ 10-fold higher potency than prednisolone (the active form of prednisone). Dexamethasone induced a higher proportion of apoptotic and dying cells than prednisolone, at all concentrations examined. Addition of progesterone reduced the capacity of both CS to drive cell death, though dexamethasone maintained significantly more killing activity. Progesterone blunted dexamethasone-induction of FKBP5 mRNA, indicating that the mechanism of action was by interference of the CS:GR complex. CONCLUSIONS: Dexamethasone is both more potent and effective than prednisolone in suppressing type 2 cytokine levels and mediating apoptosis. Progesterone attenuated these anti-inflammatory effects, indicating its potential influence on CS responses in vivo. Collectively, our data suggest that when oral CS is required, dexamethasone may be better able to control type 2 inflammation, eliminate Th2 cells and ultimately lead to improved long-term outcomes. Further research in asthmatics is needed.
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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 | 0.000 |
| 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) | 0.000 | 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 it