Negative Relationship Between Expression of Glucocorticoid Receptor α and Disease Activity: Glucocorticoid Treatment of Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Glucocorticoid receptor alpha (GRalpha) is crucial for glucocorticoids (GC) to carry out their physiological and pharmacological roles. Studies have shown the disorder of GR-GC systems in autoimmune diseases. Our study was performed to test the relationship between GRalpha expression and disease activity of systemic lupus erythematosus (SLE). METHODS: The responses of 55 patients with SLE to GC were screened. We examined GRalpha mRNA and protein expression in peripheral blood mononuclear cells from SLE patients and healthy volunteers by reverse transcriptase-polymerase chain reaction and Western blotting. RESULTS: Expression of GRalpha in patients with SLE was lower than that in controls (p < 0.05). Expression of GRalpha obviously decreased after administration of GC in the steroid-sensitive group with SLE (p < 0.05). Expression of GRalpha was negatively correlated with SLE Disease Activity Index scores in the steroid-sensitive group with SLE. CONCLUSION: The expression of GRalpha in patients with SLE was low and there was a negative correlation between GRalpha expression and disease activity; these findings might provide insight into the pathogenesis of SLE and help to screen whether the patient is sensitive to GC treatment. (Heilungkiang Provincial Health Department Guiding Projects Funding. Trial registration No. 2006-094.).
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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 | 0.002 |
| 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 | 0.000 |
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".