Glucocorticoid receptor expression and glucocorticoid therapeutic effect in nasal polyps
Bibliographic record
Abstract
PURPOSE: To investigate the expression and quantity of glucocorticoid receptor-alpha and -beta in polyp tissues taken from the patients treated were subsequently treated with topical glucocorticoid (GC). METHODS: Eighty patients with nasal polyps were initially enrolled in the study. All polyp specimens were obtained prior to treatment. Patients then received daily topical GC spray treatment for one month. Polyp specimens were tested for glucocorticoid receptor (GR) GR-alpha and GR-beta mRNA expression using fluorescent quantitative-reverse transcription-polymerase chain reaction (FQ-RT-PCR). Thirty healthy nasal mucosa tissue samples were tested at the same time. RESULTS: Forty patients finished the study and were divided into two groups: GC-sensitive (n=26) and GC-insensitive (n=14), according to treatment results. GR-beta mRNA expression in the nasal polyp tissues of the GC-insensitive group (5.72+/-0.58x10(2) copies/microg) was higher than that in the GC-sensitive group (4.82+/-0.28x10(2) copies/microg, P < 0.05) and in the normal nasal mucosa group (4.44+/-0.35x10(2) copies/microg, P < 0.01). There was also a difference in the relative expression of GR-alpha and GR-beta between the GC-sensitive group (GR-alpha/GR-beta= 829.42+/-67.36) and the GC-insensitive group (535.7+/-89) (P < 0.01). CONCLUSION: GR-beta mRNA was highly expressed in patients with nasal polyps. Down- regulation of GR-alpha mRNA suggests the existence of glucocorticoid insensitivity. Expression of GR-beta may plays an important role in the evaluation of the glucocorticoid therapeutic effect in patients with nasal polyps.
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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 | 0.000 |
| 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) | 0.001 | 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".