Identification of multiple isoforms of glucocorticoid receptor in nasal polyps of patients with chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: The conventional belief that glucocorticosteroid (GC) acts through a single brand glucocorticoid receptor (GR)α protein has changed dramatically with the discovery of multiple GR isoforms. We aimed to evaluate whether multiple GR protein isoforms are expressed in chronic rhinosinusitis with nasal polyps (CRSwNP) and whether GR protein isoform expression profiles differ between different endotypes of CRSwNP. METHODS: Thirty-eight patients with CRSwNP and ten healthy volunteers were included. The protein expression of multiple GR isoforms in nasal polyps (NPs) tissue and control mucosae was examined by western blot analysis with different GR antibodies. RESULTS: Five bands, including three bands for known proteins (GRα-A/B, GRα-C, and GRα-D) and two bands for unidentified proteins at 67 kilodaltons (kDa) and 60 kDa, were identified with both total GR antibody (PA1-511A) and GRα-specific antibody (PA1-516). GRα-D intensity, which was abundant in nasal mucosa, was significantly increased in the CRSwNP group and was especially elevated in the noneosinophilic CRSwNP (NE-CRSwNP) group (PA1-511A: P < 0.001 and P = 0.0018; PA1-516: P < 0.003 and P = 0.006, respectively). Additionally, the intensities of the newly recognized 67 kDa and 60 kDa bands were much greater in the NE-CRSwNP subgroup than in the eosinophilic CRSwNP (E-CRSwNP) subgroup; in the E-CRSwNP subgroup, the median intensities were even lower than those in the control group. CONCLUSIONS: This study provides evidence that nasal tissues express multiple GR protein isoforms. GR protein isoforms presented disease and tissue-specific expression profiles that differed between the CRSwNP and control groups and between the E-CRSwNP and NE-CRSwNP subgroups.
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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 | 0.001 | 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".