Estradiol Increases Mucus Synthesis in Bronchial Epithelial Cells
Bibliographic record
Abstract
Airway epithelial mucus hypersecretion and mucus plugging are prominent pathologic features of chronic inflammatory conditions of the airway (e.g. asthma and cystic fibrosis) and in most of these conditions, women have worse prognosis compared with male patients. We thus investigated the effects of estradiol on mucus expression in primary normal human bronchial epithelial cells from female donors grown at an air liquid interface (ALI). Treatment with estradiol in physiological ranges for 2 weeks caused a concentration-dependent increase in the number of PAS-positive cells (confirmed to be goblet cells by MUC5AC immunostaining) in ALI cultures, and this action was attenuated by estrogen receptor beta (ER-β) antagonist. Protein microarray data showed that nuclear factor of activated T-cell (NFAT) in the nuclear fraction of NHBE cells was increased with estradiol treatment. Estradiol increased NFATc1 mRNA and protein in ALI cultures. In a human airway epithelial (1HAE0) cell line, NFATc1 was required for the regulation of MUC5AC mRNA and protein. Estradiol also induced post-translational modification of mucins by increasing total fucose residues and fucosyltransferase (FUT-4, -5, -6) mRNA expression. Together, these data indicate a novel mechanism by which estradiol increases mucus synthesis in the human bronchial epithelium.
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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".