Retinoid receptors are expressed in mouse and human lungs
Bibliographic record
Abstract
Retinoid receptors are members of nuclear receptor superfamily consisting of two distinct families: RARs (retinoic acid receptors) and RXRs (retinoid X receptors). Each family contains three receptor subtypes α, β, and γ. Retinoids transduce their effects through binding to retinoid receptors and inhibit transcription factors such as activator protein-1 and nuclear factor-κB (NF-κB) both of which regulate the transcription of several inflammatory genes. Considering the role of retinoid receptors in lung physiology, we need a precise understanding of their expression in normal and inflamed lungs. We used light and electron microscopic immunohistochemistry and Western blot to determine the expression of retinoid receptors in a murine model of endotoxin-induced (E. coli; 055:B5, 80 μg intranasal) acute lung inflammation and normal human lungs. Western blot showed expression of all six retinoid receptor subtypes in normal and inflamed mouse lungs. Immunohistology localized differential expression of retinoid receptors in airway epithelium, alveolar/septal macrophages, vascular endothelium, and alveolar septum in mouse lungs. Intranasal LPS challenge in mice resulted in increased expression of RXRα in airway epithelium compared to control animals. All six retinoid receptor subtypes were expressed in normal human lungs. Immunoelectron microscopy further confirmed the localization of all the receptors in various lung cells including the nucleus of these cells. The basal and altered expression of retinoid receptors in normal and inflamed lungs, respectively, may suggest their roles in lung pathophysiology.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".