Expression of retinoid receptors in lungs of cattle, dogs, and pigs.
Bibliographic record
Abstract
Retinoids play an important role in lung development and immune response. The effects of retinoids are mediated through 2 families of retinoid receptors: retinoic acid receptors (RARs) and retinoid X receptors (RXRs), with alpha (α), beta (β), and gamma (γ) subtypes in each family. To date, no data exist on the expression pattern of retinoid receptors in lungs of cattle, dogs, and pigs. Because of the biomedical importance of retinoid receptors in inflammation and immune responses, Western blot, immunohistology, and immunoelectron microscopy were used to determine the expression of retinoid receptors in normal lungs of cattle, dogs, and pigs (n = 2 for each species). Western blot showed expression of all 6 retinoid receptor subtypes in pig lungs. Immunohistology data indicated differential expression of retinoid receptors in airway epithelium, vascular endothelium, alveolar/septal macrophages, and alveolar septum in all 3 species. Electron microscopy showed nuclear localization of retinoid receptors in neutrophils and pulmonary intravascular macrophages. Retinoic acid receptors (RAR) α subtype were localized in cytoplasmic vacuoles of pig monocytes. These data indicate constitutive expression of retinoid receptors in the lungs of cattle, dogs, and pigs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".