Expression of receptor activator of nuclear factor‐κB (RANK), RANK ligand, and osteoprotegerin in the normal and E. coli lipopolysaccharide‐treated horse lungs
Bibliographic record
Abstract
Receptor activator of nuclear factor‐κB ligand (RANKL), its receptor RANK and antagonist osteoprotegerin (OPG) are members of the TNF receptor superfamily. RANKL/RANK signaling mediates TNFα‐induced inflammation and RANKL acts as a chemoattractant for immune cells. OPG interacts with and blocks RANKL signal transduction. There are no or little data on the expression of RANKL, RANK, and OPG in normal or inflamed lungs. Therefore, we studied their expression in control and lipopolysaccharide (LPS) treated horse lungs. Immunohistochemistry showed RANKL expression in alveolar/septal macrophages, vascular endothelium, and alveolar septum but not in airway epithelium in control horses. The LPS treatment increased RANKL expression in airway epithelium and alveolar septum. While a weak staining for RANK was detected in airway epithelium and vascular endothelium of control horse lungs, the expression was increased in airway epithelium of LPS‐treated horses. OPG was expressed in airway epithelium and alveolar/septal macrophages and was increased in LPS‐treated lungs. Immunoelectron microscopy detected all three molecules in pulmonary intravascular and alveolar macrophages. Western blot confirmed expression of all three molecules in the lungs. These data show LPS‐induced changes in the expression of RANK, RANKL, and OPG to suggest their roles in endotoxin‐induced lung inflammation. Grant Funding Source : NSERC
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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 | 0.001 |
| 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".