Spatial expression of Receptor for Advanced Glycation End‐Products (RAGE) in diverse tissue and organ systems differs following exposure to secondhand cigarette smoke
Bibliographic record
Abstract
The receptor for advanced glycation end‐products (RAGE) is a pattern recognition receptor initially identified in the lung and known to be expressed by many cell types including smooth muscle cells, fibroblasts, macrophages/monocytes and epithelial cells. RAGE is capable of binding a variety of endogenous ligands including advanced glycation end‐products (AGEs), S100/calgranulins, amyloid‐β peptide, and HMGB1. Furthermore, exogenous entities such as tobacco smoke particulates also have been demonstrated to activate RAGE and as such, RAGE signaling has been implicated as a major contributor to a variety of inflammatory states. In the present study we examined the expression patterns of RAGE in pulmonary and systemic tissues from mice exposed to room air (RA) and then compared expression with mice following exposure to daily secondhand smoke for up to 6 months via a nose‐only exposure system (Scireq, Montreal, Canada). Following sacrifice, real time PCR revealed interesting expression patterns for RAGE mRNA in tissues and organs such as eyes, lungs, heart, kidney, spleen, gastrocnemius, liver, brain, small intestine, adipose tissue, and dermis. RT‐PCR analysis revealed that augmented RAGE expression is a cellular response to secondhand smoke exposure. Confirmatory studies aimed at characterizing protein levels were also conducted using immunoblot and immunohistochemistry. In conclusion, our data reveal that as already detected in several studies involving the pulmonary apparatus, elevated RAGE expression is a distinct response to smoke exposure and its expression likely elevates the pro‐inflammatory RAGE signaling axis in cells and tissues responding to exposure. Support or Funding Information This work was supported by a grant from the Flight Attendant's Medical Research Institute (FAMRI, PRR) and a BYU Mentoring Environment Grant (PRR).
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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 | 0.001 |
| 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".