RAGE signaling during tobacco smoke-induced lung inflammation and potential therapeutic utility of SAGEs
Bibliographic record
Abstract
BACKGROUND: Smoke exposure culminates as a progressive lung complication involving airway inflammation and remodeling. While primary smoke poses the greatest risk, nearly half of the US population is also at risk due to exposure to secondhand smoke (SHS). METHODS: We used WT, RAGE-/- (KO), and Tet-inducible lung-specific RAGE overexpressing transgenic (TG) mice to study the role of RAGE during short-term responses to SHS. We evaluated SHS effects in mice with and without semi-synthetic glycosaminoglycan ethers (SAGEs), which are anionic, partially lipophilic sulfated polysaccharide derivatives known to inhibit RAGE signaling. TG Mice were weaned and fed doxycycline to induce RAGE at postnatal day (PN) 30. At PN40, mice from each line were exposed to room air (RA) or SHS from three Kentucky 3R4F research cigarettes via a nose-only delivery system (Scireq Scientific, Montreal, Canada) five days a week and i.p. injections of PBS or SAGE (30 mg/kg body weight) occurred three times per week from PN40-70 before mice were sacrificed on PN70. RESULTS: RAGE mRNA and protein expression was elevated following SHS exposure of control and TG mice and not detected in RAGE KO mice. Bronchoalveolar lavage fluid (BALF) analysis revealed RAGE-mediated influence on inflammatory cell diapedesis, total protein, and pro-inflammatory mediators following exposure. Lung histological assessment revealed indistinguishable morphology following exposure, yet parenchymal apoptosis was increased. Inflammatory signaling intermediates such as Ras and NF-κB, as well as downstream responses were influenced by the availability of RAGE, as evidenced by RAGE KO and SAGE treatment. CONCLUSIONS: These data provide fascinating insight suggesting therapeutic potential for the use of RAGE inhibitors in lungs exposed to SHS smoke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".