Inflammatory Molecule Elaboration in Secondhand Smoke (SHS)-Induced or Conditional RAGE Transgenic Modeling of Chronic Rhinosinusitis (CRS)
Bibliographic record
Abstract
Chronic rhinosinusitis (CRS) is characterized by sinonasal inflammation, mucus overproduction, and edematous mucosal tissue. This inflammatory condition is characterized by mucosal thickening, nasal obstruction, facial pain or pressure, hyposmia, and nasal discharge. The aim of this research was to clarify a potential role for the receptor for advanced glycation end-products (RAGE) in mouse nasoantral epithelium in perpetuating pro-inflammatory cytokine elaboration similarly expressed by CRS patients. Specifically, wild-type (WT) mice and transgenic (TG) mice overexpressing RAGE in sinonasal epithelium (RAGE TG mice) were maintained in room air or subjected to secondhand smoke exposure using a nose-only delivery system (Scireq Scientific, Montreal, QC, Canada) for five days per week over a 30-day period. Histological analysis was performed using staining for RAGE. Tissue lysates were analyzed for pro-inflammatory cytokines. We observed increased RAGE expression in sinus tissue following SHS exposure and in sinuses from RAGE TG mice in the absence of SHS. We also discovered elevated T helper (Th)1 products (TNF-α, IL-1β, IFN-γ) and Th2/Th17 (IL-5, IL-13, IL-17A) cytokine abundance in SHS-exposed WT and SHS-exposed RTG tissues compared to room air controls. These findings highlight the pivotal role of RAGE signaling in the exacerbation of inflammatory processes, particularly in the context of chronic inflammation induced by smoke exposure. The study expands our understanding of the RAGE signaling axis as a key contributor to the progression of smoke-related lung and sinonasal pathologies. Targeting RAGE-mediated pathways could represent a novel therapeutic strategy to mitigate the progression of chronic sinusitis associated with smoke exposure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".