Lipopolysaccharides induce a RAGE-mediated sensitization of sensory neurons and fluid hypersecretion in the upper airways
Bibliographic record
Abstract
Thoracic dorsal root ganglia (tDRG) contribute to fluid secretion in the upper airways. Inflammation potentiates DRG responses, but the mechanisms remain under investigation. The receptor for advanced glycation end-products (RAGE) underlies potentiation of DRG responses in pain pathologies; however, its role in other sensory modalities is less understood. We hypothesize that RAGE contributes to electrophysiological and biochemical changes in tDRGs during inflammation. We used tDRGs and tracheas from wild types (WT), RAGE knock-out (RAGE-KO), and with the RAGE antagonist FPS-ZM1, and exposed them to lipopolysaccharides (LPS). We studied: capsaicin (CAP)-evoked currents and action potentials (AP), tracheal submucosal gland secretion, RAGE expression and downstream pathways. In WT neurons, LPS increased CAP-evoked currents and AP generation, and it caused submucosal gland hypersecretion in tracheas from WT mice exposed to LPS. In contrast, LPS had no effect on tDRG excitability or gland secretion in RAGE-KO mice or mice treated with FPS-ZM1. LPS upregulated full-length RAGE (encoded by Tv1-RAGE) and downregulated a soluble (sRAGE) splice variant (encoded by MmusRAGEv4) in tDRG neurons. These data suggest that sensitization of tDRG neurons contributes to hypersecretion in the upper airways during inflammation. And at least two RAGE variants may be involved in these effects of LPS.
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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 | 0.001 | 0.001 |
| 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".