Quercetin alleviates the progression of chronic rhinosinusitis by affecting nasal mucosal epithelial remodeling, inflammation, and Treg/Th17 imbalance
Bibliographic record
Abstract
Nasal mucosal epithelial tissue remodeling and persist inflammation are related to the development of chronic rhinosinusitis (CRS). Quercetin possesses multiple biological properties in several inflammatory diseases. However, its roles in CRS remain unclear. In this study, Serumstaphylococcus aureus enterotoxin B (SEB) increased inflammatory response in human nasal epithelial cells (hNECs), which was reversed by quercetin. Moreover, quercetin inhibited SEB-evoked epithelial-mesenchymal transition (EMT) in hNECs by increasing EMT marker E-cadherin and decreasing N-cadherin expression. Concomitantly, SEB-induced increases in transcripts and release of MMP-9 were reduced by quercetin. Mechanistically, quercetin inhibited SEB-induced activation of the TLR2-NF-kB axis in hNECs. Moreover, restoring TLR2 signaling reversed quercetin-mediated inhibition of SEB-induced inflammation, EMT and MMP-9 expression. In vivo, quercetin attenuated histopathological changes of nasal mucosal tissues in Staphylococcus aureus-constructed CRS mice. Concomitantly, quercetin alleviated inflammatory response and nasal mucosal remodeling by suppressing EMT and MMP-9 levels. Additionally, quercetin ameliorated imbalance of Treg/Th17 proportions. Notably, quercetin suppressed activation of the TLR2-NF-kB axis, while restoring this signaling reversed quercetin-mediated protection against CRS. Thus, quercetin may attenuate pathological progression of CRS by inhibiting nasal mucosal tissue remodeling, inflammation and Treg/Th17 imbalance, which may be associated with inhibition of TLR2-NF-kB axis, supporting a promising therapeutic agent for CRS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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".