Upregulated expression of Notch1/4 - JAG-1/DLL-1 detected in allergic rhinitis
Bibliographic record
Abstract
Abstract Background Allergic rhinitis (AR) is a chronic disease with high prevalence. There are currently many treatments available. However, despite an often good therapeutic response, many patients still report impairment in quality of life (QoL) during the pollen season. A skewed T helper (Th)2 polarization is a well-acknowledged pathologic feature of AR. In animal models, local notch signaling in peripheral tissue seems crucial for Th2 cell differentiation and the development of AR. However, the involvement of Notch signaling in Th2 cell differentiation and the development of AR in humans remains unknown. Hence, the present study investigated the human expression of Notch receptors on CD4+ T-cells in nasal mucosa and blood. Correspondingly Notch ligand expression was assessed on nasal epithelial cells and neutrophils. Methodology Nasal brush and blood samples from 18 patients with pollen-induced AR and 22 healthy controls were collected outside the pollen season. Notch 1–4 and Jagged-1,2 and Delta-like ligand 1,3–4 was analyzed using flow cytometry. Results The fraction of CD4+Notch1+ and CD4+Notch4+ T-cells was higher in AR patients than in healthy control patients. Further, the expression levels of the Notch ligands JAG-1 and DLL-1 were increased in nasal epithelial cells from AR patients compared to healthy control patients. In addition, AR patients displayed higher expression of JAG-1 on neutrophils both in the nasal mucosa and in peripheral blood. Conclusion The present study is the first to demonstrate increased activity in the Notch1/4 - JAG-1/DLL-1 pathways among allergic individuals. Further propagating the importance of Notch signalling in AR and blocking JAG-1 and DLL-1-induced Notch signalling by nasal epithelial cells and Neutrophils are potential targets to reduce allergic airway inflammation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".