Clinical characterization of allergic sensitization patterns and the role of mucosal dendritic cells
Bibliographic record
Abstract
In this thesis we describe differences in clinical response to allergen challenges in allergic rhinitis subjects with different sensitizations. We investigated multiple steps in the allergic cascade to find an explanation for this difference in clinical response. Furthermore, the role of dendritic cells is discussed, with emphasis on an appropriate balance between pro-inflammatory versus tolerogenic effects of different dendritic cell (DC) subsets. Numbers of myeloid DCs (mDCs), plasmacytoid DCs (pDCs) and Langerhans type DCs (LDCs) in nasal mucosa differ between subjects with different allergic sensitizations. The mDC/pDC ratio is equal for allergic and healthy subjects, however after nasal allergen provocation the ratio in healthy subjects decreases, while remaining the same in allergic subjects, suggesting lack of immunosuppression in allergic subjects. Tissue distribution of mDCs, pDCs and LDCs in human oral mucosa shows significantly more mDCs and LDCs in oral mucosa in allergic subjects compared to healthy controls. Given the complexity and heterogeneity in function of dendritic cells, and flexibility of DC subets in expression of function, we tried to gain more insight in their role by studying distribution of DC subsets in several models of immunological dysfunction. We analyzed dendritic cells in nasal mucosa of IgA Nephropathy patients, in pulmonary mucosa of chronic obstructive pulmonary disease (COPD) patients, and in intestinal mucosa and mesenteric lymph nodes of patients with Crohn’s disease and non-IBD patients. A disbalance in DC subsets seems to play a role in the pathophysiology of these diseases, even after cessation of the initial stimulus leading to the disbalance.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".