Mono-allergic and poly-allergic rhinitis patients have comparable numbers of mucosal Foxp3+CD4+ T lymphocytes
Bibliographic record
Abstract
BACKGROUND: We previously found that allergic rhinitis patients with an isolated pollen sensitization responded more strongly to a nasal provocation with grass pollen (GP) than patients who had an additional house dust mite (HDM) sensitization. To elucidate this phenomenon, we investigated the dynamics of Foxp3+CD4+ T lymphocytes in allergic rhinitis patients with distinct allergen sensitizations. METHODS: Three groups of allergic rhinitis patients with skin prick test confirmed allergic sensitizations were investigated and compared to 14 healthy controls: 14 subjects with an isolated grass pollen sensitization (Mono-GP); 9 subjects with isolated housedust mite sensitization (Mono-HDM); 29 subjects with grass pollen and house dust mite sensitization (poly-sensitized). Subjects in the Mono-GP group were challenged with grass pollen extract, subjects in the Mono-HDM group were challenged with house dust mite extract, subjects in the poly-sensitized group and the healthy controls were randomly challenged with either grass pollen or house dust mite. Nasal biopsies were taken before and after nasal provocation. We compared the distribution of FoxP3+CD4+ cells in nasal biopsies before and after nasal provocation using immunohistochemistry. RESULTS: There was no difference in the number of FoxP3+CD4+ cells between healthy and the three allergic groups at baseline.Nasal provocation did result in an increase in eosinophils in the three allergic groups, but did not result in a change in the number of FoxP3+CD4+ cells in any of the groups or induced differences between any of the groups. CONCLUSION: Clinical differences in the response between mono-GP and multiple-sensitized allergic individuals are not related to differences in the number of regulatory T cells in the nasal mucosa.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".