IL-8, IL-10, TGF-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi mathvariant="bold-italic">β</mml:mi></mml:mrow></mml:math>, and GCSF Levels Were Increased in Severe Persistent Allergic Asthma Patients with the Anti-IgE Treatment
Bibliographic record
Abstract
BACKGROUND: Allergic asthma is showed an increase in Th2-cytokine and IgE levels and an accumulation activation of Th2 cells, eosinophils and mast cells. However, recent studies focused on cell-based mechanisms for the pathogenesis of allergic asthma. Objectives. In this study, we compare the anti-IgE treatment modality in the dynamics of immune system cytokine levels in severe persistent asthma (SPA) patients who had no other any allergic disease, newly diagnosed allergic asthma patients and healthy volunteers. STUDY DESIGN: The study population consisted of 14 SPA patients, 14 newly diagnosed allergic asthma patients and 14 healthy volunteers included as controls. Cytokine levels were measured. Total and specific IgE levels of anti-IgE monoclonal antibody treated patients, serum high-sensitivity C-reactive protein (hsCRP) levels, FEV1/FVC rates and asthma control test (ACT) were measured for the clinical follow-up. RESULTS: We observed that SPA patients presented increasing levels of IL-8, IL-10, TGF-β and GCSF during the anti-IgE treatment in period of sampling times at 4 months and 18 months. However this increase was not correlated neither with serum hsCRP levels nor FEV1/FVC rates. CONCLUSIONS: Our study gives a different perspective for the SPA and anti-IgE immunotherapy efficacy at the cell cytokine-linked step.
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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 | 0.000 | 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) | 0.002 | 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".