Local Induction of a Specific Th1 Immune Response by Allergen Linked Immunostimulatory DNA in the Nasal Explants of Ragweed- Allergic Subjects
Bibliographic record
Abstract
BACKGROUND: Allergen immunotherapy is effective in allergic individuals however efforts are being made to improve its safety, convenience, and efficacy. It has recently been demonstrated that allergen-linked immunostimulatory DNA (ISS) is effective in stimulating an allergen-specific Th1 response with decreased allergenicity. The objective of this study is to investigate whether ISS linked to purified ragweed allergen Amb-a-1 (AIC) can inhibit local allergen-specific Th2 and induce allergen-specific Th1 responses in explanted nasal mucosa of ragweed-sensitive subjects. In addition, we set out to determine whether AIC is more effective compared to stimulation with unlinked Amb a 1 and ISS. METHODS: Tissue from ragweed-sensitive patients (n = 12) was cultured with whole ragweed allergen (RW), Amb-a-1, AIC, Amb-a-1 and ISS (unlinked), or tetanus toxoid (TT) for 24 hours. IL-4, -5, -13, TNF-alpha and IFN-gamma mRNA-positive cells were visualized by in situ hybridization and T cells, B cells and neutrophils were enumerated using immunocytochemistry. RESULTS: RW or Amb-a-1 increased the number of IL-4, IL-5, and IL-13 mRNA+ cells in the tissue compared to medium alone. AIC had similar cytokine mRNA reactivity as control tissue. AIC and TT increased IFNgamma-mRNA expression. Unlinked Amb-a-1 and ISS showed similar effects to AIC, however this response was weaker. The number of TNF mRNA+ cells, T cells, B cells and neutrophils remained unchanged. CONCLUSIONS: AIC is effective in stimulating a local allergen-specific Th1- and abolishing Th2-cytokine mRNA reactivity in the nose and may be considered as a strong candidate for an improved approach to immunotherapy in ragweed-sensitive individuals.
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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 | 0.001 |
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".