Regulation of Allergy with RNA Interference
Bibliographic record
Abstract
Allergic diseases such as asthma, allergic rhinitis, allergic conjunctivitis, and atopic dermatitis are clinically challenging. Although current treatments such as antihistamines, leukotriene receptor antagonists, and corticosteroids are effective at reducing symptoms, they do not address the underlying cause of the allergic response. Therefore, novel therapies that target upstream causative events in allergic diseases are desirable. The induction of RNA interference (RNAi) by small interfering RNA (siRNA) is a potent method for specifically knocking down molecular targets. Gene modulation by siRNA is therapeutically promising, with clinical safety and feasibility already demonstrated. However, to our knowledge, the use of siRNA in the area of allergic disease has been limited. Recently, we demonstrated the inhibition of CD40 by siRNA as a means of inhibiting allergic reactions. RNAi-based therapies represent a novel and promising strategy for the control of both the symptoms of allergy and the cause of the allergic response. Here we discuss the potential of siRNA in the treatment of allergic diseases by focusing on molecular and cellular interactions involved in the allergic cascade.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".