Allergic rhinitis and genetic components: focus on Toll-like receptors (TLRs) gene polymorphism
Bibliographic record
Abstract
Zhiwei Gao1, Donna C Rennie2, Ambikaipakan Senthilselvan11Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada; 2College of Nursing and Canadian Centre for Health and Agricultural Safety, University of Saskatchewan, Saskatoon, Saskatchewan, CanadaAbstract: Allergic rhinitis represents a global health issue affecting 10% to 25% of the population worldwide. Over the years, studies have found that allergic diseases, including allergic rhinitis, are associated with immunological responses to antigens driven by a Th2-mediated immune response. Because Toll-like receptors (TLRs) are involved in both innate and adaptive immune responses to a broad variety of antigens, the association between polymorphisms of TLRs and allergic diseases has been the focus in many animal and human studies. Although the etiology of allergic rhinitis is still unknown, extensive research over the years has confirmed that the underlying causes of allergic diseases are due to many genetic and environmental factors, along with the interactions among them, which include gene–environment, gene–gene, and environment–environment interactions. Currently, there is great inconsistency among studies mainly due to differences in genetic background and unique gene–environment interactions. This paper reviews studies focusing on the association between TLR polymorphisms and allergic diseases, including allergic rhinitis, which would help researchers better understand the role of TLR polymorphisms in the development of allergic rhinitis, and ultimately lead to more efficient therapeutic interventions being developed.Keywords: allergic rhinitis, allergic diseases, Toll-like receptors
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".