<p>Air Pollution and Allergic Rhinitis: Role in Symptom Exacerbation and Strategies for Management</p>
Bibliographic record
Abstract
This article reviews the current understanding of the role of air pollution in both the symptom exacerbation and rising prevalence of allergic rhinitis (AR) for the development of future AR therapeutics and management strategies. We discuss the epidemiological evidence for this relationship through birth cohort studies, the economic impact of AR, and the influence of air pollution through the lens of the exposome framework of allergic disease development. This is followed by a discussion on the influence of diesel exhaust and diesel exhaust particles (DEP) from motor vehicle emissions and their implication in the rising prevalence of allergic disease and allergic sensitization through triggering inflammatory signalling pathways that exacerbate AR symptoms. Finally, a summary is provided of clinical trials assessing the influence of air pollution on AR with a depiction of currently available therapies and management strategies. Future directions in the development of AR modalities given the air pollution-mediated symptom exacerbation are challenged with unfolding the complex gene-environment interaction product of heterogenous AR presentation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".