Mechanisms and treatments for severe, steroid‐resistant allergic airway disease and asthma
Bibliographic record
Abstract
Severe, steroid-resistant asthma is clinically and economically important since affected individuals do not respond to mainstay corticosteroid treatments for asthma. Patients with this disease experience more frequent exacerbations of asthma, are more likely to be hospitalized, and have a poorer quality of life. Effective therapies are urgently required, however, their development has been hampered by a lack of understanding of the pathological processes that underpin disease. A major obstacle to understanding the processes that drive severe, steroid-resistant asthma is that the several endotypes of the disease have been described that are characterized by different inflammatory and immunological phenotypes. This heterogeneity makes pinpointing processes that drive disease difficult in humans. Clinical studies strongly associate specific respiratory infections with severe, steroid-resistant asthma. In this review, we discuss key findings from our studies where we describe the development of representative experimental models to improve our understanding of the links between infection and severe, steroid-resistant forms of this disease. We also discuss their use in elucidating the mechanisms, and their potential for developing effective therapeutic strategies, for severe, steroid-resistant asthma. Finally, we highlight how the immune mechanisms and therapeutic targets we have identified may be applicable to obesity-or pollution-associated asthma.
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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) | 0.001 | 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 | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".