Pathogenesis of severe asthma
Bibliographic record
Abstract
Patients with severe asthma have asthma symptoms which are difficult to control, require high dosages of medication, and continue to experience persistent symptoms, asthma exacerbations or airflow obstruction. Epidemiological and clinical evidences point to the fact that severe asthma is not a single phenotype. Cluster analyses have identified subclasses of severe asthma using parameters such as patient characteristics, and cytokine profiles have also been useful in classifying moderate and severe asthma. The IL-4/IL-13 signalling pathway accounts for the symptoms experienced by a subset of severe asthmatics with allergen-associated symptoms and high serum immunoglobulin E (IgE) levels, and these patients are generally responsive to anti-IgE treatment. The IL-5/IL-33 signalling pathway is likely to play a key role in the disease pathogenesis of those who are resistant to high doses of inhaled corticosteroid but responsive to systemic corticosteroids and anti-IL5 therapy. The IL-17 signalling pathway is thought to contribute to 'neutrophilic asthma'. Although traditionally viewed as players in the defence mechanism against viral and intracellular bacterial infection, mounting evidence supports a role for Th1 cytokines such as IL-18 and IFN-γ in severe asthma pathogenesis. Furthermore, these cytokine signalling pathways interact to contribute to the spectrum of clinical pathological outcomes in severe asthma. To date, glucocorticoids are the most effective anti-asthma drugs available, yet severe asthma patients are typically resistant to the effects of glucocorticoids. Glucocorticoid receptor dysfunction and histone deacetylase activity reduction are likely to contribute to glucocorticoid resistance in severe asthma patients. This review discusses recent development in different cytokine signalling pathways, their interactions and steroid resistance, in the context of severe asthma pathogenesis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".