Bibliographic record
Abstract
Asthma and autoimmune diseases both result from a dysregulated immune system, and have been conventionally considered to have mutually exclusive pathogenesis. Autoimmunity is believed to be an exaggerated Th1 response, while asthma with a Th2 underpinning is congruent with the well-accepted Th1/Th2 paradigm. The hypothesis of autoimmune involvement in asthma has received much recent interest, particularly in the adult late-onset non-atopic patients (the "intrinsic asthma"). Over the past decades, circulating autoantibodies against diverse self-targets (beta-2-adrenergic receptors, epithelial antigens, nuclear antigens, etc.) have been reported and subsequently dismissed to be epiphenomena resulting from a chronic inflammatory condition, primarily due to lack of evidence of causality/pathomechanism. Recent evidence of 'granulomas' in the lung biopsies of severe asthmatics, detection of pathogenic sputum autoantibodies against autologous eosinophil proteins (e.g., eosinophil peroxidase) and inadequate response to monoclonal antibody therapies (e.g., subcutaneous mepolizumab) in patients with evidence of airway autoantibodies suggest that the role of autoimmune mechanisms be revisited. In this review, we have gathered available reports of autoimmune responses in the lungs, reviewed the evidence in the context of immunogenic tissue-response and danger-associated molecular patterns, and constructed the possibility of an autoimmune-associated pathomechanism that may contribute to the severity of 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".