Bibliographic record
Abstract
Asthma is a common chronic respiratory disease where patients suffer from restricted airways and airway inflammation (mainly eosinophilic type 2 (T2) inflammation). Inhaled (ICS)/oral corticosteroids (OCS) and, more recently T2-targeting biologics, are prescribed as mainstay therapies for asthma. Despite these therapies, a subset of asthma patients continues to have symptoms and airway inflammation, suggesting an underlying additional asthma pathology. We have observed multiple airway autoantibodies in 55% of moderate-to-severe asthma patients associated with inadequate response to corticosteroids and anti-T2 biologics. Increased airway degranulation (eosinophilic and/or infective) together with lymphopenia (low lymphocyte counts) underlie these self-reactive/autoimmune-like events. In healthy individuals, regulatory lymphocytes limit the development and activity of self-reactive cells, including those capable of producing autoantibodies. Lymphopenia can lead to skewed non-regulatory to regulatory lymphocyte subsets that support a microenvironment with reduced ability to limit self-reactivity. In this study, wanted to measure B cell subsets by flow cytometry and non-regulatory to regulatory B cell ratios in asthma patients and healthy controls. To understand B cell compartmentalization, we analyzed peripheral and sputum B cells. We identified decreased regulatory B cells (Bregs), in particular CD5+ Bregs, and skewed non-regulatory to regulatory B cell ratios in both circulation and airways of asthma patients. Compared to healthy controls, only patients requiring daily OCS had significantly lower CD5+ Bregs, suggesting a reduced regulatory component in more severe patients. Further, CD5+ Bregs, capable of producing immunomodulatory interleukin-10, were significantly lower in patients with a history of multiple lymphopenic events (70% requiring daily OCS). Together, this supports the need to investigate lymphopenia-induced dysregulation of Bregs, increase in autoreactive B cells and airway autoreactivities, and subsequent progression into a more-severe therapy refractory autoimmune pathology. This opens a new avenue for asthma treatment, particularly for the severe population with airway autoimmune responses often not targeted/controlled by current anti-inflammatory therapies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".