Combined immune and airway epithelial cell profiles define asthma severity phenotypes
Bibliographic record
Abstract
Abstract Asthma constitutes a spectrum of conditions that affect a substantial proportion of the population in developed countries. A large segment of asthmatics are characterized by an immunologic type 2 allergic profile. A preponderance of conventional and biologically-based therapies are available to treat these subjects. However, a small minority of individuals clinically classified as severe asthmatics (SA), are highly refractory to treatment and account for a significantly disproportionate share of the clinical and economic impact of the asthma condition as a whole. In order to better define the immune response in SA with the ultimate goal of developing novel therapeutic modalities, we analyzed bronchoalveolar lavage (BAL) cells from a cohort of SA, mild to moderate asthmatics (M/M), and healthy controls by mass cytometry (CyTOF). Unbiased bioinformatic assessment revealed 34 clusters of BAL cells with assignment of subjects to one of four groupings by principal component analysis (PCA). The majority of SA (78.9%) fell into two of these groups and constituted 65.2% of their makeup, with M/M representing the remaining 34.8%. Similarly, analysis of airway bronchial epithelial cells (BECs) by RNA sequencing (RNASeq) revealed three subject groups with SA falling largely into one of these. Synthesis of CyTOF and RNASeq data in the context of relevant clinical parameters resulted in a combined profile for each subject, which was able to distinguish those with the most severe disease. Our approach demonstrates that an unbiased, multifactorial assessment of immune and epithelial profiles can predict asthma severity, but may also reveal mechanism-based clues for targeted, personalized therapeutic intervention in severe asthmatics.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".