Persistent and progressive acute lung allograft dysfunction is linked to cell compositional and transcriptional changes in small airways
Bibliographic record
Abstract
BACKGROUND: ) decline concerning for chronic lung allograft dysfunction (CLAD) onset. Novel diagnostic tools are needed to identify those with ALAD who will progress to CLAD and to target appropriate therapies. We hypothesized that progressive ALAD would be associated with changes in small airway cell composition and cell-specific transcription. METHODS: decline. Cell compositional changes, pseudobulk Reactome pathways, and the AI2 score, previously linked to CLAD in airway brush transcriptomes, were assessed as a function of ALAD outcome group. RESULTS: Across 68,140 cells, the distribution of cell composition was linked to ALAD outcome group (PERMANOVA, p = 0.004). Worse ALAD outcomes correlated with loss of basal cells, changes in club and ciliated subsets, a loss of macrophages, and expansion of cytotoxic T cells. The AI2 gene score was positively associated with ALAD outcome group, particularly in epithelial cell subsets (p < 0.001). Pathway analysis showed increased interferon signaling and inhibition of cell proliferation in epithelial cells. CONCLUSIONS: In this pilot study, persistent and progressive ALAD was associated with changes in bronchiolar cell composition and transcriptional programs. Molecular phenotyping may help identify and characterize individuals with ALAD at increased risk for progression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".