Small Airway Reduction and Fibrosis Is an Early Pathologic Feature of Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
Abstract Rationale To improve disease outcomes in idiopathic pulmonary fibrosis (IPF), it is essential to understand its early pathophysiology so that it can be targeted therapeutically. Objectives Perform three-dimensional assessment of the IPF lung microstructure using stereology and multiresolution computed tomography (CT) imaging. Methods Explanted lungs from patients with IPF (n = 8) and donor control subjects (n = 8) were inflated with air and frozen. CT scans were used to assess large airways. Unbiased, systematic uniform random samples (n = 8/lung) were scanned with microCT for stereological assessment of small airways (count number, and measure airway wall and lumen area) and parenchymal fibrosis (volume fraction of tissue, alveolar surface area, and septal wall thickness). Measurements and Main Results The total number of airways on clinical CT was greater in IPF lungs than control lungs (P < 0.01), owing to an increase in the wall (P < 0.05) and lumen area (P < 0.05) resulting in more visible airways with a lumen larger than 2 mm. In IPF tissue samples without microscopic fibrosis, assessed by the volume fraction of tissue using microCT, there was a reduction in the number of the terminal (P < 0.01) and transitional (P < 0.001) bronchioles, and an increase in terminal bronchiole wall area (P < 0.001) compared with control lungs. In IPF tissue samples with microscopic parenchymal fibrosis, terminal bronchioles had increased airway wall thickness (P < 0.05) and dilated airway lumens (P < 0.001) leading to honeycomb cyst formations. Conclusions This study has important implications for the current thinking on how the lung tissue is remodeled in IPF and highlights small airways as a potential target to modify IPF outcomes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".