Bronchiolectasis of respiratory bronchioles leads to honeycomb cyst formation within the IPF lung
Bibliographic record
Abstract
Background: Idiopathic pulmonary fibrosis (IPF) is a prevalent interstitial lung disease marked by honeycomb cysts formation, lung scarring, and progressive respiratory failure. Aims: Determine the structural and inflammatory cells driving honeycombing in IPF. Method: Eight control and eight IPF lungs were inflated, frozen, and sampled using systematic uniform random sampling for micro-CT imaging to assess the 3-dimensional pathology of honeycombing. Samples were then formalin-fixed, paraffin-embedded, and respiratory bronchiole locations on the micro-CT scan were mapped to histological sections using image registration. Tissue sections were assessed for structural and immune markers. Result: In fibrotic IPF samples (increased volume fraction of tissue), the respiratory bronchioles exhibited bronchiolectasis with increased inner lumen area, branch length and roughness compared to control samples (P<0.0001). Image registration of micro-CT to histology revealed the respiratory bronchioles with bronchiolectasis formed honeycomb cysts in IPF lungs with increased MUC5AC and MUC5B expression, positive staining for CD4, CD8 T cells, B cells and M2-like macrophages which formed aggregations, compared to respiratory bronchioles without (P<0.0001). Neutrophils were reduced in all IPF respiratory bronchioles compared to control lungs (P<0.001). Conclusion: Bronchiolectatic respiratory bronchioles form honeycomb cyst formation in IPF lungs, are associated with increased mucus production, and infiltration of CD4 and CD8 T cells, B cells, and M2-like macrophage aggregates. These data reveal an active immune response in IPF without neutrophilic infiltration, highlighting potential therapeutic targets.
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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 | 0.000 | 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".