Pulmonary Fibrosis After COVID-19 is Characterized by Airway Abnormalities and Persistently Elevated CC16
Bibliographic record
Abstract
Abstract Introduction/Rationale: There are no known serum biomarkers that provide mechanistic insight or prognostic enrichment for post-COVID-19 pulmonary fibrosis. Methods: We tested associations of 18 serum biomarkers of inflammation, aging, endothelial activation, pulmonary epithelial function, fibrosis, and fibrinolysis with fibrotic patterns (reticulation, traction bronchiectasis, or honeycombing) on thoracic CT scans 4-months, 15-months, and 3-years post-hospitalization in a New York City-based discovery cohort of severe-to-critical COVID-19 survivors, and externally validated findings in two Canadian cohorts of moderate-to-severe COVID-19 survivors. In the discovery cohort, we radiographically validated a dose-response effect of the biomarker with airway-to-lung ratio. We discovered the pulmonary source of the identified biomarker via single-cell RNA sequencing (scRNAseq) of COVID-19 survivor transbronchial lung biopsies obtained 4-years after COVID-19 hospitalization and immunofluorescent analysis of COVID-19 lung explants. Findings: Among 150 discovery cohort participants, only higher levels of circulating club-cell secretory protein-16 (CC16) at hospital discharge, 4-months, 15-months, and 3-years were associated with thoracic CT fibrotic patterns (Fig. 1A). Higher CC16 levels were associated with thoracic CT fibrotic patterns in two validation cohorts (n=56 and n=37) (Fig. 1B and C). CC16 levels were linearly associated with larger airway-to-lung ratio. scRNAseq revealed increased SCGB1A1 expression (gene encoding CC16) in epithelial cells in COVID-19 survivors with fibrosis (Fig. 1D), and immunofluorescence demonstrated 3-fold more CC16/MUC5B co-expressing cells in respiratory bronchioles of COVID-19 lung explants with fibrosis (Fig. 1E and F). Conclusion: Higher CC16 levels are consistently associated with CT fibrotic patterns for up to 3-years among adult survivors of moderate-to-critical COVID-19. Elevated CC16 reflects dysregulated airway epithelial progenitor cell remodeling and increased CC16/MUC5B pro-fibrotic signaling in respiratory bronchioles.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".