Bone marrow-derived progenitor cells in end-stage lung disease patients
Bibliographic record
Abstract
BACKGROUND: Chronic lung diseases are marked by progressive inflammation, tissue damage and remodelling. Bone marrow-derived progenitor cells may contribute to these processes. The objectives of this study were to (1) to quantify CD45⁺Collagen-1⁺ fibrocytes and a novel epithelial-like population of bone marrow-derived cells, which express Clara Cell Secretory Protein, in patients at the time of lung transplant and (2) to evaluate mediators that may act to recruit these cells during injury. METHODS: Using an observational design, progenitor cells were quantified by flow cytometry from both bone marrow (BM) and peripheral blood (PB). Migration was tested using in vitro transwell assays. Multiplex bead-based assays were used to quantify plasma cytokines. RESULTS: An increase in CD45⁺Collagen-1⁺ fibrocytes was found in pulmonary fibrosis and bronchiolitis obliterans patients. Cystic fibrosis patients had an increase in CCSP⁺ cells in both the BM and PB. The proportion of CCSP⁺ cells in the BM and PB was correlated. CCSP+ cells express the chemokine receptors CCR2, CCR4, CXCR3, and CXCR4, and significantly migrated in vitro toward Stromal Derived Factor-1 (SDF-1) and Stem Cell Growth Factor-β (SCGF-β). Plasma cytokine levels differed between disease groups, with a significant correlation between SCGF-β and CCSP⁺ cells and between Monocyte Chemotactic Protein-1 and fibrocytes. CONCLUSIONS: Different bone marrow-derived cells are found in various lung diseases. Increased fibrocytes were associated with fibrotic lung diseases. An increase in the novel CCSP⁺ epithelial-like progenitors in cystic fibrosis patients was found. These differences may be mediated by alterations in plasma cytokines responsible for cell recruitment.
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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 | 0.001 | 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) | 0.001 | 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".