Human fetal lung mesenchymal stem cells ameliorate lung injury in an animal model
Bibliographic record
Abstract
Acute lung injury (ALI) is a critical condition with limited treatment options. This study evaluates the therapeutic potential of human fetal lung-derived mesenchymal stem cells (hFL-MSCs) in an experimental model of ALI. Our proof-of-concept findings suggest a paradigm shift in the approach to cell sourcing for lung diseases, proposing that fetal lung cells may be potential targets for stem cell differentiation studies when the derived cells are intended to be used for lung cell therapy. After characterizing hFL-MSCs, 18-week fetal lung cells were intratracheally instilled into rats with bleomycin-induced ALI. All the animals were evaluated on days 3 - 28 post-injury for cell count and the cytokines in bronchoalveolar lavage fluid (BALF), lung wet/dry weight ratio, lung tissue histological staining and expression of an extracellular matrix component, inflammatory and fibrotic genes. The findings confirm mesenchymal stem cell identity of the isolated cells and stability in their cell cycle distribution. Analysis of BALF showed that immune cell response to acute inflammation and adaptive immunity was significantly ameliorated by cell therapy with hFL-MSCs. Same results were confirmed by the levels of IL-6, TNF-α, IL-10 and NO in BALF, the lung wet/dry weight ratio and histopathological analysis of lung tissues after H&E and Masson's trichrome staining. Effective modulation of key pro-inflammatory (Il6, Tnf, Il1b), pro-fibrotic (Tgfb1) and Col1a1 genes were also confirmed after therapy with hFL-MSCs. Our findings suggest that fetal lung tissue-specific stem cells are viable options for lung cell therapy and could be considered as targets for engineering of regenerative cells for lung diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".