The ARDS microenvironment enhances MSC-induced repair via VEGF in experimental acute lung inflammation
Bibliographic record
Abstract
Clinical trials investigating the potential of mesenchymal stromal cells (MSCs) for the treatment of inflammatory diseases, such as acute respiratory distress syndrome (ARDS), have been disappointing, with less than 50% of patients responding to treatment.Licensed MSCs show enhanced therapeutic efficacy in response to cytokine-mediated activation signals.There are two distinct sub-phenotypes of ARDS: hypo-and hyper-inflammatory.We hypothesized that pre-licensing MSCs in a hyper-inflammatory ARDS environment would enhance their therapeutic efficacy in acute lung inflammation (ALI).Serum samples from patients with ARDS were segregated into hypoand hyper-inflammatory categories based on interleukin (IL)-6 levels.MSCs were licensed with pooled serum from patients with hypo-or hyper-inflammatory ARDS or healthy serum controls.Our findings show that hyper-inflammatory ARDS pre-licensed MSC conditioned medium (MSC-CM Hyper ) led to a significant enrichment in tight junction expression and enhanced barrier integrity in lung epithelial cells in vitro and in vivo in a vascular endothelial growth factor (VEGF)-dependent manner.Importantly, while both MSC-CM Hypo and MSC-CM Hyper significantly reduced IL-6 and tumor necrosis factor alpha (TNF-a) levels in the bronchoalveolar lavage fluid (BALF) of lipopolysaccharide (LPS)-induced ALI mice, only MSC-CM Hyper significantly reduced lung permeability and overall clinical outcomes including weight loss and clinical score.Thus, the hypo-and hyper-inflammatory ARDS environments may differentially influence MSC cytoprotective and immunomodulatory functions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".