Healthy<i>versus</i>inflamed lung environments differentially affect mesenchymal stromal cells
Bibliographic record
Abstract
Background Despite increased interest in mesenchymal stromal cell (MSC)-based cell therapies for acute respiratory distress syndrome (ARDS), clinical investigations have not yet been successful and our understanding of the potential in vivo mechanisms of MSC actions in ARDS remains limited. ARDS is driven by an acute severe innate immune dysregulation, often characterised by inflammation, coagulation and cell injury. How this inflammatory microenvironment influences MSC functions remains to be determined. Aim The aim of this study was to comparatively assess how the inflammatory environment present in ARDS lungs versus the lung environment present in healthy volunteers alters MSC behaviour. Methods Clinical-grade human bone marrow-derived MSCs (hMSCs) were exposed to bronchoalveolar lavage fluid (BALF) samples obtained from ARDS patients or from healthy volunteers. Following exposure, hMSCs and their conditioned media were evaluated for a broad panel of relevant properties, including viability, levels of expression of inflammatory cytokines, gene expression, cell surface human leukocyte antigen expression, and activation of coagulation and complement pathways. Results Pro-inflammatory, pro-coagulant and major histocompatibility complex (self-recognition) related gene expression was markedly upregulated in hMSCs exposed ex vivo to BALF obtained from healthy volunteers. These changes were less apparent and often opposite in hMSCs exposed to ARDS BALF samples. Conclusion These data provide new insights into how hMSCs behave in healthy versus inflamed lung environments, and strongly suggest that the inflamed environment in ARDS induces hMSC responses that are potentially beneficial for cell survival and actions. This further highlights the need to understand how different disease environments affect hMSC functions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".