The hyper-inflammatory SARS-CoV-2-induced ARDS patient micro-environment licenses MSCs and enhances their therapeutic efficacy in a model of acute lung injury
Bibliographic record
Abstract
Background: Clinical trials investigating the potential of mesenchymal stromal cells (MSCs) in acute respiratory distress syndrome (ARDS) have provided disappointing results. MSCs are known to require cytokine-mediated activation signals, or licensing, in order to mediate protective effects in vivo and therefore MSCs may be more efficacious in the hyper-inflammatory ARDS sub-phenotype. We investigated the therapeutic efficacy of MSCs licensed with differential ARDS patient micro-environments (hyper- vs hypo-inflammatory) in a model of acute lung injury (ALI). Methods: MSCs were exposed to 20% patient serum from SARS-CoV-2-induced ARDS patients for 24 hours. ARDS patient serum was segregated into hypo- or hyper-inflammatory phenotype based on IL-6 levels. The MSCs and their secretome were then screened both in vitro and in vivo. Results: The secretome of MSCs exposed to the hyper- but not hypo-inflammatory ARDS serum reduced LPS-induced lung permeability, significantly increasing expression of tight junction genes occludin, claudin-4 and zo-1 in the lung epithelium in a VEGF dependent manner. Conclusion: MSCs exposed to hyper, but not hypo, ARDS patient serum have the capacity to reduce lung permeability, due to enhanced tight junction formation, in a VEGF-dependent manner. Disclosures: Conflict of interest: The authors declare there is no conflict of interest. Funding: This project has been supported by the Science Foundation Ireland Award to Prof. Karen English under the grant number 20/FFP-A/8948 and the National Irish COVID Biobank.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".