Informed target discovery for gene and stem cell therapy in acute lung injury
Bibliographic record
Abstract
Introduction Acute lung injury (ALI)/sepsis‐induced acute respiratory distress syndrome (ARDS) accounts for 9% ICU deaths. There is an urgent need for specific treatments and mesenchymal stem cells (MSC) have reparative potential in sepsis and ALI. Methods In the murine model of CLP‐induced ARDS, microarray detected changes in global gene expression following sepsis and MSC treatment. LIMMA analysis identified mRNA and miRNA with significant transcriptional changes between sham, CLP‐operated, and CLP+MSC treated mice. Results We confirmed our in silico data in our in vitro model. Adhesion molecules, occludin and claudin‐2, were putative targets of one miRNA of interest. In silico, occludin was reduced after CLP compared with sham, confirmed by western blots (in vivo and in vitro), and there was a 50% mRNA reduction. Similarly, claudin‐2 expression from the in silico data matched the protein and mRNA analyses (in vivo and in vitro). Subsequently, following transfection of the inhibitor of the miRNA of interest, occludin and claudin‐2′s mRNA levels were observed to be significantly up‐regulated. Conclusion mRNA and miRNA microarrays are informative in identifying candidate therapeutic tools for gene regulation in ARDS treatment. MSC administration after CLP in the murine model acts as a strategy that offers great insight in the detection of therapeutically relevant genes to treat ARDS/ALI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".