Prediction of lung overdistension during mechanical ventilation using micro-RNA and gene expression
Bibliographic record
Abstract
BACKGROUND: Overstretching of lung parenchyma may lead to injury, especially during mechanical ventilation. To date, there are no specific biomarkers of lung stretch, but transcriptomic signatures have not been explored. Our objective was to identify stretch-specific signatures using micro-RNA and gene expression. METHODS: Data on micro-RNA and RNA expression in response to stretch in experimental models were systematically pooled. Signatures were identified as those micro-RNAs or genes with differential expression in samples from stretched cells or tissues, and optimized using a greedy algorithm. Expression data was used to calculate transcriptomic scores. The accuracy of these scores was validated in animal models of lung injury, ex vivo mechanically ventilated human lungs, and bronchoalveolar lavage fluid (BALF, n = 7) and in serum samples (n = 31) of mechanically ventilated patients. RESULTS: Six micro-RNAs (mir-383, mir-877, mir-130b; mir-146b, mir-181b, and mir-26b) were differentially expressed in stretched cell cultures (n = 24). Amongst the genes regulated by these micro-RNAs, a 451-gene signature was identified in vitro (n = 106) and refined using data from animal models (n = 143) to obtain a 6-gene signature (Lims1, Atp6v1c1, Dedd, Bclb7, Ppp1r2 and F3). Transcriptomic scores were significantly higher in samples submitted to stretch or injurious mechanical ventilation. The microRNA and RNA signatures were validated in human tissue, BALF and serum, with areas under the ROC curve between 0.7 and 1 to identify lung overdistention. CONCLUSIONS: Lung cell stretch induces the expression of specific micro-RNA and genes. The potential of these signatures to identify lung stretch in a clinical setting must be explored.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".