Noninjurious mechanical ventilation activates a proinflammatory transcriptional program in the lung
Bibliographic record
Abstract
Mechanical ventilation is a life-saving intervention in patients with respiratory failure. However, human and animal studies have demonstrated that mechanical ventilation using large tidal volumes (>or=12 ml/kg) induces a potent inflammatory response and can cause acute lung injury. We hypothesized that mechanical ventilation with a "noninjurious" tidal volume of 10 ml/kg would still activate a transcriptional program that places the lung at risk for severe injury. To identify key regulators of this transcriptional response, we integrated gene expression data obtained from whole lungs of spontaneously breathing mice and mechanically ventilated mice with computational network analysis. Topological analysis of the gene product interaction network identified Jun and Fos families of proteins as potential regulatory hubs. Electrophoretic mobility gel shift assay confirmed protein binding to activator protein-1 (AP-1) consensus sequences, and supershift experiments identified JunD and FosB as components of ventilation-induced AP-1 binding. Specific recruitment of JunD to the regulatory region of the F3 gene by mechanical ventilation was confirmed by chromatin immunoprecipitation assay. In conclusion, we demonstrate a novel computational framework to systematically dissect transcriptional programs activated by mechanical ventilation in the lung, and show that noninjurious mechanical ventilation initiates a response that can prime the lung for injury from a subsequent insult.
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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) | 0.000 | 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".