Mechanical Stress and the Induction of Lung Fibrosis via the Midkine Signaling Pathway
Bibliographic record
Abstract
RATIONALE: Lung-protective ventilatory strategies have been widely used in patients with acute respiratory distress syndrome (ARDS), but the ARDS mortality rate remains unacceptably high and there is no proven pharmacologic therapy. OBJECTIVES: Mechanical ventilation can induce oxidative stress and lung fibrosis, which may contribute to high dependency on ventilator support and increased ARDS mortality. We hypothesized that the novel cytokine, midkine (MK), which can be up-regulated in oxidative stress, plays a key role in the pathogenesis of ARDS-associated lung fibrosis. METHODS: Blood samples were collected from 17 patients with ARDS and 10 healthy donors. Human lung epithelial cells were challenged with hydrogen chloride followed by mechanical stretch for 72 hours. Wild-type and MK gene-deficient (MK(-/-)) mice received two-hit injury of acid aspiration and mechanical ventilation, and were monitored for 14 days. MEASUREMENTS AND MAIN RESULTS: Plasma concentrations of MK were higher in patients with ARDS than in healthy volunteers. Exposure to mechanical stretch of lung epithelial cells led to an epithelial-mesenchymal transition profile associated with increased expression of angiotensin-converting enzyme, which was attenuated by silencing MK, its receptor Notch2, or NADP reduced oxidase 1. An increase in collagen deposition and hydroxyproline level and a decrease in lung tissue compliance seen in wild-type mice were largely attenuated in MK(-/-) mice. CONCLUSIONS: Mechanical stretch can induce an epithelial-mesenchymal transition phenotype mediated by the MK-Notch2-angiotensin-converting enzyme signaling pathway, contributing to lung remodeling. The MK pathway is a potential therapeutic target in the context of ARDS-associated lung fibrosis.
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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) | 0.001 | 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".