Endothelial apoptosis promotes lung tissue remodeling via activation of matrix metalloprotease
Bibliographic record
Abstract
Endothelial apoptosis has been implicated in abnormal lung tissue remodeling. Here, we tested the hypothesis that lung endothelial apoptosis, via formation of reactive oxygen species (ROS), may trigger matrix metalloprotease (MMP) activation, which in turn propagates lung disease by promoting further apoptosis and tissue remodeling. In isolated perfused mouse lungs, programmed endothelial cell death was induced by a pro-apoptotic peptide and verified by real-time imaging of annexin-V, mitochondrial depolarization and the FLICA™ assay for caspase activation. Endothelial apoptosis was associated with enhanced ROS formation, as shown by imaging of H2DCF-loaded cells, and increased pericellular MMP activity, as demonstrated by imaging of DQ-gelatin which becomes unquenched upon cleavage by MMPs. Inhibition of apoptosis by the caspase inhibitor Z-Asp-CH2-DCB attenuated MMP activation. Conversely, the MMP inhibitor GM6001 and the ROS scavenger N-acetyl-l-cysteine each attenuated endothelial apoptosis. Our data suggest an intricate interplay between endothelial apoptosis and MMP activation that is regulated by ROS. The evolving positive feedback may critically promote lung tissue remodeling. Grant Funding Source: Canadian Institutes of Health Research (CIHR), Heart and Stroke Foundation of Canada (HSFC), German Research Foundation (DFG)
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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 | 0.001 |
| 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".