Imbalance of Pulmonary Microvascular Endothelial Cell-Expression of Metalloproteinases and Their Endogenous Inhibitors Promotes Septic Barrier Dysfunction
Bibliographic record
Abstract
Sepsis is a life-threatening disease characterized by excessive inflammation leading to organ dysfunction. During sepsis, pulmonary microvascular endothelial cells (PMVEC) lose barrier function associated with inter-PMVEC junction disruption. Matrix metalloproteinases (MMP) and a disintegrin and metalloproteinases (ADAM), which are regulated by tissue inhibitors of metalloproteinases (TIMPs), can cleave cell–cell junctional proteins, suggesting a role in PMVEC barrier dysfunction. We hypothesize that septic PMVEC barrier dysfunction is due to a disruption in the balance between PMVEC-specific metalloproteinases and TIMPs leading to increased metalloproteinase activity. The effects of sepsis on TIMPs and metalloproteinases were assessed ex vivo in PMVEC from healthy (sham) and septic (cecal ligation and perforation) mice, as well as in vitro in isolated PMVEC stimulated with cytomix, lipopolysaccharide (LPS), and cytomix + LPS vs. PBS. PMVEC had high basal Timp expression and lower metalloproteinase expression, and septic stimulation shifted expression in favour of metalloproteinases. Septic stimulation increased MMP13 and ADAM17 activity associated with a loss of inter-PMVEC junctional proteins and barrier dysfunction, which was rescued by treatment with metalloproteinase inhibitors. Collectively, our studies support a role for metalloproteinase–TIMP imbalance in septic PMVEC barrier dysfunction, and suggest that inhibition of specific metalloproteinases may be a therapeutic avenue for septic patients.
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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".