Tissue inhibitor of metalloproteinases 3-dependent microvascular endothelial cell barrier function is disrupted under septic conditions
Bibliographic record
Abstract
Sepsis is associated with dysfunction of microvascular endothelial cells (MVEC) leading to tissue edema and multiple organ dysfunction. Metalloproteinases can regulate MVEC function through processing of cell surface proteins, and tissue inhibitor of metalloproteinases 3 (TIMP3) regulates metalloproteinase activity in the lung following injury. We hypothesize that TIMP3 promotes normal pulmonary MVEC barrier function through inhibition of metalloproteinase activity. Naive Timp3(-/-) mice had significantly higher basal pulmonary microvascular Evans blue (EB) dye-labeled albumin leak vs. wild-type (WT) mice. Additionally, cecal-ligation/perforation (CLP)-induced sepsis significantly increased pulmonary microvascular EB-labeled albumin leak in WT but not Timp3(-/-) mice. Similarly, PBS-treated isolated MVEC monolayers from Timp3(-/-) mice displayed permeability barrier dysfunction vs. WT MVEC, evidenced by lower transendothelial electrical resistance and greater trans-MVEC flux of fluorescein-dextran and EB-albumin. Cytomix (equimolar interferon γ, tumor necrosis factor α, and interleukin 1β) treatment of WT MVEC induced significant barrier dysfunction (by all three methods), and was associated with a time-dependent decrease in TIMP3 mRNA and protein levels. Additionally, basal Timp3(-/-) MVEC barrier dysfunction was associated with disrupted MVEC surface VE-cadherin localization, and both barrier dysfunction and VE-cadherin localization were rescued by treatment with GM6001, a synthetic metalloproteinase inhibitor. TIMP3 promotes normal MVEC barrier function, at least partially, through inhibition of metalloproteinase-dependent disruption of adherens junctions, and septic downregulation of TIMP3 may contribute to septic MVEC barrier dysfunction.
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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".