Quantification of adherens junction disruption and contiguous paracellular protein leak in human lung endothelial cells under septic conditions
Bibliographic record
Abstract
OBJECTIVE: Sepsis is associated with dysfunction of MVEC resulting in organ edema and inflammation. VE-cadherin, a component of MVEC adherens junctions, may be disrupted in sepsis. However, the direct connection between individual MVEC VE-cadherin disruption and increased paracellular permeability is uncertain. METHODS: Human pulmonary MVEC were cultured on a biotin matrix and treated with cytomix, as a model of sepsis, vs PBS. MVEC permeability was assessed by trans-MVEC monolayer leak of Oregon green 488-conjugated avidin, which bound subcellular biotin to localize sites of paracellular leak. Leak was correlated with individual cell-specific MVEC surface VE-cadherin continuity by fluorescence microscopy. RESULTS: Cytomix treatment reduced total MVEC VE-cadherin density, disrupted surface VE-cadherin continuity, was associated with intercellular gap formation, and enhanced paracellular avidin leak. Cytomix-induced MVEC paracellular avidin leak was strongly correlated temporally and was highly contiguous with focal MVEC surface VE-cadherin disruption. Total cellular VE-cadherin density was less strongly correlated with MVEC paracellular avidin leak and individual cell-specific focal surface VE-cadherin discontinuity. CONCLUSIONS: These data support a mechanistic link between septic human lung MVEC VE-cadherin disruption and contiguous paracellular protein leak, and will permit more detailed assessment of individual cell-specific mechanisms of septic MVEC barrier dysfunction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".