Endothelial paxillin and focal adhesion kinase (FAK) play a critical role in neutrophil transmigration
Bibliographic record
Abstract
During an inflammatory response, endothelial cells undergo morphological changes to allow for the passage of neutrophils from the blood vessel to the site of injury or infection. Although endothelial cell junctions and the cytoskeleton undergo reorganization during inflammation, little is known about another class of cellular structures, the focal adhesions. In this study, we examined several focal adhesion proteins during an inflammatory response. We found that there was selective loss of paxillin and focal adhesion kinase (FAK) from focal adhesions in proximity to transmigrating neutrophils; in contrast the levels of the focal adhesion proteins β1-integrin and vinculin were unaffected. Paxillin was lost from focal adhesions during neutrophil transmigration both under static and flow conditions. Down-regulating endothelial paxillin with siRNA blocked neutrophil transmigration while having no effect on rolling or adhesion. As paxillin dynamics are regulated partly by FAK, the role of FAK in neutrophil transmigration was examined using two complementary methods. siRNA was used to down-regulate total FAK protein while dominant-negative, kinase-deficient FAK was expressed to block FAK signaling. Disruption of the FAK protein or FAK signaling decreased neutrophil transmigration. Collectively, these findings reveal a novel role for endothelial focal adhesion proteins paxillin and FAK in regulating neutrophil transmigration.
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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".