Endothelial cell paxillin is remodeled during leukocyte adhesion and is essential for transmigration
Bibliographic record
Abstract
The focal adhesion protein paxillin plays a critical role in focal adhesion remodeling and signaling and as such was examined within endothelial cells (EC) during in vitro neutrophil transmigration. We perfused isolated human neutrophils across TNF‐treated EC in a parallel plate flow chamber and investigated paxillin remodeling by immunostaining. Immunofluorescence data showed that there was a localized loss in EC paxillin staining following neutrophil adhesion under flow conditions. Imaging exogenous paxillin‐YFP in EC, we observed endothelial paxillin loss and remodeling in real‐time. In flow experiments in which we perfused fixed neutrophils or a neutrophilic cell line, both of which adhere but do not transmigrate, we found that paxillin loss was transmigration‐dependant. A functional role for EC paxillin was examined by utilizing siRNA. Using flow cytometry and western blotting, we showed more than ninety percent transfection efficiency and down regulation of paxillin. Downregulation of EC paxillin had no effect on the total number of neutrophils recruited to TNF‐stimulated EC; however, transmigration was blocked by more than 40%. This was not due to changes in expression of E‐selectin, IL‐8, or ICAM‐1 in paxillin downregulated EC. These data suggest that focal adhesion remodeling occurs during leukocyte recruitment and is essential for maximal transmigration. Research funded by The Canadian Institutes of Health Research.
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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".