Apoptotic Phenotype Alters the Capacity of Tumor Necrosis Factor-Related Apoptosis-Inducing Ligand to Induce Human Vascular Endothelial Activation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND/AIMS: The ability of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) to activate vascular endothelium is unclear. This study investigates the link between endothelial apoptosis and activation in response to TRAIL. METHODS AND RESULTS: Endothelial cell apoptosis was modeled with the immortalized human endothelial cell line EA.hy926, and with primary human umbilical vein endothelial cells (HUVEC) sensitized with the phosphatidylinositol 3-kinase inhibitor LY294002 in 1% serum. EA.hy926 expressed greatest levels of TRAIL receptors R1 and R2, and HUVEC of R2 and R3, determined by flow cytometry. Recombinant human (rh)TRAIL induced apoptosis in both models, reducing cell numbers preventable with caspase inhibitors, and confirmed by annexin V staining. In EA.hy926, rhTRAIL activated NF-kappaB (1 h) with increased ICAM-1 expression (24 h). rhTRAIL also increased adhesion of human neutrophils, blocked with an antibody to neutrophil CD18, a ligand for ICAM-1, and with antibodies to TRAIL and TRAIL-R1 and R2. rhTRAIL increased neutrophil adhesion to sensitized HUVEC, without effect on unmodified HUVEC. rhTRAIL did not increase surface labeling of ICAM-1 or E-selectin in sensitized HUVEC. CONCLUSIONS: TRAIL increases neutrophil adhesion when it concurrently induces apoptosis both in EA.hy926 and in sensitized HUVEC. TRAIL may therefore induce endothelial activation in concert with endothelial apoptosis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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) | 0.000 | 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 it