Quantitative Proteomic Analysis of Sokotrasterol Sulfate-stimulated Primary Human Endothelial Cells
Bibliographic record
Abstract
The endothelium forms a continuous monolayer at the interface between blood and tissue and contributes significantly to the sensing and transducing of signals between blood and tissue. New blood vessel formation, or angiogenesis, is initiated by the activation of endothelial cells and is an important process required for various pathological and physiological situations. This study used cleavable isotope-coded affinity tag reagents combined with mass spectrometry to investigate the molecular basis of a recently discovered angiogenesis-promoting steroid, sokotrasterol sulfate. Changes in the relative abundances of over 1000 proteins within human endothelial cells treated with sokotrasterol sulfate and vehicle-treated cells were identified and quantitated using this technique. A method that examines the entire ensemble of quantitative measurements was developed to identify proteins that showed a statistically significant change in relative abundance resulting from treatment with sokotrasterol sulfate. A total of 93 proteins was significantly up-regulated, and 37 were down-regulated in response to sokotrasterol sulfate stimulation of endothelial cells. Among the up-regulated proteins, several were identified that are novel to endothelial cells and are likely involved in cell communication and morphogenesis. These findings are consistent with a role for sokotrasterol sulfate in endothelial sprouting.
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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 | 0.001 | 0.001 |
| 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".