Differential proteomic analysis of lymphatic, venous, and arterial endothelial cells extracted from bovine mesenteric vessels
Bibliographic record
Abstract
Differential protein profiling by 2-D PAGE is generally useful in biomarker discovery, proteome analysis and routine sample preparation prior to analysis by MS. The goal of this study was to compare 2-D PAGE-resolved protein profile of lymphatic endothelial cells to those of venous, and arterial endothelial cells isolated from lymphatic and blood vessels of bovine mesentery (bm). Three 2-D PAGE electrophoretograms were produced for each of the three cell types and quantitatively analyzed. Protein identification by LC-MS/MS was performed to identify 39 proteins found to be present at statistically significantly different levels in the three cell types (p<0.05). Most of the 39 proteins have not been previously reported in EC proteomic studies of 2-D PAGE electrophoretograms. Three proteins, HSPA1B (HSP70 family member), HSPB1 (HSP27 family member), and UBE2D3 (a member of E2 ubiquitin-conjugating enzymes) found to be at highest levels in bm arterial endothelial cells, bm venous endothelial cells, and bm lymphatic endothelial cells, respectively, were validated by immunoblotting with appropriate antibodies. The lack of substantial overlap between our results and those of other groups' comparative studies are discussed. Functional implications of differences in levels of various proteins identified in the three cell types are also discussed.
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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) | 0.000 | 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 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".