Somatostatin receptor subtype expression and function in human vascular tissue
Bibliographic record
Abstract
In animal models the somatostatin analog angiopeptin inhibits intimal hyperplasia by acting primarily through somatostatin receptor 2 (SSTR-2). However, the results of clinical trials using angiopeptin have been disappointing. In this study we showed that human blood vessels express high levels of SSTR-1 with significantly lower levels of SSTR-2 and -4. Samples of normal veins and arteries, as well as atherosclerotic arteries, expressed predominantly SSTR-1. In addition, the levels of SSTR-1 varied between individuals, indicating that the vascular disease process may have affected SSTR gene expression. Immunocytochemical studies demonstrated that SSTR-1 was present in endothelial but not vascular smooth muscle cells. No evidence of SSTR-3 or -5 expression was detected in normal or diseased blood vessels. Two endothelial cell preparations, ECV304 and human umbilical vein endothelial cells, were investigated and shown to express only SSTR-1 and -4. Exposure of these cells to 10 nM somatostatin or 10 nM SSTR-1-specific agonist resulted in alterations to the actin cytoskeleton, as characterized by a loss of actin stress fibers coupled with an increase in lamellipodia formation at the plasma membrane. These results suggest that the lack of effectiveness of angiopeptin in humans may be due to the differential expression of SSTR-1 by human endothelial cells.
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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) | 0.002 | 0.001 |
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".