Expression of somatostatin receptors in splanchnic blood vessels of normal and cirrhotic rats
Bibliographic record
Abstract
BACKGROUND/AIMS: Somatostatin has been used for over two decades to treat acute variceal bleeding. Although it is assumed that somatostatin lowers portal pressure by constriction of the splanchnic arteries, little is known about the expression of somatostatin receptors (SSTR) in splanchnic blood vessels. In this study we investigated SSTR expression in splanchnic blood vessels from normal and cirrhotic rats. METHODS/RESULTS: Cirrhosis was induced by intraperitoneal injection of 50 mg thioacetamide twice a week for 14 weeks. In portal vein, mesenteric artery and aorta of normal and cirrhotic rats, mRNA for the five known SSTR was measured by quantitative reverse transcriptase-polymerase chain reaction. SSTR subtypes 1, 2, 3 and 4 were expressed, but subtype 5 was undetectable. In the portal vein of cirrhotic animals, SSTR1 was significantly down-regulated as compared with controls. Otherwise, no major differences in receptor expression between normal and cirrhotic animals were observed. Using immunohistochemistry, we identified all five receptors, although the staining of receptor 5 was very weak. CONCLUSION: All five SSTR are expressed in splanchnic blood vessels. Our results suggest that cirrhosis reduces expression of SSTR1 in portal vein. In other vessels, no major differences between the normal and cirrhotic state were noted.
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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 | 0.000 |
| 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 | 0.000 |
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".