A Comparison of Aorta and Vena Cava Medial Message Expression by cDNA Array Analysis Identifies a Set of 68 Consistently Differentially Expressed Genes, All in Aortic Media
Bibliographic record
Abstract
We performed a systematic analysis of gene expression in arteries and veins by comparing message profiles of macaque aorta and vena cava media using a cDNA array containing 4048 known human genes, approximately 35% of currently named human genes (approximately 11,000). The data show extensive differences in RNA expression in artery versus vein media. Sixty-eight genes had consistent elevation in message expression by the aorta, but none were elevated in the vena cava. The most differentially expressed gene was regulator of G-protein signaling (RGS) 5, at an expression ratio of 46.5+/-12.6 (mean+/-SEM). The data set also contained 2 genes already known to be expressed in the aorta, elastin at 5.0+/-1.4, and the aortic preferentially expressed gene 1 (APEG-1) at 2.3+/-0.6. We chose to analyze RGS5 expression further because of its high level of differential expression in the aorta. Levels of RGS5 mRNA were confirmed by Northern analysis and in situ hybridization. A human tissue RNA dot blot showed that RGS5 message is highest in aorta, followed by small intestine, stomach, and then heart. Northern analysis confirmed that RGS5 expression in human aorta is higher than in any region of the heart. RGS5 is a G-protein signaling regulator of unknown specificity most homologous to RGS4, an inhibitory regulator of pressure-induced cardiac hypertrophy. The expression pattern of the 68 differential genes as a whole is a start toward identifying the molecular phenotypes of arteries and veins on a systematic basis.
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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) | 0.001 | 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".