Quantification of Endothelin Receptor mRNA by Competitive RT-PCR
Bibliographic record
Abstract
The potent effects of the endothelins (ETs), including their vasoconstrictor, positive inotropic and co-mitogenic actions, are mediated by at least two distinct ET receptor subtypes, ET A ( 1 ) and ET B ( 2 ). The ET A receptor is selective for ET-1, with binding affinity ET-1>ET-2≫ET-3, while the ET B receptor is nonisoform selective. Both subtypes are structurally similar, having seven transmembrane domains characteristic of the G-protein-coupled superfamily ( 1 , 2 ). In several pathophysiological conditions, including myocardial infarction ( 3 ), congestive heart failure ( 4 ), and renal failure ( 5 ), there is altered expression of ET receptors. These changes further implicate ET in the pathogenesis of such conditions and provide additional characterization of the disease process. It is, therefore, essential to have an accurate and reliable means of measuring ET receptor expression. Traditional Northern analysis has the disadvantage of low sensitivity, and while the reverse transcription-polymerase chain reaction (RT-PCR) offers 1000–10,000-fold greater sensitivity, the exponential nature of its amplification kinetics makes it difficult to obtain truly quantitative information. Competitive RT-PCR obviates this problem by co-amplifying the gene of interest with a known concentration of mutant cDNA, which as the name suggests, competes for primer binding and PCR substrates. The subsequent PCR products from wild-type and mutant are distinguished by size or the presence or absence of a restriction enzyme site. By constructing plots of the ratio of wild-type to competitor densities vs molar concentration of competitor cRNA, the starting concentration of the wild-type RNA can be calculated. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".