Distribution of α<sub>1</sub>-adrenoceptor subtype proteins in different tissues of neonatal and adult rats
Bibliographic record
Abstract
Distribution of α1-adrenoceptor (α1AR) subtype (α1A, α1B, α1D) proteins in brain, heart, kidney, and liver of 1-week-old rats and in brain, heart, aorta, kidney, liver, vas deferens, prostate, and adrenal glands of adult rats was investigated by Western analysis, using receptor subtype specific polyclonal antibodies. High levels of immunoreactive α1AAR and α1DAR in brain and heart and of α1BAR in liver and heart of neonatal rats were detected. In adult rat tissues, the abundance of α1AAR protein was most marked in the brain, intermediate in heart, aorta, liver, vas deferens, and adrenals, and minimal in the kidney and prostate; relative to other tissues, the expression of α1BAR was higher in brain and heart and that of α1DAR in brain. All the three receptor subtypes increased with age in the brain cortex, whereas the abundance of α1BAR increased in the heart but decreased in the liver; α1AAR and α1DAR in liver, kidney, and heart were not affected by age. It is concluded that α1AR subtypes are widely expressed in different neonatal and adult rat tissues.Key words: α1A-adrenoceptors, α1B-adrenoceptors, α1D-adrenoceptors, α1-adrenoceptor proteins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".