Doxazosin-induced up-regulation of α<sub>1A</sub>-adrenoceptor mRNA in the rat lower urinary tract
Bibliographic record
Abstract
Alpha1-adrenoceptor (AR) antagonists can provide effective treatment of symptoms caused by benign prostatic hyperplasia. However, their mechanisms of action have not been fully elucidated. We previously reported that chronic administration of doxazosin causes an up-regulation in the mRNA expression of all three alpha1-AR subtypes in the rat prostate. As alpha1-AR antagonists might also affect the properties of alpha1-ARs in the lower urinary tract, we examined the effects of doxazosin (2 or 4 mg/kg daily subcutaneously, supplemented with 4 mg/kg daily orally for 8 or 12 weeks) on alpha1-AR subtype mRNAs in the rat bladder dome, bladder base, and urethra using real-time reverse transcription PCR. Rats that received the highest doses of doxazosin had significantly heavier bladder base and prostatic urethra than controls. PCR data showed that all three alpha1-AR subtypes were expressed in all tissues studied. Doxazosin treatment caused an up-regulation in the mRNA levels of alpha1A-AR in the rat bladder base and prostatic urethra, indicating that chronic doxazosin treatment may cause an alteration in the properties of alpha1A-AR subtype mRNA in these two areas. Furthermore, the heavier bladder base and prostatic urethra in the doxazosin-treated rats suggest that alpha1-AR antagonist treatment might also influence the growth process in these areas of the rat lower urinary tract.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".