Ascorbic Acid Decreases the Binding Affinity of the AT1 Receptor for Angiotensin II
Bibliographic record
Abstract
BACKGROUND: Ascorbic acid is an essential vitamin and a powerful antioxidant. Many studies have highlighted the benefits of ascorbic acid for chronic cardiovascular diseases such as hypertension in which angiotensin II (Ang II) plays an significant role. We therefore hypothesized that ascorbic acid could modify the pharmacological properties of the AT(1) receptor for Ang II. METHODS: Binding studies and Ca(2+) mobilization studies were performed with HEK293 cells stably expressing the AT(1) receptor for Ang II. Smooth muscle contraction studies were performed with rabbit aorta strips that endogenously express the AT(1) receptor. RESULTS: Scatchard analysis revealed that ascorbic acid decreased the binding affinity of the AT(1) receptor without modifying its maximal binding capacity. Ascorbic acid did not modify the binding affinity of the AT(2) receptor for Ang II or of the UT receptor for urotensin II. In single-cell Ca(2+) imaging assays, ascorbic acid reduced the frequency of intracellular Ca(2+) oscillations induced by a low dose of Ang II. In functional assays, ascorbic acid significantly diminished the contraction of rabbit aorta pre-contracted with Ang II but not those pre-contracted with urotensin II. CONCLUSIONS: Ascorbic acid decreases the binding affinity of the AT(1) receptor. These results offer a mechanistic explanation for the reported blood pressure lowering effect of ascorbic acid.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".