Inhibitory activities of baicalin against renin and angiotensin-converting enzyme
Bibliographic record
Abstract
CONTEXT: Baicalin has been characterized as the active compound and quality control marker in Scutellaria baicalensis Georgi, traditionally used as a hypotensive herb. OBJECTIVES: To investigate the inhibitory activities of baicalin against renin and angiotensin-I converting enzyme (ACE) and their molecule mechanism of interactions. METHODS: The fluorescence method using renin substrate 1(R-2932) and the spectroscopy method by Cushman were used to determine renin and ACE activities, respectively. The fluorescence quench techniques were used to characterize their interactions. RESULTS: The results showed that baicalin inhibited renin activity with an IC(50) value of 120.36 µM and inhibited ACE activity with an IC(50) value of 2.24 mM in vitro. The fluorescence emission of both renin and ACE were efficiently quenched by baicalin and a complete quenching was achieved at a high concentration of baicalin. Furthermore, baicalin was more effective in quenching the fluorescence of renin (K(SV) = 60 × 10(3) M(-1)) than ACE (K(SV) = 17.1 × 10(3) M(-1)). The quenching of fluorescence of renin and ACE involved static interactions, which was characterized by the formation of quencher-enzyme complex. The baicalin-renin complex formed through three-sites binding including the active site with a binding constant of 796.15 × 10(13) M(-1), but there was only one binding site for the baicalin-ACE complex with a much smaller binding constant of 6.8 × 10(5) M(-1). CONCLUSION: The inhibition activity of baicalin against renin was a result of the formation of stable complex through multisites binding including the active site, which could explain the higher inhibitory efficiency.
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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 | 0.000 | 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) | 0.001 | 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".