Metabolic model for laboratory control of anti-ischaemic therapy effectiveness: a case study of nicorandil
Bibliographic record
Abstract
Scientific relevance. A key anti-ischaemic mechanism of some medicinal products involves their effects on the metabolism of endothelial vasodilators, particularly the synthesis of nitric oxide from arginine and its precursor citrulline. Aim. The study was aimed to determine whether the plasma time course of guanidine derivatives (arginine precursors) is applicable to laboratory control of anti-ischaemic therapy effectiveness using a single oral dose of nicorandil in patients with coronary heart disease as a case study. Materials and methods. The authors used high-performance liquid chromatography to determine metabolites. Blood samples for analysis were obtained from 30 patients with angina pectoris (Grade II–III, Canadian Cardiovascular Society) and 30 healthy donors. All the study participants received a single oral dose of 20 mg nicorandil after 10 h of fasting. Results. At baseline, patients showed significantly higher plasma citrulline levels than donors. However, the elevated levels decreased to the healthy range after nicorandil administration. Plasma arginine levels in patients showed a statistically significant increase following nicorandil administration. Plasma homoarginine levels in patients remained reduced both before and after dosing. Nicorandil did not influence elevated levels of the endogenous nitric oxide synthase inhibitor (asymmetrical dimethylarginine). Conclusions. In addition to the established mechanisms responsible for altering cell metabolism, nicorandil enhances the contribution of citrulline to arginine resynthesis. It is reasonable to include citrulline and arginine, which are involved in the vasodilator response, in model schemes for laboratory control of the effectiveness of anti-ischaemic therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".