Determination of Lappaconitin, Diterpene Alkaloide Obtained from Plants <i>Aconitum leucostomum</i>, and its Active Metabolite N-desacetyllappaconitin in Human Plasma and Blood
Bibliographic record
Abstract
Introduction. Lappaconitine is an alkaloid, contained into Aconitum leucostomum Vorosh. roots and herbs. The alkaloid is indicated to arrhythmia. The lappaconitine drugs are metabolized into eight pharmacologically active substances, but N-desacetyllappaconitine is the most effective. Drugs based on a lappaconitine has narrow therapeutic range and many kinds of side effects. Pharmacokinetics of lappaconitine should be more studied for safety medical use of lappaconitine drugs. Aim. The aim of this study is to develop method for the quantitative determination of lappaconitine and its active metabolite N-desacetyllappaconitine in human plasma and blood by high performance liquid chromatography and tandem mass spectrometry (HPLC-MS/MS). Materials and methods. Determination of lappaconitine and N-desacetyllappaconitine in plasma and blood was carried out by HPLC-MS/MS. The samples were processed by acetonitrile protein precipitation. Results and discussion. This method was validated by next parameters: selectivity, matrix effect, calibration curve, accuracy, precision, spike recovery, lower limit of quantification, carry-over effect and stability. Conclusion. The method of the quantitative determination of lappaconitine and N-desacetyllappaconitine in human plasma and blood was developed and validated by HPLC-MS/MS. The analytical range of the was 0.50-50.00 ng/ml for lappaconitine and 0.50-100.00 ng/ml for N-desacetyllappaconitine in biological matrix. Method could be applied to determination of lappaconitine and N-desacetyllappaconitine for PK studies.
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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".