Rapid identification of chemical components <i>in vitro</i> and <i>in vivo</i> of Menispermi Rhizoma by integrating UPLC‐Q‐TOF‐MS with data post‐processing strategy
Bibliographic record
Abstract
INTRODUCTION: Menispermi Rhizoma (MR), the dried rhizome of Menispermum dauricum DC. (Menispermaceae), has been used to treat sore throat, enteritis, dysentery, and rheumatic arthralgia. Despite extensive research on its pharmacological effects, the chemical components in vitro and in vivo have not been thoroughly studied. OBJECTIVE: To establish an efficient method for rapid classification and identification of alkaloids in MR and its preparations, as well as metabolites in vivo after oral administration of MR. METHODS: Rapid identification of alkaloids and absorbed components of MR was performed using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) coupled with UNIFI software. Moreover, the characteristic fragmentations and neutral losses of different types of alkaloids in MR were summarised to realise the rapid classification of alkaloids. RESULTS: A total of 55 components were unambiguously or tentatively identified in MR. Among them, 37 and 31 components were found in MR capsules and tablets, respectively. Meanwhile, 109 compounds were tentatively identified in rat plasma, urine and faeces, including 55 prototypes and 54 metabolites. Hydrogenation, hydroxylation, methylation, glucuronic acid and sulphate conjugations were the dominating metabolic fates of alkaloids. CONCLUSION: The data post-processing strategy established could greatly enhance the structural identification efficiency. The results obtained might lay the foundation for further interpretation of clinical effects, mechanism of action and quality control of MR.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".