Rapid structural characterisation of benzylisoquinoline and aporphine alkaloids from <i>Ocotea spixiana</i> acaricide extract by HPTLC‐DESI‐MS<sup><i>n</i></sup>
Bibliographic record
Abstract
Abstract Introduction Lauraceae alkaloids are a structurally diverse class of plant specialised secondary metabolites that play an important role in modern pharmacotherapy, being useful as well as model compounds for the development of synthetic analogues. However, alkaloids characterisation is challenging due to low concentrations, the complexity of plant extracts, and long processes for accurate structural determinations. Objective The use of high‐performance thin layer chromatography coupled with desorption electrospray ionisation multistage mass spectrometry (HPTLC DESI‐MSn) as a fast tool to identify alkaloids present in Ocotea spixiana extract and evaluate the extract's acaricide activity. Methods Ocotea spixiana twigs were extracted by conventional liquid–liquid partitioning. HPTLC analysis of the ethyl acetate extract was performed to separate isobaric alkaloids prior to DESI‐MSn analysis, performed from MS3 up to MS7. The extract's acaricide activity against Rhipicephalus microplus was evaluated by in vitro (larval immersion test) and in silico tests. Results HPTLC‐DESI‐MSn analysis was performed to identify a total of 13 aporphine and four benzylisoquinoline‐type alkaloids reported for the first time in O. spixiana. In vitro evaluation of the extract and the alkaloid boldine showed significant activity against R. microplus larvae. It was established in silico that boldine had important intermolecular interactions with R. microplus acetylcholinesterase enzyme. Conclusion The present study demonstrated that HPTLC‐DESI‐MSn is a useful analytical tool to identify isoquinoline alkaloids in plant extracts. The acaricide activity of the O. spixiana ethyl acetate extract can be correlated to the presence of alkaloids.
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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".