Nontargeted Profiling of Specialized Metabolites of<i>Digitalis purpurea</i>with a Focus on Cardiac Glycosides
Bibliographic record
Abstract
There is an urgent need to develop rapid and powerful tools for structural annotation and natural product identification to support the discovery of genes involved in specialized metabolite accumulation. In this study, nontargeted profiling of metabolites from tissues of the medicinal plant Digitalis purpurea was exploited. Application of liquid chromatography/time-of-flight mass spectrometry (LC-TOF MS) with multiplexed collision-induced dissociation (CID) generated molecular and fragment ion masses to support metabolite identification. Twenty-nine metabolites extracted from various tissues were annotated as steroidal glycosides, on the basis of mass measurements of pseudomolecular and fragment ions in conjunction with previous reports of steroidal glycosides in Digitalis tissues. The MS/MS spectra obtained by hybrid triple quadrupole-linear ion trap (QTrap) MS were complemented by accurate pseudomolecular mass measurements generated with TOF MS and fragment ion masses generated by quasi-simultaneous collision voltage. These combined methods permitted the metabolite profiling of cardiac glycosides and the tentative identification of a novel cardiac glycoside.
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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 | 0.001 | 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) | 0.002 | 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".