Metabolomics analysis and biological investigation of three Malvaceae plants
Bibliographic record
Abstract
INTRODUCTION: Metabolomics is a fast growing technology that has effectively contributed to many plant-related sciences and drug discovery. OBJECTIVE: To use the non-targeted metabolomics approach to investigate the chemical profiles of three Malvaceae plants, namely Hibiscus mutabilis L. (Changing rose), H. schizopetalus (Dyer) Hook.f. (Coral Hibiscus), and Malvaviscus arboreus Cav. (Sleeping Hibiscus), along with evaluating their antioxidant and anti-infective potential. METHODOLOGY: Metabolic profiling was carried out using liquid chromatography coupled with high-resolution electrospray ionisation mass spectrometry (LC-HR-ESI-MS) for dereplication purposes. The chemical composition of the studied plants was further compared by principal component analysis (PCA). The antioxidant and anti-infective properties of their different extracts were correlated to their phytochemical profiles by orthogonal partial least square discriminant analysis (OPLS-DA). RESULTS: A variety of structurally different metabolites, mostly phenolics, were characterized. Comparing the distribution pattern of these tentatively identified metabolites among the studied plant species/fractions revealed the chemical uniqueness of the dichloromethane fraction of M. arboreus. Some extracts and fractions of these plants demonstrated noteworthy antioxidant and antitrypanosomal potential; the latter was partly attributed to their anti-protease activities. The active principles of these plants were pinpointed before any laborious isolation steps, to avoid the redundant isolation of previously known compounds. CONCLUSION: This study highlighted the use of the established procedure in exploring the metabolomes of these species, which could be helpful for chemotaxonomic and authentication purposes, and might expand the basis for their future phytochemical analysis. Coupling the observed biological potential with LC-MS data has also accelerated the tracing of their bioactive principles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".