Antioxidant, enzyme inhibition, toxicity, and molecular docking analysis of <i>Melaleuca cajuputi</i> leaf extract and fractions
Bibliographic record
Abstract
Melaleuca cajuputi plant has piqued the interest of researchers due to its pharmacological properties. However, there is scarce information regarding its enzyme inhibitory effects and possible mechanism of action. Thus, this study aimed to investigate the antioxidant, enzyme inhibitory, and potential binding interactions of bioactive components in M. cajuputi extract and fractions with the target enzymes. The 1,1-diphenyl-2-picrylhydrazyl radical scavenging (DPPH) assay was used for the antioxidant activity. The enzyme inhibition potential was assessed using tyrosinase and acetylcholinesterase (AChE) enzymes. M. cajuputi methanolic extract (MCME) demonstrated the most potent scavenging activity in comparison to Melaleuca fraction (MF1) and (MF2), as evidenced by IC50 values of 28.14 ± 0.96, 35.26 ± 0.64, and 80.90 ± 0.17 μg/mL, respectively. Similarly, the results of the enzyme inhibition assay showed that the MCME (IC50 65.81± 0.39 μg/mL) had the highest potency on tyrosinase enzyme compared to MF1 (IC50 131.3± 0.43 μg/mL) and MF2 (IC50 215.3 ± 0.48 μg/mL). However, MF2 (IC50 3.35 ± 0.37 μg/mL) showed a remarkable AChE enzyme inhibition than MCME and MF1. On the other hand, MCME possessed a low toxicity effect with an LC50 of 781 ± 0.07 μg/mL, whereas MF1 and MF2 were non-toxic. The 10-methylanthracene-9-carboxaldehyde, 2-isopropyl-10-methylphenanthrene, 2-tert butylanthracene, β-eudesmol, and α-eudesmol showed a strong binding propensity ranging from -5.1 to -6.2 and -6.4 to -7.6 kcal/mol towards tyrosinase and AChE respectively. The leaf extract of M. cajuputi could be considered a novel source of naturally occurring antioxidants and promising enzyme inhibitors.
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".