Phenolic Profile, Inhibition of α-Amylase and α-Glucosidase Enzymes, and Antioxidant Properties of Solanum elaeagnifolium Cav. (Solanaceae): In Vitro and In Silico Investigations
Bibliographic record
Abstract
In this study, the chemical composition and the antioxidant and antidiabetic properties of S. elaeagnifolium flower (SEFl), fruit (SEFr), and leaf (SEFe) extracts were investigated in vitro and in silico. HPLC-DAD analysis was used to determine the chemical components. Colorimetric techniques were used to identify polyphenols and flavonoids. The antioxidant capacity was determined using DPPH and TAC assays. The antidiabetic activity was examined using the enzymes α-amylase and α-glucosidase. Molecular docking methods were used to assess the anti-dipeptidyl peptidase IV (DPP-IV) activity. According to HPLC findings, extracts of S. elaeagnifolium flowers, leaves, and fruits are rich in salicylic acid, sinapic acid, chlorogenic acid, naringin, quercetin, quercetin-3-O-beta-glucoside, kaempferol, and chalcone. The IC50 for flower, leaf, and fruit extracts were 132 ± 5.59 μg/mL, 43.19 ± 1.46 μg/mL, and 132 ± 5.59 μg/mL, respectively. The total antioxidant capacity of SEFr, SEFe, and SEFl were determined to be 900.06 ± 4.01 μg AAE/mg, 792.10 ± 6.72 μg AAE/mg, and 681.10 ± 3.02 μg AAE/mg, respectively. Importantly, SEFe, SEFl, and SEFr displayed significant anti-α-amylase activity, with IC50 values of 79.16 ± 2.35 µg/mL, 99.16 ± 1.17 µg/mL, and 40.31 ± 2.04 µg/mL, respectively. The results also showed that SEFr, SEFe, and SEFl all exhibited potent anti-α-glucosidase activity, whose IC50 values were determined to be 20.53 ± 0.37 µg/mL (SEFr), 20.05 ± 0.12 µg/mL (SEFe), and 41.1 ± 1.55 µg/mL (SEFl). Molecular docking of S. elaeagnifolium phenolic compounds in the active site of DPP-IV revealed a strong inhibitory effect, with a glide score ranging from −2.63 to −8.10 Kcal/mol. Notably—with glide scores of −8.10, −6.23, −5.73, and −5.37 Kcal/mol—rutin, quercetin-3-O-beta-glucoside, chalcone, and naringin were the most active molecules against DPP-IV.
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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 | 0.001 |
| 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) | 0.001 | 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".