Cytotoxic and anti-diabetic potential, metabolic profiling and <i>insilico</i> studies of <i>Syzygium cumini</i> (L.) Skeels belonging to family <i>Myrtaceae</i>
Bibliographic record
Abstract
LC-HR-MS-coupled metabolic profiling of the methanol extracts from different parts of Syzygium cumini (L.), which was extensively identified via DNA fingerprinting, led to dereplication of 24 compounds. Cytotoxic investigation highlighted both extracts as the most potent, against both MCF-7 and MDA-231 Cell lines, with IC50 value of 5.86 ± 0.63 µg/ml and against HCT −116 cell line, with IC50 value of 1.24 ± 0.09 µg/ml, respectively. A molecular docking study was performed on the dereplicated compounds, which highlighted myricetin-3-glucoside (7), myricitrin (12), reynoutrin (15) and quercitrin (16) as the top scoring ligands within the protein active site (FIH-1). Interestingly, the extracts were significant against streptozotocin-induced diabetes in the order of flowers > seeds > leaves with BGL level of 98.9 ± 4.3, 123.2 ± 4.9 and 132.8 ± 5.9 mg/dl, respectively. The study highlights the health benefits of Syzygium cumini (L.) as a promising cytotoxic source. Cytotoxic and anti-diabetic potential, metabolic profiling and insilico studies of Syzygium cumini (L.) Skeels belonging to family MyrtaceaeAll authorsSeham Salah Eldin Elhawary, Amira kamal Eldin Elmotayam, Doaa kamel Alsayed, Eman Maher Zahran, Merivat Ahmed Fouad, Amany Ameen Sleem, Hanan Elimam , Mohamed H. Rashed, Alaa M. Hayallah, Anber F. Mohammed & Usama Ramadan Abdelmohsenhttps://doi.org/10.1080/14786419.2020.1843032Published online:04 November 2020
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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) | 0.001 | 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".