GC-MS profiling and assessment of antioxidant, antibacterial, and anticancer properties of extracts of Annona squamosa L. leaves
Bibliographic record
Abstract
BACKGROUND: The research and application of plants in food supplements and drugs have attracted great interest. This study aimed to examine the efficiency of several solvents for the extraction of the main compounds from Annona squamosa leaves and to evaluate the antioxidant, antibacterial, and anticancer activities of these extracts. METHODS: Gas chromatography-mass spectrometry was used to screen the bioactive compounds of A. squamosa methanolic extract. The free radical, hydrogen peroxide, and nitric oxide scavenging activities of the extracts were investigated. Furthermore, MTT, nuclear staining, LDH, and monolayer wound repair assays were performed to evaluate the potential anticancer activity of the extracts in colon cancer cells while the antibacterial activity was tested by using a well diffusion assay. RESULTS: ). These extracts exhibited strong antioxidant activity and antibacterial potency against both gram-positive and gram-negative bacteria. Different A. squamosa leaves extracts displayed remarkable antiproliferative, cytotoxic, antimigration, and apoptotic activities in colon cancer cells. CONCLUSIONS: A. squamosa leaves contain major bioactive compounds that inhibit the growth of several types of bacteria and colon cancer cell lines, which demonstrated their efficacy as an alternative source of antibiotics and for the development of novel drugs for colon cancer therapy.
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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 | 0.001 | 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".