Effect of <i>Annona squamosa</i> leaf extract on human promyelocytic leukemia cells subjected to oxidative stress
Bibliographic record
Abstract
OBJECTIVES: has beneficial properties. However, its cytotoxicity and antioxidative effects on human promyelocytic leukemia cells (HL60) deserve investigation. Therefore, the efficacy of its crude extracts in offsetting damage in HL60 cells subjected to oxidative stress was studied. METHODS: Crude extracts at different concentrations were incubated with HL60 cells. The beneficial properties of the plant extract against oxidative damage were evaluated post-induction of oxidative stress utilizing hydrogen peroxide. RESULTS: Extracts at concentrations 600 and 800 μg/mL were most effective at increasing the viability of damaged cells compared to the control group after 48 h of incubation. Significant increases in lipid peroxidation were observed in exposed cells treated with 600 μg/mL extract after 72 h of incubation. Superoxide dismutase (SOD) and catalase activities significantly increased in exposed cells after 24 h of incubation at all extract concentrations. Exposed cells treated with 600 and 1,000 μg/dL of the extract showed significantly increased catalase activity after 48 h, and a similar profile was maintained after 72 h of exposure. SOD activity in exposed cells remained significantly increased at all treatment concentrations after 48 and 72 h of incubation. Treatment with 400, 600, and 800 μg/mL of the extract resulted in significantly increased reduced glutathione levels compared to the other groups after 24 and 72 h of incubation. However, after 48 h of incubation, significant increases were noted in glutathione levels in exposed cells incubated with either 400, 800, or 1,000 μg/mL extract. CONCLUSIONS: might effectively protect against oxidative damage in a time and extract concentration-dependent manner.
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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 | 0.000 | 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".