Evaluation of ferroptosis-based anti-leukemic activities of ZnO nanoparticles synthesized by a green route against Pre-B acute lymphoblastic leukemia cells (Nalm-6 and REH)
Bibliographic record
Abstract
Background Our research presents an efficient and practical method for producing Zinc Oxide nanoparticles (ZnO NPs), which have anti-leukemic effects based on ferroptosis. Methods The black cardamom extract was employed as a capping and reducing agent for the green synthesis. The NPs have been characterized via scanning electron microscopy, X-ray diffraction, and Fourier-transform infrared spectroscopy. Additionally, leukemic and normal cells were exposed to ZnO NPs (25, 50, 75, 100, 150, 200, and 300 μg/mL) for 24 and 48 h. The cell vitality was then measured using the MTT test. Moreover, ferroptosis indicators were assessed via commercial testing kits, and finally, qRT-PCR and flow cytometry were used to measure gene expression and cell death. Results The findings displayed that green synthesized ZnO NPs reduced the survival of leukemic cells, with IC50 values of 150.89 μg/ml for Nalm-6 and 101.31 μg/ml for REH cells after 48 h. The ZnO NPs increased ferroptosis by significantly increasing MDA, intracellular iron, ACSL4 , ALOX15 , and p5 3 mRNA expressions while significantly decreasing GSH and GPx activity levels and SLC7A11 and GPx4 mRNA expressions. On the other hand, ZnO NPs exhibited no toxicity toward normal cells. Conclusions The research suggests that ZnO NPs synthesized using the green approach can induce ferroptosis in leukemic cells by disrupting redox homeostasis and increasing intracellular iron levels, potentially enhancing the benefits of anti-leukemic therapies in the future.
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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) | 0.000 | 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".