Selective Induction of Apoptosis by Azadarichta indica Leaf Extract by Targeting Oxidative Vulnerabilities in Human Cancer Cells
Bibliographic record
Abstract
PURPOSE: Natural products have been a great source of medications used in conventional medicines for the treatment of various diseases; more importantly, they have played a significant role in the development of anti-cancer drugs for a number of decades. The benefits to employing whole extracts of natural health products, rather than a single ingredient, for cancer treatment remains unexplored. Our research group has previously demonstrated the potential anti-cancer benefits of several natural health products (NHPs), prompting further studies into other NHPs, such as Neem (Azadarichta indica), a tree native to India and has been used in Ayurvedic medicine for over 4000 years. The objective of this study is to determine the possible anti-cancer potential of aqueous and ethanolic Neem leaf extracts (NLEs) and to identify the specific mode(s) of action. METHODS: Cells were treated with NLE and cell viability was then assessed using a water-soluble tetrazolium salt. Cell death was confirmed using the fluorescent dye propidium iodide and apoptosis was identified using the Annexin-V binding assay. Mitochondrial membrane permeabilization was visualized using JC-1 staining and the production of whole cell and mitochondrial ROS was measured with 2',7'-dichlorodihydrofluorescein diacetate (H2DCFDA) and Amplex Red, respectively. In vivo efficacy of aqueous NLE was assessed in human tumour xenografts in CD-1 nu/nu immunocompromised mice. RESULTS: Results indicate that both ethanolic and aqueous extracts of Neem leaf were effective in inducing apoptosis in leukemia and colon cancer cells, following destabilization of the mitochondrial membrane. Furthermore, an increase in the production of reactive oxygen species (ROS) was observed in cancer cells treated with NLEs, indicating that oxidative stress may play a role in the mechanism of cell death. Additionally, in vivo results showed that aqueous NLE (delivered orally) was well tolerated and inhibited tumour growth of human xenografts in mice. CONCLUSIONS: These findings suggest the potential of NLEs as safer and effective alternatives to conventional chemotherapy. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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) | 0.001 | 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".