The Pre-Clinical Evaluation of the Anti-Cancer Activity of Hibiscus and Lemon Grass Extracts on Non-Hodgkin Lymphoma Cells
Bibliographic record
Abstract
Cancer is the leading cause of death in Canada and it is predicted that 2 out of 5 Canadians will develop the disease in their life-time. Current cancer therapies are often associated with severe side effects that include the formation of secondary malignancies and even death. As a result, research is focused on studying various novel anti-cancer agents, to discover new therapies that are safer and more effective. A majority of anti-cancer agents have been derived from natural health products; however, anecdotal evidence indicates that the benefits of the whole extract remains unexplored. Thus, it is important for scientific research to explore whether traditionally used natural extracts possess safe and effective anti-cancer properties. This project aims to uncover the anti-cancer benefits possessed by lemon grass and hibiscus extracts against Non-Hodgkin Lymphoma, as well as the mechanism(s) of action of these extracts alone and in combination with each other while identifying the pharmacologically active components within these extracts. These extracts have shown promising results is preliminary cytotoxicity screenings using a WST-1 assay. Also, cell staining with annexin V and propidium iodide have shown that these extracts induce apoptosis in U-937 cells.The most effective doses are currently being examined further to determine the mode of cell death being induced by the extracts. Furthermore, synergistic studies are being carried out to ascertain if there are benefits to using multiple effective extracts to combat the complexity of cancer. Preliminary findings also show a selectivity of these extracts to cancer cells. This research will determine if lemon grass and hibiscus can be used as safe and effective treatments for Non-Hodgkin Lymphoma.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".