The Efficacy of Natural Extracts (Lemongrass, White Tea, and Dandelion Root) and their Interactions with Conventional Chemotherapeutic Drugs for the Treatment of Colon Cancer
Bibliographic record
Abstract
The problem with conventional cancer treatments is that the drugs used are not selective towards cancer and in turn are toxic to healthy cells. New therapeutic development should target vulnerabilities that are unique to cancer cells that can trigger cell death. Numerous natural extracts and compounds have been reported to have efficacious medicinal properties with selective activity towards various diseases. Dandelion (Taraxacum spp) root and lemongrass (Cymbopogon citratus) extracts each contain multiple bioactive compounds and have been shown to target multiple pathways in cancer cells to selectively induce apoptosis. Recent work in our lab shows that lemongrass and white tea extracts possess the ability to selectively induce apoptosis in lymphoma and leukemia models. Herein, we report the anticancer properties of ethanolic lemongrass extract in colorectal cancer models. These extracts are to be tested for possible interactions with existing colorectal cancer chemotherapy drugs. Additionally, none of these extracts have been examined for their efficacy in tumour-bearing transgenic mice. Therefore, our objective is to utilize a transgenic animal model to demonstrate the ability of these extracts to inhibit the onset of colon cancer. Thus, utilizing natural extracts could be a potential means to treating and/or preventing the occurrence of cancer in a non-toxic manner without disrupting chemotherapeutic treatments. Most importantly, since these extracts are well-tolerated, they can be taken over long periods of time, decreasing the chances of relapse.
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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".