Natural Health Products (NHP’s) and Natural Compounds as Therapeutic Agents for the Treatment of Cancer; Mechanisms of Anti-Cancer Activity of Natural Compounds and Overall Trends
Bibliographic record
Abstract
Most cancer therapeutics, such as tubulin-targeting chemotherapy drugs, cause cytotoxic, non-selective effects. These harmful side-effects drastically reduce the cancer patient's quality of life. Recently, researchers have focused their efforts on studying natural health products (NHP's) which have demonstrated the ability to selectively target cancer cells in cellular and animal models. However, the major hurdle of clinical validation remains. NHP's warrant further clinical investigation as a therapeutic option since they exhibit low toxicity, while retaining a selective effect. Additionally, they can sensitize cancerous cells to chemotherapy, which enhances the efficacy of chemotherapeutic drugs, indicating that they can be utilized as supplemental therapy. An additional area for further research is the investigation of drug-drug interactions between NHP's and chemotherapeutics. The objectives of this review are to report the most recent results from the field of anticancer NHP research, and to highlight the most recent advancements in possible supplemental therapeutic options.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".