Advances in the Research and Development of Natural Health Products as Main Stream Cancer Therapeutics
Bibliographic record
Abstract
Natural health products (NHPs) are defined as natural extracts containing polychemical mixtures; they play a leading role in the discovery and development of drugs, for disease treatment. More than 50% of current cancer therapeutics are derived from natural sources. However, the efficacy of natural extracts in treating cancer has not been explored extensively. Scientific research into the validity and mechanism of action of these products is needed to develop NHPs as main stream cancer therapy. The preclinical and clinical validation of NHPs would be essential for this development. This review summarizes some of the recent advancements in the area of NHPs with anticancer effects. This review also focuses on various NHPs that have been studied to scientifically validate their claims as anticancer agents. Furthermore, this review emphasizes the efficacy of these NHPs in targeting the multiple vulnerabilities of cancer cells for a more selective efficacious treatment. The studies reviewed here have paved the way for the introduction of more NHPs from traditional medicine to the forefront of modern medicine, in order to provide alternative, safer, and cheaper complementary treatments for cancer therapy and possibly improve the quality of life of cancer patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".