Plant-Derived Anti-Cancer Therapeutics and Biopharmaceuticals
Bibliographic record
Abstract
In spite of significant advancements in diagnosis and treatment, cancer remains one of the major threats to human health due to its ability to cause disease with high morbidity and mortality. A multifactorial and multitargeted approach is required towards intervention of the multitude of signaling pathways associated with carcinogenesis inclusive of angiogenesis and metastasis. In this context, plants provide an immense source of phytotherapeutics that show great promise as anticancer drugs. There is increasing epidemiological data indicating that diets rich in vegetables and fruits could decrease the risks of certain cancers. Several studies have proved that natural plant polyphenols, such as flavonoids, lignans, phenolic acids, alkaloids, phenylpropanoids, isoprenoids, terpenes, and stilbenes, could be used in anticancer prophylaxis and therapeutics by recruitment of mechanisms inclusive of antioxidant and anti-inflammatory activities and modulation of several molecular events associated with carcinogenesis. The current review discusses the anticancer activities of principal phytochemicals with focus on signaling circuits towards targeted cancer prophylaxis and therapy. Also addressed are plant-derived anti-cancer vaccines, nanoparticles, monoclonal antibodies, and immunotherapies. This review article brings to light the importance of plants and plant-based platforms as invaluable, low-cost sources of anti-cancer molecules of particular applicability in resource-poor developing countries.
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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 | 0.000 |
| 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 | 0.002 |
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".