Promising Phytoconstituents in Antiangiogenesis Drug Development
Bibliographic record
Abstract
Angiogenesis, the process of forming new blood vessels from existing ones, is crucial in both physiological and pathological conditions, such as tumor growth, metastasis, and inflammatory disorders. Targeting angiogenesis has emerged as a promising therapeutic strategy. Recent research has increasingly focused on the role of bioactive components found in food in regulating angiogenesis, although there are certain limitations. This review provides a comprehensive examination of the origins, composition, pharmacological activities, and mechanisms of action of these components in medicinal foods, reflecting the growing intersection of medicine and nutrition. The goal is to aid in preventing angiogenesis-related complications and fostering healthier habits. The insights offered in this review aim to advance the development of effective, low-toxicity antiangiogenic drugs. Additionally, there has been a notable rise in interest in plant-derived compounds with antiangiogenic properties. This review investigates the potential of phytoconstituents from plants as drug candidates targeting angiogenesis, exploring their mechanisms of action, the research conducted thus far, and the challenges associated with transitioning these compounds into clinical applications.
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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 | 0.001 | 0.001 |
| 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) | 0.004 | 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".