Pomegranate, its Components, and Modern Deliverable Formulations as Potential Botanicals in the Prevention and Treatment of Various Cancers
Bibliographic record
Abstract
Cancer is a global multifactorial disease consisting of over 200 types of cancers. It is well recognized that primary prevention is an effective way to fight cancers by using natural polyphenolic anticancer foods, vegetables, and fruits, avoiding exposure to the carcinogenic environment, smoking cessation, and through lifestyle modifications. The present review provides up-todate information on the effects and functions of pomegranate juice and its bioactive components on the most widespread six cancer types. Pomegranate contains important polyphenolic compounds, such as ellagitannins and punicalagin, with strong antioxidant ability for scavenging free radicals and producing metal-chelates in the biological tissues. The in vitro and in vivo studies suggests that antioxidant and anti-inflammation properties of pomegranate constitute have major antimutagenic and antiproliferative activities for regulating gene expression, modulating cellular mechanisms, and limiting the ability of cancers to metastasize. A limited number of clinical studies have suggested that pomegranate ingredients have the potential for the prevention and treatment of cancer, especially colorectal and prostate cancer. In cancer therapy, it remains a clinical dilemma to hit the right target without inducing side effects. The costly anticancer chemotherapies are often associated with drug resistance and serious side effects in vital organs and noncancerous neighboring cells. It appears that the pomegranate-based phytotherapy would be affordable and cost-effective for next-generation non-pharmacologic anticancer remedies with lesser side effects. However, well-designed, randomized, double-blind, and multi-center studies are needed to establish the long-term safety, efficacy, and dose schedules for orally deliverable pomegranate formulations.
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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 | 0.001 | 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".