Liposome encapsulation of curcumin and resveratrol in combination reduces prostate cancer incidence in PTEN knockout mice
Bibliographic record
Abstract
Increasing interest in the use of phytochemicals to reduce prostate cancer led us to investigate 2 potential agents, curcumin and resveratrol as preventive agents. However, there is concern about the bioavailability of these agents pertinent to the poor absorption and thereby limiting its clinical use. With the view to improve their bioavailability, we used the liposome encapsulated curcumin, and resveratrol individually and in combination in male B6C3F1/J mice. Further, we examined the chemopreventive effect of liposome encapsulated curcumin and resveratrol in combination in prostate-specific PTEN knockout mice. In vitro assays using PTEN-CaP8 cancer cells were performed to investigate the combined effects curcumin with resveratrol on (i) cell growth, apoptosis and cell cycle (ii) impact on activated p-Akt, cyclin D1, m-TOR and androgen receptor (AR) proteins involved in tumor progression. HPLC analysis of serum and prostate tissues showed a significant increase in curcumin level when liposome encapsulated curcumin coadministered with liposomal resveratrol (p < 0.001). Combination of liposomal forms of curcumin and resveratrol significantly decreased prostatic adenocarcinoma in vivo (p < 0.001). In vitro studies revealed that curcumin plus resveratrol effectively inhibit cell growth and induced apoptosis. Molecular targets activated due to the loss of phosphatase and tensin homolog (PTEN) including p-Akt, cyclin D1, mammalian target of rapamycin and AR were downregulated by these agents in combination. Findings from this study for the first time provide evidence on phytochemicals in combination to enhance chemopreventive efficacy in prostate cancer. These findings clearly suggest that phytochemicals in combination may reduce prostate cancer incidence due to the loss of the tumor suppressor gene PTEN.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".