Prevention of Prostate Tumor Development by Stimulation of Antitumor Immunity Using a Standardized Herbal Extract (Deep Immune®) in TRAMP Mice
Bibliographic record
Abstract
Low‐risk prostate cancer (PCa) does not require immediate treatment, but PCa progression after years of active surveillance will need the treatment. This study was to test the efficacy of immunostimulant Deep Immune (DI) in controlling PCa progression. DI is an extract of eight different medicinal herbs. In vitro activity of DI was determined by phagocytosis activation using flow cytometric analysis of fluorescence‐labeled latex bead uptake, expression of immune‐modulating 84 genes using PCRarray, and tumor killing using coculturing with immune cells. Anti‐PCa activity of DI in vivo was examined in male TRAMP mice. In vitro DI stimulated phagocytosis and expression of a panel of inflammatory mediators (C4b, CXCL3, lymphotoxin, NOS2, TLR1, TNF, and TNFSF14) in cultured macrophages and increased tumor killing of both macrophages and TRAMP mouse splenocytes. Daily intake of this herbal product significantly suppressed the tumor size ( P = 0.0368) with lower histopathologic scores ( P = 0.0364) in TRAMP mice, which were associated with an increase in both splenocyte cytotoxicity against tumor cells and numbers of CD8 T cells, macrophages, and dendritic cells in the spleens in vivo . In conclusion, daily intake of DI prevents PCa progression in TRAMP mice, suggesting the possible effectiveness of the immunostimulant herbal products on prevention of PCa progression after diagnosis of low‐risk PCa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".