Rh2 Synergistically Enhances Paclitaxel or Mitoxantrone in Prostate Cancer Models
Bibliographic record
Abstract
PURPOSE: We explored the efficacy of the ginsenoside Rh2 and examined its impact on the effective dose of paclitaxel and mitoxantrone in the LNCaP prostate tumor model. MATERIALS AND METHODS: Cultured LNCaP cell viability was assessed following treatment (48 hours) with Rh2 (0 to 40 microM) alone or in combination with paclitaxel and mitoxantrone. Synergism or antagonism observed when combining treatment was calculated using CalcuSyn software (Biosoft). In addition, the inhibition of LNCaP human xenograft tumor growth was examined in vivo when Rh2 treatment was combined with chemotherapy. Harvested tumors were immunohistochemical stained with p27kip and Ki67. RESULTS: Rh2 and paclitaxel act synergistically in cultured LNCaP cells to lower ED50 and ED75 values. Rh2 and mitoxantrone are also synergistic. However, when combined as ED95, an antagonistic effect was observed in this cell line. Treatment of LNCaP tumors by Rh2 plus paclitaxel produced a significant decrease in tumor growth and serum prostate specific antigen. Immunohistochemical analysis revealed an apparent but nonsignificant effect on proliferation markers in LNCaP tumors. When Rh2 and mitoxantrone were combined in vivo, there was no significant benefit observed. CONCLUSIONS: These results indicate that the combination of Rh2 and paclitaxel has an effect on growth inhibition that is greater and synergistic, as demonstrated in a cultured LNCaP cell line. Conversely combining Rh2 with mitoxantrone appears to elicit no benefit. Therefore, combination therapy using chemotherapy and Rh2 requires further investigation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".