Stone Alkaline Water Induces Apoptosis of Prostate Cancer Cells and Inhibits Tumor Cell Induced Angiogenesis In Vitro
Bibliographic record
Abstract
Abstract Treatment options to improve overall survival rate of prostate cancer patients are limited since tumor cells acquire resistance to the chemotherapeutic drugs. We aimed to determine anticancer effects of stone alkaline water (SAW) on PC-3 and DU-145 prostate adenocarcinoma cell lines. SAW was obtained by triturating high stones under vacuum at 3000 °C. High mineral and trace element containing fraction of SAW was used for the experiments. Viability of the tumor cells was analyzed using tetrazolium based WST-1 cell proliferation assay, cell cycle analysis was carried out with Propidum Iodide staining (Muse™ Cell Cycle Kit). Acridine Orange and Annexin V stainings were done to analyze the cellular morphology and to determine apoptosis. Tumor cell derived angiogenesis was analyzed with migration and tube formation assays. SAW treatment resulted in accumulation of cells at G0/G1 phase and inhibited tumor cell induced HUVEC tube formation and migration. SAW treatment significantly decreased viability of PC-3 and DU-145 prostate adenocarcinoma cells and induced apoptotic cell death. Intriguingly, treatment of the prostate cancer cells with SAW inhibited tumor cell derived angiogenesis. SAW may aid in treating prostate cancer and molecules important for SAW’s apoptotic and anti-angiogenic effects need to be determined.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".