Squalene Selectively Protects Mouse Bone Marrow Progenitors Against Cisplatin and Carboplatin-Induced Cytotoxicity In Vivo Without Protecting Tumor Growth
Bibliographic record
Abstract
Squalene, an isoprenoid antioxidant is a potential cytoprotective agent against chemotherapy-induced toxicity. We have previously published that squalene protects light-density bone marrow cells against cis-diamminedichloroplatinum( II) (cisplatin)-induced toxicity without protecting tumor cells in vitro. Here, we developed an in vivo mouse model of cisplatin and cis-diammine (cyclobutane-1,1-dicarboxylato) platinum(II) (carboplatin)-induced toxicity to further investigate squalene-mediated LD-BM cytoprotection including the molecular mechanism behind selective cytoprotection. We found that squalene significantly reduced the body weight loss of cisplatin and carboplatin-treated mice. Light-density bone marrow cells from squalene-treated mice exhibited improved formation of hematopoietic colonies (colony-forming unit-granulocyte macrophage). Furthermore, squalene also protected mesenchymal stem cell colonies (colony-forming unit-fibroblast) from cisplatin and carboplatin-induced toxicity. Squalene-induced protection was associated with decreased reactive oxygen species and increased levels of glutathione and glutathione peroxidase/glutathione-S-transferase. Importantly, squalene did not protect neuroblastoma, small cell carcinoma, or medulloblastoma xenografts against cisplatin-induced toxicity. These results suggest that squalene is a potential candidate for future development as a cytoprotective agent against chemotherapeutic toxicity.
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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) | 0.001 | 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".