Squalene protects mice bone marrow hematopoietic and mesenchymal stem cells against high-dose cisplatin in vivo by restoring antioxidant balance: implications in cancer chemotherapy
Bibliographic record
Abstract
1128 Background: There is an urgent need to develop selective cytoprotective agent to protect normal tissues without protecting malignant tumors from cytotoxic chemotherapy. We have previously shown that squalene, an isoprenoid antioxidant showed in vitro cytoprotective activity in a bone marrow versus neuroblastoma model of cisplatin-induced toxicity (Das B et al. Eur J Cancer, 2003; 39:2556-2565). Here we investigated the in vivo cytoprotection of squalene in a mouse model of cisplatin-toxicity against normal versus neuroblastoma xenograft. Methods: cisplatin (8-15mg/kg body weight) was injected i.p. to Balb/c mice with or without squalene (100mg/kg mixed with Intralipid) 3hrs before cisplatin injection. Bone marrow was collected five days after drug injection, and CFU assays were performed. Toxicity to mesenchymal stem cells was measured by culturing mesenchymal colonies in special mesencult media (Stem Cell Inc. Vancouver BC). Renal toxicity was measured by plasma BUN level. Oxidative stress parameter was measured by measuring GSH, GST, SOD, and GSpx activities by established biochemical methods (Chemicon International, USA). Results: cisplatin, 15mg/kg dose reduced BM colony formation by 51%, whereas the addition of squalene reduced BM colony formation by only 15%, suggesting 36% protection (p
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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 | 0.001 |
| 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".