Chemically Induced Brain Cancer in Sprague-Dawley Rats: Changed Lipidomics Mimics the Human Conditions
Bibliographic record
Abstract
Malignant gliomas are one of the most treatment-refractory cancers. Development of resistance to chemo- and radiotherapies contributes to these tumors’ aggressive phenotypes. Elevated lipid levels in gliomas have been reported for the last 50 years. However, the molecular mechanisms of how tumor tissues obtain lipids and utilize them are not well understood.In our study, 48.6% of phosphatidylcholines were significantly changed during an early stage of brain cancer in females, and 66.2% in males. As for lysophosphatidylcholines 57.1% metabolites were significantly changed in female, and 64.3% in male rats. We observed the most interesting results in the group of sphingomyelins, where 85.8% metabolites were significantly elevated during brain cancer. According to VIP projection, the most important metabolites were: PC ae C40:3, PC ae C38:1, PC ae C30:1, PC ae C38:3, PC ae C44:3, PC aa C40:2, PC aa C42:0, PC ae C30:2, SM C20:2, PC aa C42:1 in females, and PC ae C38:1, PC ae C40:3, PC ae C30:1, PC ae C42:1, SM C20:2, PC aa C34:4, PC ae C38:4, PC aa C32:2, PC aa C38:5, lysoPC a C14:0. The identification of lipid biomarkers during the early stage of cancer could improve patient prognosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".