Can Behenic Acid (C22:0) Levels be a Prognostic Factor in Glial Tumors?
Bibliographic record
Abstract
BACKGROUND: Inhibition of fatty acid synthase leads to apoptosis in cancers, which leads to high levels of fatty acid synthesis. This indicates that cancer cells depend on fatty acid in order to survive. In this study, we investigated whether or not there was a relationship between the glial tumor grade and free fatty acid level of tumor tissue. METHODS: Twenty patients who had high grade glial tumors and 20 patients who had low grade glial tumors, were included in the study. Tumors samples were obtained intraoperatively in order to measure the fatty acid levels. The fatty acids were studied in three groups: saturated fatty acids, monounsaturated fatty acids and polyunsaturated fatty acids. They were analyzed with gas chromatography. RESULTS: The oleic acid, linoleic acid, eicosadienoic acid, arachidonic acid, and docosadienoic acid levels were high in the tumor tissue of low grade tumors. The myristic acid, palmitic acid, stearic acid, alpha linoleic acid, eicosenoic acid, dihomo-gamma-linolenic acid, docosahexaenoic acid, and ceramide levels were high in the tumor tissue of high grade glial tumors. However, none of these high values were statistically significant. The high values of behenic acid, a saturated fatty acid, in low grade glial tumors were statistically significant. CONCLUSION: High levels of behenic acid in patients with low grade glial tumor is important as it indicates persistence of the tissue integrity and tissue resistance. behenic acid levels can be a prognostic factor in glial tumors. Le taux d'acide béhénique (C22:0) peut-il constituer un facteur de pronostic dans les tumeurs gliales?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".