Plasma Fatty Acid Composition in Patients with Pancreatic Cancer: Correlations to Clinical Parameters
Bibliographic record
Abstract
Pancreatic cancer (PC) ranks as the fourth cause of cancer-related deaths in the Czech Republic. Evidence exists that deregulation of fatty acid (FA) metabolism is connected with some malignancies; therefore, we decided to analyze FA profile in plasma lipid classes in patients with PC with relation to tumor staging, nutritional status, and survival. The study included 84 patients (47 males, 37 females) with PC and 68 controls (36 males, 32 females). FA patterns were analyzed in plasma lipid classes by gas-chromatography. We observed increased proportion of total monounsaturated FA (MUFA) in PC group in all plasma lipid classes. These changes were connected with increased Δ9-desaturase (SCD1) and Δ5-desaturase indices. Correlations of dihomo-γ-linolenic acid (DHGLA) with these variables were opposite. Longer survival of patients was connected with higher content of EPA, DHA, and with lower SCD1 index, respectively. Plasma phospholipid proportions of α-linolenic acid, DHGLA, EPA, and n-3 polyunsaturated fatty acids displayed negative trend with tumor staging. Plasma lipid FA pattern in PC patients resulted from decreased dietary fat intake and increased de novo synthesis of FA with transformation into MUFA. Changes in FA profile implicated some pathophysiological mechanisms responsible for disturbed FA metabolism in PC and importance of appropriate nutritional support.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".