Acute Hypercapnia/Ischemia Alters the Esterification of Arachidonic Acid and Docosahexaenoic Acid Epoxide Metabolites in Rat Brain Neutral Lipids
Bibliographic record
Abstract
Abstract In the brain, approximately 90% of oxylipins are esterified to lipids. However, the significance of this esterification process is not known. In the present study, we (1) validated an aminopropyl solid phase extraction (SPE) method for separating esterified lipids using 100 and 500 mg columns and (2) applied the method to quantify the distribution of esterified oxylipins within phospholipids (PL) and neutral lipids (NL) (i.e. triacylglycerol and cholesteryl ester) in rats subjected to head‐focused microwave fixation (controls) or CO2‐induced hypercapnia/ischemia. We hypothesized that oxylipin esterification into these lipid pools will be altered following CO2‐induced hypercapnia/ischemia. Lipids were extracted from control (n = 8) and CO2‐asphyxiated (n = 8) rat brains and separated on aminopropyl cartridges to yield PL and NL. The separated lipid fractions were hydrolyzed, purified with hydrophobic–lipophilic–balanced SPE columns, and analyzed with ultra‐high‐pressure liquid chromatography coupled to tandem mass spectrometry. Method validation showed that the 500 mg (vs 100 mg) aminopropyl columns yielded acceptable separation and recovery of esterified fatty acid epoxides but not other oxylipins. Two epoxides of arachidonic acid (ARA) were significantly increased, and three epoxides of docosahexaenoic acid (DHA) were significantly decreased in brain NL of CO2‐asphyxiated rats compared to controls subjected to head‐focused microwave fixation. PL‐bound fatty acid epoxides were highly variable and did not differ significantly between the groups. This study demonstrates that hypercapnia/ischemia alters the concentration of ARA and DHA epoxides within NL, reflecting an active turnover process regulating brain fatty acid epoxide concentrations.
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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) | 0.001 | 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) | 0.001 | 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".