High‐resolution lipidomics coupled with rapid fixation reveals novel ischemia‐induced signaling in the rat neurolipidome
Bibliographic record
Abstract
The field of lipidomics has evolved vastly since its creation 15 years ago. Advancements in mass spectrometry have allowed for the identification of hundreds of intact lipids and lipid mediators. However, because of the release of fatty acids from the phospholipid membrane in the brain caused by ischemia, identifying the neurolipidome has been challenging. Microwave fixation has been shown to reduce the ischemia-induced release of several lipid mediators. Therefore, this study aimed to develop a method combining high-resolution tandem mass spectrometry (MS/MS), high-energy head-focused microwave fixation and statistical modeling, allowing for the measurement of intact lipids and lipid mediators in order to eliminate the ischemia-induced release of fatty acids and identify the rat neurolipidome. In this study, we demonstrated the ischemia-induced production of bioactive lipid mediators, and the reduction in variability using microwave fixation in combination with liquid chromatography (LC)-MS/MS. We have also illustrated for the first time that microwave fixation eliminates the alterations in intact lipid species following ischemia. While many phospholipid species were unchanged by ischemia, other intact lipid classes, such as diacylglycerol, were lower in concentration following microwave fixation compared to ischemia.
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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 | 0.000 |
| 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".