Mass Spectrometry-Based Lipidomics in the Characterization of Individual Triacylglycerol (TAG) and Phospholipid (PL) Species from Marine Sources and Their Beneficial Health Effects
Bibliographic record
Abstract
Lipidomics is a research area that investigates lipidomes that refer to the complete lipid profile within a cell, tissue, organism, or ecosystem by focusing on the interactions with neighboring molecules at the inter- or intracellular spaces of organisms. In recent decades, lipidomics has greatly evolved along with developments in mass spectrometric analysis (i.e., ionization approaches, the types of analyzers, and the data processing tools), leading to a rapid and accurate performance in the identification and quantification of individual lipid species. In this contribution, the identification and quantification of triacylglycerol (TAG) and phospholipid (PL) species in marine sources using mass spectrometry analysis are comprehensively discussed along with their physiological roles and health-beneficial effects on the human body. Moreover, this review emphasizes the importance of the isotopic deconvolution and instrument responses caused by the difference in the carbon number and the unsaturation of TAG and PL species in quantitative analysis. Most TAG and PL detected in marine taxa possess a high proportion of polyunsaturated ω3 long-chain polyunsaturated fatty acids (LCPUFA, ≥C20) in particular eicosapentaenoic acid (EPA, 20:5ω3) and docosahexaenoic acid (DHA, 22:6ω3), indicating the large number and structural diversity of glycerolipid species containing EPA and DHA. This review also discusses the health effects of TAG and PL species esterified with ω3 LCPUFA. Given that most fisheries science and aquaculture research during recent decades has mainly focused on ω3 LCPUFA after release from parent molecules, discussion of the health effects of ω3 LCPUFA in the form of TAG and PL extends understanding of the bioactivities of ω3 fatty acids as they occur naturally.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".