A multimodal analytical method to simultaneously determine monoacetyldiacylglycerols, medium and long chain triglycerides in biological samples during routine lipidomics
Bibliographic record
Abstract
Abstract Background Monoacetyldiglycerides (MAcDG), are acetylated triglycerides (TG) and an emerging class of bioactive or functional lipid with promising nutritional, medical, and industrial applications. A major challenge exists when analyzing MAcDG from other subclasses of TG in biological matrices, limiting knowledge on their applications and metabolism. Methods Herein a multimodal analytical method for resolution, identification, and quantitation of MAcDG in biological samples was demonstrated based on thin layer chromatography-flame ionization detection complimentary with C30-reversed phase liquid chromatography-high resolution accurate mass tandem mass spectrometry. This method was then applied to determine the MAcDG molecular species composition and quantity in E. solidaginis larvae. The statistical method for analysis of TG subclass composition and molecular species composition of E. solidaginis larvae was one-way analysis of variance (ANOVA). Results The findings suggest that the proposed analytical method could simultaneously provide a fast, accurate, sensitive, high throughput analysis of MAcDG from other TG subclasses, including the fatty acids, isomers, and molecular species composition. Conclusion This method would allow for MAcDG to be included during routine lipidomics analysis of biological samples and will have broad interests and applications in the scientific community in areas such as nutrition, climate change, medicine and biofuel innovations. Graphical abstract
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".