Detailed fatty acid profile of serum lipid classes in lactating women and their relationship with milk fat
Bibliographic record
Abstract
Little is known about fatty acid (FA) distribution among triacylglyceride (TG), cholesterol ester (CE), and phospholipid (PL) plasma lipid fractions in lactating women, and the relationships between these fractions and the FA profile of the milk that mothers produce. Milk fat and serum lipid FA compositions in lactating women were compared to investigate which serum lipids are the source of FA in milk fat. A TLC methodology was used to fractionate serum lipids including PL subclasses, that is, phosphatidylethanolamine (PE), phosphatidylcholine (PC), and sphingomyelin (SM). Plasma lipids consisted of CE (36.4%), TG (19.1%), PE (1.40%), PC (39.80%), and SM (3.29%). With regard to PL fractions, PE was characterized by the highest levels of 18:0, 20:4 n‐6, 22:6 n‐3, and alkenyl ethers. PC revealed 16:0 as the predominant FA, followed by 18:2 n‐6, 18:0, cis‐9 18:1, and 20:4 n‐6, whereas saturated FA from 16:0 to 24:0 were characteristic of SM. Although n‐6 and n‐3 polyunsaturated FA were mostly found in PE and PC, strong correlations were found for 18:2 n‐6 and 18:3 n‐3 between plasma TG and milk lipids. This study also shows that trans 18:1 isomers may be selectively incorporated into milk fat from specific serum fractions. Practical applications: An easy‐to‐use and inexpensive two‐stage TLC methodology to fractionate plasma lipids requiring only 1 mL of blood serum has been developed. Moreover, this study provides valuable information on the composition of serum lipids in lactating women, particularly with regard to PL classes, and their influence on the milk fat FA profile. Fatty acid transfer from serum lipids to milk fat in lactating women.
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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".