Classification of Human Milks Based on Their <i>Trans</i> 18:1 Fatty Acid Profile and Effect of Maternal Diet
Bibliographic record
Abstract
BACKGROUND: The diet of breastfeeding women influences the trans fatty acid (TFA) composition of the milk excreted. However, the effects associated to TFA are isomer-dependent and diverse TFA profiles may have different nutritional implications. OBJECTIVE: The aim of this research was to evaluate whether certain TFA patterns in human milk fat can be used as indicators of TFA intake from different sources. METHODS: Milk fat from 60 women were examined and classified based on their TFA profile and conjugated linoleic acid (CLA) contents by principal component analysis (PCA). RESULTS: The PCA of the data allowed the classification of the women into 3 groups depending on milk TFA content and profile. From the 60 subjects, 19 presented a TFA profile characteristic of ruminant products intake, 10 a typical TFA profile of industrial trans fats consumption and 31 a negligible trans content. Superimposed on this, 21 women presented high amounts of docosahexaenoic acid (22:6 n-3) which was related to fish intake. CONCLUSIONS: The present research overcome problems associated to heterogeneous groups in nutritional experiments by a statistical classification of lactating subjects based on the TFA composition of their milk. This classification could be extrapolated to other nutritional studies dealing with TFA analysis and samples of different nature as biological samples or foodstuffs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".