RRR-α-tocopherol as a Predominant Stereo-isomer in Chinese Breast Milk During Lactation Stages (P11-057-19)
Bibliographic record
Abstract
Human milk is the most important source for neonates to acquire adequate vitamin E for their immune system and brain growth. Among all tocopherols, infant brain discriminates in favor of the natural occurring RRR-α-tocopherol against the synthetic α-tocopherol stereoisomers and other tocopherols. However, the stereoisomer profiles of α-tocopherol in Chinese human milk have not been previously reported. This study aimed to analyze the stereoisomer profile of α-tocopherol in Chinese human milk over different lactation stages. Colostrum (day 0–7), transitional milk (day 8–15) and mature milk (day 40–45) were collected longitudinally from 89 healthy lactating mothers of full-term, singleton delivery. The levels of α-tocopherol stereoisomers (RRR, RSR, RSS, RRS, Σ2S) were determined by high performance liquid chromatography with fluorescence detection. In three lactation stages, RRR-α-tocopherol is the predominated stereoisomers in Chinese human milk (P < 0.0001), accounting for an average proportion of 85% of total α-tocopherol. In contrast, the ratios of the synthetic stereoisomers were RRS, 5.10-6.02%; RSR, 2.32-3.31%, RSS, 2.66-2.85%; and Σ2S, 2.89%-3.49%. The mean total α-tocopherol was 9.20mg/L in colostrum but sharply declined to 4.10 mg/L in mature milk. After taking the different breastfeeding volumes over lactation stages into consideration, Chinese human milk constantly provided about 3.5 mg/day α-tocopherol and 2.98 mg/day RRR-α-tocopherol to infants. RRR-α-tocopherol is the predominated stereoisomer in Chinese human milk over different lactation stages. Chinese breastmilk supply higher % of RRR- stereoisomer as 85% of total α-tocopherol than US report as 75%. There is, therefore, a demand to add matched level of RRR- α-tocopherol in Chinese formula. Abbott.
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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 | 0.001 | 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".