The effect of non-genetic determinants of human milk oligosaccharide profiles in milk of Ugandan mothers
Bibliographic record
Abstract
Abstract Background Human milk oligosaccharides (HMOs) protect against infection and promote growth and cognitive development in breastfeeding children. Non-genetic factors which influence HMO composition in breastfeeding mothers in rural Africa have not been investigated. Objective We undertook a cross-sectional study to determine the association between HMO profiles and non-genetic maternal factors and children’s sex in Ugandan mother-children pairs. Method Human milk was collected from 127 breastfeeding mothers by manual expression. HMO analysis was by high performance liquid chromatography. The proportion of each HMO per total HMO concentration was calculated. Spearman’s correlation and Mann-Whitney U test were used to assess the relationship between individual HMOs and maternal factors and infant sex. Result Nineteen HMOs were assayed. The prevalence of secretor and non-secretor status, based on the proportion of mothers with high milk concentrations of 2’FL and LNFP 1, was 80.3 % and 19.7 %, respectively. In secretor mothers, 2’FL, DFLac and LNFP I constituted > 57 % while in non-secretor mothers LNT and LNFPII constituted 46.9 % of the measured total HMOs. The median 3’SL concentration in milk of all mothers of male children was significantly higher than that in all mothers of female children. The median DFLac concentration in all mothers was significantly higher in multiparous mothers compared to primiparous mothers. Higher FDSLNH and lower LNH concentrations were observed in overweight secretor and non-secretor mothers, respectively. Median concentrations of LNFP I and DSLNT were significantly higher in all mothers < 18 years old compared all mothers > 18 years old. Concentrations of specific HMOs increased, decreased, or remained unchanged with increasing lactation duration in secretor and non-secretor mothers. Conclusions Specific HMOs were associated with infant sex and maternal age, parity and post-partum BMI in Ugandan mothers but were different from those reported in other populations.
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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 | 0.003 |
| 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".