Gestational age at birth influences protein and RNA content in human milk extracellular vesicles
Bibliographic record
Abstract
Human milk extracellular vesicles (HM EVs) are proposed to protect against disease development in infants. This protection could in part be facilitated by the bioactive EV cargo of proteins and RNA. Notably, mothers birth infants of different gestational ages with unique needs, wherein the EV cargo of HM may diverge. We collected HM from lactating mothers within two weeks of a term or preterm birth. Following purification of EVs, proteins and mRNA were extracted for proteomics and sequencing analyses, respectively. Over 2000 protein groups were identified, and over 8000 genes were quantified. The total number of proteins and mRNA did not differ significantly between the two conditions, while functional bioinformatics of differentially expressed cargo indicated enrichment in immunoregulatory cargo for preterm HM EVs. In term HM EVs, significantly upregulated cargo was enriched in metabolism-related functions. Based on gene expression signatures from HM-contained single cell sequencing data, we proposed that a larger portion of preterm HM EVs are secreted by immune cells, whereas term HM EVs contain more signatures of lactocyte epithelial cells. Proposed differences in EV cargo could indicate variation in mother's milk based on infants' gestational age and provide basis for further functional characterisation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".