Network-Based Bioinformatics Highlights Broad Importance of Human Milk Hyaluronan
Bibliographic record
Abstract
Human milk (HM) is rich in bioactive factors promoting postnatal small intestinal development and maturation of the microbiome. HM is also protective against necrotizing enterocolitis (NEC), a devastating inflammatory condition predominantly affecting preterm infants. The HM glycosaminoglycan, hyaluronan (HA), is present at high levels in colostrum and early milk. Our group has demonstrated that HA with a molecular weight of 35 kDa (HA35) promotes maturation of the murine neonatal intestine and protects against two distinct models of NEC. However, the molecular mechanisms underpinning HA35-induced changes in the developing ileum are unclear. CD-1 mouse pups were treated with HA35 or vehicle control daily, from P7 to P14, and we used network and functional analyses of bulk RNA-seq ileal transcriptomes to further characterize molecular mechanisms through which HA35 likely influences intestinal maturation. HA35-treated pups separated well by principal component analysis, and cell deconvolution revealed increases in stromal, Paneth, and mature enterocyte and progenitor cells in HA35-treated pups. Gene set enrichment and pathway analyses demonstrated upregulation in key processes related to antioxidant and growth pathways, such as nuclear factor erythroid 2-related factor-mediated oxidative stress response, hypoxia inducible factor-1 alpha, mechanistic target of rapamycin, and downregulation of apoptotic signaling. Collectively, pro-growth and differentiation signals induced by HA35 may present novel mechanisms by which this HM bioactive factor may protect against NEC.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".