Breastfeeding and Neonatal Age Influence Neutrophil‐Driven Ontogeny of Blood Cell Populations in the First Week of Human Life
Bibliographic record
Abstract
The first few days of life are characterized by rapid external and internal changes that require substantial immune system adaptations. Despite growing evidence of the impact of this period on lifelong immune health, this period remains largely uncharted. To identify factors that may impact the trajectory of immune development, we conducted stringently standardized, high-throughput phenotyping of peripheral white blood cell (WBC) populations from 796 newborns across two distinct cohorts (The Gambia, West Africa; Papua New Guinea, Melanesia) in the framework of a Human Immunology Project Consortium (HIPC) study. Samples were collected twice from each newborn during the first week of life, first at Day of Life 0 (at birth) and then subsequently at Day of Life 1, 3, or 7 depending on the randomization group the newborn belongs to. The subsequent analysis was conducted at an unprecedented level of detail using flow cytometry and an unbiased automated gating algorithm. The results showed that WBC composition in peripheral blood changes along patterns highly conserved across populations and environments. Changes across days of life were most pronounced in the innate myeloid compartment. Breastfeeding, and at a smaller scale neonatal vaccination, were associated with changes in peripheral blood neutrophil and monocyte cell counts. Our results suggest a common trajectory of immune development in newborns and possible association with timing of breastfeeding initiation, which may contribute to immune-mediated protection from infection in early life. These data begin to outline a specific window of opportunity for interventions that could deliberately direct WBC composition, and with that, immune trajectory and thus ontogeny in early life. This trial is registered with NCT03246230.
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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 | 0.000 | 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".