The role of antibiotic exposure and the effects of breastmilk and human milk feeding on the developing infant gut microbiome
Bibliographic record
Abstract
The World Health Organization (WHO) recommends exclusive breastfeeding for the first 6 months of life followed by complementary foods and sustained breastfeeding for at least 2 years, underscoring its pivotal role in reducing infant mortality and preventing various illnesses. This perspective delves into the intricate relationship between breastfeeding practices, early life antibiotic exposure, and infant gut microbiome development, highlighting their profound influence on child health outcomes. Antibiotics are extensively prescribed during pregnancy and childhood, disrupting the microbiome, and are related to increased risks of allergies, obesity, and neurodevelopmental disorders. Breastfeeding is a significant determinant of a healthier gut microbiome, characterized by higher levels of beneficial bacteria such as Bifidobacterium and lower levels of potential pathogens. Despite widespread recognition of the benefits of breastfeeding, gaps persist in healthcare practices and support mechanisms, exacerbating challenges faced by breastfeeding families. This highlights the pressing need for comprehensive research encompassing breastfeeding behaviors, human milk intake, and their impact on infant health outcomes. Additionally, promoting awareness among healthcare providers and families regarding the detrimental effects of unnecessary formula supplementation could facilitate informed decision-making and bolster exclusive breastfeeding rates. Moreover, donor human milk (DHM) is a promising alternative to formula, potentially mitigating disruptions to the infant gut microbiome after antibiotic exposure. Overall, prioritizing breastfeeding support interventions and bridging research gaps are essential steps towards improving child health outcomes on a global scale.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".