Can breastfeeding protect against antimicrobial resistance?
Bibliographic record
Abstract
BACKGROUND: The proportion of infections among young children that are antimicrobial-resistant is increasing across the globe. Newborns may be colonized with enteric antimicrobial-resistant pathogens early in life, which is a risk factor for infection-related morbidity and mortality. Breastfeeding is actively promoted worldwide for its beneficial impacts on newborn health and gut health. However, the role of breastfeeding and human milk components in mitigating young children's carriage of antimicrobial-resistant pathogens and antibiotic resistance genes has not been comprehensively explored. MAIN BODY: Here, we review how the act of breastfeeding, early breastfeeding, and/or human milk components, such as the milk microbiota, secretory IgA, human milk oligosaccharides, antimicrobial peptides, and microRNA -bearing extracellular vesicles, could play a role in preventing the establishment of antimicrobial-resistant pathogens in young children's developing gut microbiomes. We describe findings from recent human studies that support this concept. CONCLUSION: Given the projected rise in global morbidity and mortality that will stem from antimicrobial-resistant infections, identifying behavioral or nutritional interventions that could decrease children's susceptibility to colonization with antimicrobial-resistant pathogens may be one strategy for protecting their health. We suggest that breastfeeding and human milk supplements deserve greater attention as potential preventive measures in the global effort to combat antimicrobial resistance, particularly in low- and middle-income settings.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".