Antibiotic use during pregnancy increases offspring asthma severity in a dose‐dependent manner
Bibliographic record
Abstract
BACKGROUND: The use of antibiotics during pregnancy is associated with increased allergic asthma risk in the offspring, and given that approximately 25% of pregnant women are prescribed antibiotics, it is important to understand the mechanisms contributing to this phenomenon. Currently, there are no studies that directly test this association experimentally. Our objective was to develop a mouse model in which antibiotic treatment during pregnancy results in increased offspring asthma susceptibility. METHODS: Pregnant mice were treated daily from gestation day 8-17 with an oral solution of the antibiotic vancomycin, and three concentrations were tested. At weaning, offspring were subjected to an adjuvant-free experimental asthma protocol using ovalbumin as an allergen. The composition of the gut microbiome was determined in mothers and offspring with samples collected from five different time points; short-chain fatty acids were also analyzed in allergic offspring. RESULTS: We found that maternal antibiotic treatment during pregnancy was associated with increased offspring asthma severity in a dose-dependent manner. Furthermore, maternal vancomycin treatment during pregnancy caused marked changes in the gut microbiome composition in both mothers and pups at several different time points. The increased asthma severity and intestinal microbiome changes in pups were also associated with significantly decreased cecal short-chain fatty acid concentrations. CONCLUSION: Consistent with the "Developmental Origins Hypothesis," our results confirm that exposure to antibiotics during pregnancy shapes the neonatal intestinal environment and increases offspring allergic lung inflammation.
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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 | 0.000 |
| 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 | 0.001 |
| 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".