Antibiotic use during pregnancy is linked to offspring gut microbial dysbiosis, barrier disruption, and altered immunity along the gut–lung axis
Bibliographic record
Abstract
Abstract Antibiotic use during pregnancy is associated with increased asthma risk in children. Since approximately 25% of women use antibiotics during pregnancy, it is important to identify the pathways involved in this phenomenon. We investigate how mother‐to‐offspring transfer of antibiotic‐induced gut microbial dysbiosis influences immune system development along the gut–lung axis. Using a mouse model of maternal antibiotic exposure during pregnancy, we immunophenotyped offspring in early life and after asthma induction. In early life, prenatal‐antibiotic exposed offspring exhibited gut microbial dysbiosis, intestinal inflammation (increased fecal lipocalin‐2 and IgA), and dysregulated intestinal ILC3 subtypes. Intestinal barrier dysfunction in the offspring was indicated by a FITC‐dextran intestinal permeability assay and circulating lipopolysaccharide. This was accompanied by increased T‐helper (Th)17 cell percentages in the offspring's blood and lungs in both early life and after allergy induction. Lung tissue additionally showed increased percentages of RORγt T‐regulatory (Treg) cells at both time points. Our investigation of the gut–lung axis identifies early‐life gut dysbiosis, intestinal inflammation, and barrier dysfunction as a possible developmental programming event promoting increased expression of RORγt in blood and lung CD4+ T cells that may contribute to increased asthma risk.
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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) | 0.001 | 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".