Prenatal Maternal Stress Causes Preterm Birth and Affects Neonatal Adaptive Immunity in Mice
Bibliographic record
Abstract
Maternal stress is a well-established risk factor for preterm birth and has been associated with adverse neonatal outcomes in the first and subsequent generations, including increased susceptibility to disease and lasting immunological changes. However, a causal link between prenatal maternal stress and preterm birth, as well as compromised neonatal immunity, has yet to be established. To fill this gap in knowledge, we used a murine model of prenatal maternal stress across three generations and high-dimensional flow cytometry to evaluate neonatal adaptive immunity. We report that recurrent prenatal maternal stress induced preterm birth in the first and second filial generations and negatively impacted early neonatal growth. Strikingly, prenatal maternal stress induced a systematic reduction in T cells and B cells, the former including regulatory CD4+ T cells as well as IL-4- and IL-17A-producing T cells, in the second generation. Yet, neonatal adaptive immunity gained resilience against prenatal maternal stress by the third generation. We also show that the rate of prenatal maternal stress-induced preterm birth can be reduced upon cessation of stress, though neonatal growth impairments persisted. These findings provide evidence that prenatal maternal stress causes preterm birth and affects neonatal immunity across generations, adverse effects that can be ameliorated upon cessation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".