Gestational stress and perinatal <scp>SSRIs</scp> differentially impact the maternal and neonatal microbiome‐gut‐brain axis
Bibliographic record
Abstract
Selective serotonin reuptake inhibitors (SSRIs) are the most popular antidepressant medications used to manage perinatal mood disturbances, yet our understanding of how they affect the microbiome-gut-brain axis of the mother and offspring is limited. The purpose of this study was to determine how peripartum SSRI treatment may prevent the effects of gestational stress on plasticity in the maternal hippocampus, plasticity in the neonatal brain and related changes in gut microbiota. To do this Sprague-Dawley female rats were left untreated or subjected to unpredictable stress during pregnancy. Half of the females were supplemented daily with fluoxetine. On postpartum day 2 brains were collected for measurement of plasticity (neurogenesis and microglia content) in the maternal hippocampus and in the neonatal brain. Glucocorticoid receptor density was also investigated in the maternal hippocampus. Microbiota composition was analyzed in fecal samples of dams during and after pregnancy, and colon tissue samples from offspring on postnatal day 2. Main findings show there are significant changes to the maternal microbiome-gut-brain axis that may be fundamental to mediating plasticity in the maternal hippocampus. In addition, there is significant impact of gestational stress on neonatal gut microbiota and brain microglia density, while the effects of SSRIs are limited. This is the first study to explore the impact of gestational stress and SSRIs on the microbiome-gut-brain axis in the mother and neonate. Findings from this study will help inform pathways to intervention strategies including stress reduction techniques and/or microbiota targeted nutritional approaches directed towards improving maternal gut health and outcomes for mother and neonate.
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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".