Evidence for long‐lasting alterations in the fecal microbiota following prenatal alcohol exposure
Bibliographic record
Abstract
BACKGROUND: There is growing evidence that the gut microbiota can be shaped by early-life experiences/exposures, with long-term consequences for brain, behavior, and health. Changes in the gut microbiota have also been identified in neurodevelopmental disorders including Autism Spectrum Disorder and schizophrenia. In contrast, no studies to date have investigated whether the gut microbiota is altered in individuals with Fetal Alcohol Spectrum Disorder (FASD), the neurodevelopmental disorder that results from prenatal alcohol exposure (PAE). The current study was designed to assess the impact of PAE on the fecal microbiota. METHODS: We used a rodent model in which pregnant Sprague-Dawley rats were provided with an EtOH-containing diet or a control diet throughout gestation. Fecal samples were collected from adult male and female animals and 16s rRNA sequencing was performed. RESULTS: Overall, PAE rats showed greater richness of bacterial species, with community structure investigations demonstrating distinct clustering by prenatal treatment. In addition, prenatal treatment and sex-specific alterations were observed for many specific microbes. For example, in males, Bacteroides and Bifidobacterium, and in females, Faecalitalea and Proteus, differed in abundance between PAE and control rats. CONCLUSIONS: Taken together, these results show for the first time that PAE has a long-lasting and sex-specific impact on the fecal microbiota. Further research is needed that considers fetal microbiota in the development of new interventions in FASD.
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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 | 0.001 |
| 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".