Prebiotic, Probiotic, and Synbiotic Consumption Alter Behavioral Variables and Intestinal Permeability and Microbiota in BTBR Mice
Bibliographic record
Abstract
Given that prebiotics have been shown to improve gut microbiota composition, gastrointestinal symptoms and select behaviors in autism spectrum disorder (ASD), we hypothesized that prebiotic supplementation would improve sociability, communication, and repetitive behaviors in a murine model of ASD. We also examined the effect of a synbiotic (probiotic + prebiotic). Juvenile male BTBR mice were randomized to: (1) control; (2) probiotic (1 × 1010 CFU/d Lactobacillus reuteri RC-14®; now known as Limosilactobacillus reuteri); (3) prebiotic (10% oligofructose-enriched inulin); (4) prebiotic + probiotic (n = 12/group) administered through food for 3 weeks. Sociability, communication, repetitive behavior, intestinal permeability and gut microbiota were assessed. Probiotic and symbiotic treatments improved sociability (92 s and 70 s longer in stranger than empty chamber) and repetitive behaviors (50% lower frequency), whereas prebiotic intake worsened sociability (82 s less in stranger chamber) and increased the total time spent self-grooming (96 s vs. 80 s CTR), but improved communication variables (4.6 ms longer call duration and 4 s higher total syllable activity). Mice consuming probiotics or synbiotics had lower intestinal permeability (30% and 15% lower than CTR). Prebiotic, probiotic, and symbiotic treatments shifted gut microbiota to taxa associated with improved gut health. L.reuteri may help alleviate ASD behavioral symptom severity and improve gut health. The potential use of prebiotics in an ASD population warrants further research.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".