Microbe–Immune–Stress Interactions Impact Behaviour during Postnatal Development
Bibliographic record
Abstract
Decades of research have established the role of microbiota-brain communication in behaviour and brain function. Studies have shown that microbiota composition and diversity are influenced by a variety of factors including host genetics, diet, and other environmental exposures, with implications for the immunological and neurobiological development of the host organism. To further understand early-life interactions between environment, genetic factors, the microbiome and the central nervous system, we investigated the impact of postnatal stress in C57Bl/6 wild type and T-cell deficient mice on microbe-brain interactions and behaviour. Mice were exposed to immune challenge with lipopolysaccharide (LPS) at postnatal day (P) 3 and maternal separation at P9 (16 h overnight). Behavioural assessment of growth and development as well as behaviour (righting reflex, ultrasonic vocalizations in response to brief maternal separation, open field, sociability, and grooming) was conducted. Microbiota diversity and composition of fecal samples collected at P24 revealed reduced alpha diversity in T-cell-deficient mice as well as genotype- and stress-related taxa. Notably, integrated analyses of microbiota and behaviour in the context of immunocompromise revealed key behavioural related taxa that may be important to brain development. These findings are important to determining the influence of genetic and environmental factors on gut microbiota and advances our understanding microbiome-brain signaling pathways on neurodevelopment and behaviour.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".